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Author SHA1 Message Date
agent-company 5d11f514c0 Add model allow-list validation to analysis endpoints
Reject unsupported LLM model identifiers with HTTP 400 on all analysis
endpoints (single, batch, async batch). The SUPPORTED_MODELS list was
already defined for the /models endpoint but not enforced on incoming
requests. This completes the multi-model support feature by adding the
missing server-side validation.

Closes leeworks-agents/SPARC#1013

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-29 16:13:29 +00:00
AI-Manager cbc8f449a1 Merge pull request 'Generate TypeScript API client from OpenAPI spec' (#443) from feature/426-generate-ts-api-client into main
Merge pull request #443: Generate TypeScript API client from OpenAPI spec

Closes leeworks-agents/SPARC#426
2026-03-27 20:42:17 +00:00
agent-company 44620614b6 feat: generate TypeScript API client from OpenAPI spec and add CI freshness check
Closes leeworks-agents/SPARC#426

- Generate schema.d.ts from committed openapi.json using openapi-typescript
- Rewrite types/index.ts to derive all application types from the generated schema
- Add CI step in both build.yaml and test.yaml to verify schema.d.ts stays in sync
- TypeScript compilation passes with zero errors

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 20:09:11 +00:00
AI-Manager c72a44aa56 Merge pull request 'feat: add model picker UI and wire model param through backend' (#353) from feature/351-frontend-model-picker into main 2026-03-27 16:45:05 +00:00
agent-company 6aa71eb17e merge: resolve Batch.tsx conflict between model picker and job history
Combine both useQuery hooks (modelsQuery for model selector, jobsQuery for
job history) and pass selectedModel to analyzeBatch while also triggering
jobsQuery.refetch() on successful submission.
2026-03-27 16:44:47 +00:00
AI-Manager fb52d08387 Merge pull request 'feat: add loading skeletons and error states to Batch page' (#352) from feature/343-batch-loading-states into main 2026-03-27 16:43:40 +00:00
agent-company 223d5f7e5d feat: add model picker to Analysis and Batch pages with full backend wiring
Thread the optional model parameter through the entire analysis pipeline:
- analyzer.py: analyze_company, _analyze_company_safe, analyze_companies,
  and analyze_single_patent now accept and forward model override
- api.py: single company endpoint accepts model query param; batch and
  async batch endpoints pass request.model through to the analyzer
- client.ts: analyzeCompany, analyzeBatch, analyzeBatchAsync accept model;
  add listModels() to fetch available models from GET /models
- Analysis.tsx: add model selector dropdown that loads from /models API
- Batch.tsx: add model selector alongside the workers slider

Users can now pick a specific LLM (GPT-4o, Claude 3.5, Gemini, etc.)
per analysis request, or leave it on the server default.

Closes leeworks-agents/SPARC#351

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 16:13:00 +00:00
agent-company 595516e330 feat: add loading skeletons, error states, and empty state to Batch page
Add a Job History section that loads past jobs via useQuery with:
- Animated skeleton placeholders while the job list is loading
- Error banner with retry button when the API call fails
- Empty state with helpful message when no jobs exist
- Job list cards with status badges and progress bars

Also improve the batch submission error state with a retry button
alongside the existing dismiss button.

Closes leeworks-agents/SPARC#343

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 16:08:49 +00:00
AI-Manager 514e274fdb Merge pull request 'CI: add tsc --noEmit TypeScript type checking to test job' (#269) from feature/260-tsc-ci into main 2026-03-27 11:07:02 +00:00
AI-Manager 3d2c0ea27d Merge pull request 'Docs: document MODEL, SERP_CACHE_TTL_HOURS, LOG_LEVEL in .env.example' (#270) from feature/env-example-updates into main 2026-03-27 11:06:57 +00:00
agent-company f611e3a30c Docs: add MODEL, SERP_CACHE_TTL_HOURS, and LOG_LEVEL to .env.example
These environment variables were already supported in config.py but
were not documented in .env.example, making them hard to discover.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 10:08:52 +00:00
agent-company 2bbf2d70bb CI: add tsc --noEmit TypeScript type checking to test job
Adds a step to install Node.js and run tsc --noEmit in the frontend
directory, catching TypeScript type errors before images are built.
Ruff was already present; this completes issue #260.

Closes leeworks-agents/SPARC#260

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 10:08:06 +00:00
AI-Manager f8ca1b80b1 Merge pull request 'feat: add PDF export for analysis reports' (#171) from feature/export-pdf into main 2026-03-27 05:04:55 +00:00
agent-company 338ac86086 feat: add PDF export for analysis reports
Add a new /export/{company_name}/pdf endpoint that generates a formatted
PDF report using reportlab, including a summary table and all analysis
results. Add the corresponding frontend Export PDF button alongside the
existing Export CSV button on the Analysis page.

Closes leeworks-agents/SPARC#85

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-27 02:03:53 +00:00
AI-Manager ce31a32322 Merge pull request 'feat: add multi-model support for per-analysis LLM selection' (#64) from feature/multi-model into main 2026-03-26 12:14:25 +00:00
agent-company 449055b026 merge: resolve multi-model conflicts with trends and export endpoints
Keeps model selection, analytics trends, and CSV export endpoints.
2026-03-26 12:14:15 +00:00
AI-Manager 70925fbf04 Merge pull request 'feat: add OpenAPI TypeScript client generation setup' (#63) from feature/openapi-client-gen into main 2026-03-26 12:13:19 +00:00
agent-company 9b2b2c75db merge: resolve openapi-client-gen conflicts with CI typecheck script
Keeps both generate scripts and typecheck script in package.json.
2026-03-26 12:13:08 +00:00
AI-Manager 730f455e2b Merge pull request 'feat: add patent trend charts to the Analytics page' (#62) from feature/trend-charts into main 2026-03-26 12:12:24 +00:00
agent-company 03f8f7fa79 merge: resolve trend-charts conflicts with export and tracked endpoints
Keeps both analytics/trends endpoint and export endpoint from main.
2026-03-26 12:12:09 +00:00
AI-Manager f0edc5a3ae Merge pull request 'feat: add side-by-side patent portfolio comparison view' (#61) from feature/compare-view into main 2026-03-26 12:11:01 +00:00
agent-company f64d1b745f merge: resolve compare-view conflicts with dark mode changes
Combines GitCompareArrows icon import with Sun/Moon and ThemeContext imports.
2026-03-26 12:10:37 +00:00
AI-Manager 513b682dad Merge pull request 'feat: add S3/MinIO object storage support for patent PDFs' (#58) from feature/s3-storage into main 2026-03-26 12:09:49 +00:00
agent-company 04f4d36307 feat: add multi-model support for per-analysis LLM selection
Allow users to choose the LLM model on a per-analysis basis. The
model field is optional in both single and batch analysis requests,
defaulting to the server-configured MODEL env var. The model used
is recorded in the analysis result and database.

- Add model parameter to LLMAnalyzer.analyze_patent_content and
  analyze_patent_portfolio
- Add model field to CompanyAnalysisResult and API response
- Add model field to BatchAnalysisRequest
- Add GET /models endpoint listing supported models and the default
- Store model in llm_messages metadata for attribution

Closes leeworks-agents/SPARC#37

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 10:28:25 +00:00
agent-company 7a364e6736 feat: add OpenAPI TypeScript client generation setup
Add openapi-typescript devDependency and npm scripts for generating
typed TypeScript schema from the FastAPI OpenAPI spec. Include a
static openapi.json snapshot for offline generation.

- npm run generate: fetch schema from running backend and generate types
- npm run generate:local: generate types from the bundled openapi.json

Closes leeworks-agents/SPARC#26

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 10:26:06 +00:00
agent-company 52972bbff0 feat: add patent trend charts to the Analytics page
Add GET /analytics/trends endpoint returning per-company analysis
counts by month and analysis type distribution over time. Render
these as a line chart (analyses per company) and stacked bar chart
(analysis types) on the Analytics page using recharts.

Closes leeworks-agents/SPARC#24

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 10:23:47 +00:00
agent-company c738f785c3 feat: add side-by-side patent portfolio comparison view
Add /compare route with two-panel layout for comparing company patent
portfolios. Each panel shows patent count, analysis timestamp, and
full LLM narrative. The page is responsive (stacks vertically on
mobile) and supports URL params (?a=nvidia&b=intel) for shareability.

Closes leeworks-agents/SPARC#21

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-03-26 10:22:14 +00:00
20 changed files with 3091 additions and 114 deletions
+15
View File
@@ -47,12 +47,27 @@ STORAGE_BACKEND=local
# AWS_SECRET_ACCESS_KEY=minioadmin
# To start MinIO locally: docker compose --profile s3 up -d minio
# ---- LLM ----
# LLM model to use via OpenRouter
# Supported: anthropic/claude-3.5-sonnet, openai/gpt-4o, openai/gpt-4o-mini,
# google/gemini-pro-1.5, meta-llama/llama-3.1-70b-instruct
# MODEL=anthropic/claude-3.5-sonnet
# ---- Cache ----
# When USE_CACHE=true: check database for cached responses before making API calls
# When USE_CACHE=false: always make fresh API calls (still stores results in database)
USE_CACHE=true
# SERP API cache TTL in hours (how long cached search results are considered fresh)
# SERP_CACHE_TTL_HOURS=24
# ---- Logging ----
# Log level: DEBUG, INFO, WARNING, ERROR, CRITICAL
# LOG_LEVEL=INFO
# ---- Webhooks ----
# Comma-separated list of webhook URLs for job completion and alert notifications
+14
View File
@@ -33,6 +33,20 @@ jobs:
run: |
ruff check SPARC/ tests/
- name: Install Node.js and check TypeScript types
shell: sh
run: |
apk add --no-cache nodejs npm
cd frontend
npm ci
npm run generate:local
if ! git diff --quiet src/api/schema.d.ts; then
echo "ERROR: src/api/schema.d.ts is out of date. Run 'npm run generate:local' and commit the result."
git diff src/api/schema.d.ts
exit 1
fi
npx tsc --noEmit
- name: Run pytest
shell: sh
env:
+10
View File
@@ -40,6 +40,16 @@ jobs:
apk add --no-cache nodejs npm
cd frontend && npm ci
- name: Verify generated API types are up to date
shell: sh
run: |
cd frontend && npm run generate:local
if ! git diff --quiet src/api/schema.d.ts; then
echo "ERROR: src/api/schema.d.ts is out of date. Run 'npm run generate:local' and commit the result."
git diff src/api/schema.d.ts
exit 1
fi
- name: Run TypeScript type check
shell: sh
run: |
+12 -7
View File
@@ -33,7 +33,7 @@ class CompanyAnalyzer:
self.db.connect()
self.db.initialize_schema()
def analyze_company(self, company_name: str, patents: "Patents | None" = None) -> str:
def analyze_company(self, company_name: str, patents: "Patents | None" = None, model: str | None = None) -> str:
"""Analyze a company's performance based on their patent portfolio.
This is the main entry point that orchestrates the full pipeline:
@@ -46,6 +46,7 @@ class CompanyAnalyzer:
Args:
company_name: Name of the company to analyze
patents: Optional pre-fetched Patents result to avoid duplicate API calls
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
Comprehensive analysis of company's innovation and performance outlook
@@ -100,12 +101,12 @@ class CompanyAnalyzer:
# Analyze the full portfolio with LLM
analysis = self.llm_analyzer.analyze_patent_portfolio(
patents_data=processed_patents, company_name=company_name
patents_data=processed_patents, company_name=company_name, model=model
)
return analysis
def analyze_single_patent(self, patent_id: str, company_name: str) -> str:
def analyze_single_patent(self, patent_id: str, company_name: str, model: str | None = None) -> str:
"""Analyze a single patent by ID.
If the patent PDF is not already on disk, this method attempts to
@@ -116,6 +117,7 @@ class CompanyAnalyzer:
Args:
patent_id: Publication ID of the patent (e.g. "US-11234567-B2")
company_name: Name of the company (for context)
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
Analysis of the specific patent's innovation quality
@@ -151,7 +153,7 @@ class CompanyAnalyzer:
minimized_content = SERP.minimize_patent_for_llm(sections)
analysis = self.llm_analyzer.analyze_patent_content(
patent_content=minimized_content, company_name=company_name
patent_content=minimized_content, company_name=company_name, model=model
)
return analysis
@@ -201,18 +203,19 @@ class CompanyAnalyzer:
logger.warning("Failed to process %s: %s", patent.patent_id, e)
return None
def _analyze_company_safe(self, company_name: str) -> CompanyAnalysisResult:
def _analyze_company_safe(self, company_name: str, model: str | None = None) -> CompanyAnalysisResult:
"""Internal wrapper that catches exceptions and returns structured result.
Args:
company_name: Name of the company to analyze
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
CompanyAnalysisResult with success/failure status
"""
try:
# Delegate to analyze_company which handles SERP/patent caching
analysis = self.analyze_company(company_name)
analysis = self.analyze_company(company_name, model=model)
# Determine patent count from cached SERP query
query_hash = hashlib.sha256(company_name.lower().encode()).hexdigest()
@@ -252,6 +255,7 @@ class CompanyAnalyzer:
companies: list[str],
max_workers: int = 3,
progress_callback: Callable[[str, int, int], None] | None = None,
model: str | None = None,
) -> BatchAnalysisResult:
"""Analyze multiple companies' patent portfolios in batch.
@@ -262,6 +266,7 @@ class CompanyAnalyzer:
companies: List of company names to analyze
max_workers: Maximum concurrent analyses (default 3 to avoid rate limits)
progress_callback: Optional callback(company_name, completed, total)
model: Optional LLM model override (e.g. 'openai/gpt-4o')
Returns:
BatchAnalysisResult containing all individual results and summary stats
@@ -273,7 +278,7 @@ class CompanyAnalyzer:
with ThreadPoolExecutor(max_workers=max_workers) as executor:
future_to_company = {
executor.submit(self._analyze_company_safe, company): company
executor.submit(self._analyze_company_safe, company, model): company
for company in companies
}
+295 -3
View File
@@ -41,6 +41,7 @@ class CompanyAnalysisResponse(BaseModel):
patent_count: int
success: bool
error: str | None = None
model: str | None = None
timestamp: datetime
@@ -54,6 +55,15 @@ class BatchAnalysisResponse(BaseModel):
timestamp: datetime
class CompanyAnalysisRequest(BaseModel):
"""Request model for single company analysis with optional model selection."""
model: str | None = Field(
default=None,
description="LLM model to use (e.g. 'anthropic/claude-3.5-sonnet', 'openai/gpt-4o'). Defaults to server config.",
)
class BatchAnalysisRequest(BaseModel):
"""Request model for batch company analysis."""
@@ -63,6 +73,10 @@ class BatchAnalysisRequest(BaseModel):
max_workers: int = Field(
default=3, ge=1, le=5, description="Max concurrent analyses"
)
model: str | None = Field(
default=None,
description="LLM model to use for all analyses in this batch. Defaults to server config.",
)
class JobStatus(BaseModel):
@@ -140,6 +154,7 @@ def _convert_result(result: CompanyAnalysisResult) -> CompanyAnalysisResponse:
patent_count=result.patent_count,
success=result.success,
error=result.error,
model=result.model,
timestamp=result.timestamp,
)
@@ -453,6 +468,118 @@ async def get_analytics(
)
# ============== Model Selection Endpoints ==============
# Supported models via OpenRouter
SUPPORTED_MODELS = [
{"id": "anthropic/claude-3.5-sonnet", "name": "Claude 3.5 Sonnet", "provider": "Anthropic"},
{"id": "openai/gpt-4o", "name": "GPT-4o", "provider": "OpenAI"},
{"id": "openai/gpt-4o-mini", "name": "GPT-4o Mini", "provider": "OpenAI"},
{"id": "google/gemini-pro-1.5", "name": "Gemini Pro 1.5", "provider": "Google"},
{"id": "meta-llama/llama-3.1-70b-instruct", "name": "Llama 3.1 70B", "provider": "Meta"},
]
_SUPPORTED_MODEL_IDS = {m["id"] for m in SUPPORTED_MODELS}
def _validate_model(model: str | None) -> None:
"""Raise HTTP 400 if *model* is not in the supported allow-list."""
if model is not None and model not in _SUPPORTED_MODEL_IDS:
raise HTTPException(
status_code=400,
detail=(
f"Unsupported model '{model}'. "
f"Supported models: {', '.join(sorted(_SUPPORTED_MODEL_IDS))}"
),
)
@app.get("/models", tags=["System"])
async def list_models():
"""List supported LLM models for analysis.
Returns the available models that can be passed as the `model` field
in analysis requests. The default model is determined by the `MODEL`
environment variable on the server.
"""
return {
"models": SUPPORTED_MODELS,
"default": config.model,
}
@app.get("/analytics/trends", tags=["Analytics"])
async def get_analytics_trends(
days: int = Query(default=90, ge=7, le=365),
_: UserResponse = Depends(get_current_user),
):
"""Get trend data for patent analysis over time.
Returns two datasets:
- ``by_month``: analysis count per company per month
- ``by_type_over_time``: analysis type distribution per month
Args:
days: Number of days to look back (default 90)
Returns:
Trend data suitable for time-series and distribution charts
"""
db = get_db_client()
with db.get_conn() as conn:
with conn.cursor() as cur:
# Analyses per company per month
cur.execute(
"""
SELECT
TO_CHAR(timestamp, 'YYYY-MM') AS month,
company_name,
COUNT(*) AS count
FROM llm_messages
WHERE timestamp >= NOW() - INTERVAL '%s days'
AND is_cached = FALSE
AND company_name IS NOT NULL
GROUP BY month, company_name
ORDER BY month
""",
(days,),
)
by_month_rows = cur.fetchall()
# Analysis type distribution per month
cur.execute(
"""
SELECT
TO_CHAR(timestamp, 'YYYY-MM') AS month,
analysis_type,
COUNT(*) AS count
FROM llm_messages
WHERE timestamp >= NOW() - INTERVAL '%s days'
AND is_cached = FALSE
GROUP BY month, analysis_type
ORDER BY month
""",
(days,),
)
by_type_rows = cur.fetchall()
by_month = [
{"month": row[0], "company_name": row[1], "count": row[2]}
for row in by_month_rows
]
by_type_over_time = [
{"month": row[0], "analysis_type": row[1], "count": row[2]}
for row in by_type_rows
]
return {
"by_month": by_month,
"by_type_over_time": by_type_over_time,
"period_days": days,
}
# ============== Export Endpoints ==============
@@ -508,6 +635,164 @@ async def export_company_csv(
)
@app.get("/export/{company_name}/pdf", tags=["Export"])
async def export_company_pdf(
company_name: str,
_: UserResponse = Depends(get_current_user),
):
"""Export analysis results for a company as a formatted PDF report.
Returns all stored analysis records for the given company, including
analysis type, model used, response text, and timestamp, formatted
as a downloadable PDF document.
Args:
company_name: Company name to export results for
Returns:
PDF file download
"""
import io
import textwrap
from reportlab.lib import colors
from reportlab.lib.pagesizes import letter
from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
from reportlab.lib.units import inch
from reportlab.platypus import (
Paragraph,
SimpleDocTemplate,
Spacer,
Table,
TableStyle,
)
db = get_db_client()
with db.get_conn() as conn:
with conn.cursor() as cur:
cur.execute(
"""
SELECT company_name, analysis_type, model, response, timestamp
FROM llm_messages
WHERE LOWER(company_name) = LOWER(%s) AND is_cached = FALSE
ORDER BY timestamp DESC
""",
(company_name,),
)
rows = cur.fetchall()
if not rows:
raise HTTPException(status_code=404, detail=f"No analysis results found for '{company_name}'")
buffer = io.BytesIO()
doc = SimpleDocTemplate(
buffer,
pagesize=letter,
rightMargin=0.75 * inch,
leftMargin=0.75 * inch,
topMargin=0.75 * inch,
bottomMargin=0.75 * inch,
)
styles = getSampleStyleSheet()
title_style = ParagraphStyle(
"CustomTitle",
parent=styles["Title"],
fontSize=20,
spaceAfter=6,
)
subtitle_style = ParagraphStyle(
"Subtitle",
parent=styles["Normal"],
fontSize=11,
textColor=colors.grey,
spaceAfter=20,
)
heading_style = ParagraphStyle(
"SectionHeading",
parent=styles["Heading2"],
fontSize=13,
spaceBefore=16,
spaceAfter=8,
textColor=colors.HexColor("#1a1a2e"),
)
body_style = ParagraphStyle(
"BodyText",
parent=styles["Normal"],
fontSize=9,
leading=13,
spaceAfter=10,
)
elements = []
# Title and date
display_name = rows[0][0] # Use the casing from the database
analysis_date = datetime.now().strftime("%Y-%m-%d")
elements.append(Paragraph(f"SPARC Analysis Report: {display_name}", title_style))
elements.append(Paragraph(f"Generated on {analysis_date}", subtitle_style))
# Summary table
summary_data = [
["Total Analyses", str(len(rows))],
["Analysis Types", ", ".join(sorted(set(r[1] for r in rows)))],
["Models Used", ", ".join(sorted(set(r[2] for r in rows)))],
]
summary_table = Table(summary_data, colWidths=[2 * inch, 4.5 * inch])
summary_table.setStyle(
TableStyle(
[
("BACKGROUND", (0, 0), (0, -1), colors.HexColor("#f0f0f5")),
("FONTNAME", (0, 0), (0, -1), "Helvetica-Bold"),
("FONTSIZE", (0, 0), (-1, -1), 9),
("PADDING", (0, 0), (-1, -1), 6),
("GRID", (0, 0), (-1, -1), 0.5, colors.HexColor("#cccccc")),
("VALIGN", (0, 0), (-1, -1), "TOP"),
]
)
)
elements.append(summary_table)
elements.append(Spacer(1, 16))
# Individual analysis sections
for i, row in enumerate(rows, 1):
_, analysis_type, model, response, timestamp = row
ts_str = timestamp.strftime("%Y-%m-%d %H:%M:%S") if hasattr(timestamp, "strftime") else str(timestamp)
elements.append(
Paragraph(f"Analysis {i}: {analysis_type} (via {model})", heading_style)
)
elements.append(
Paragraph(f"<i>Performed: {ts_str}</i>", body_style)
)
# Wrap long response text into paragraphs, escaping XML special chars
safe_response = (
response.replace("&", "&amp;")
.replace("<", "&lt;")
.replace(">", "&gt;")
)
# Split into manageable paragraphs to avoid overflow
for line in safe_response.split("\n"):
if line.strip():
elements.append(Paragraph(line, body_style))
else:
elements.append(Spacer(1, 4))
elements.append(Spacer(1, 10))
doc.build(elements)
buffer.seek(0)
safe_name = company_name.replace(" ", "_").lower()
filename = f"{safe_name}-analysis-{analysis_date}.pdf"
return StreamingResponse(
iter([buffer.getvalue()]),
media_type="application/pdf",
headers={"Content-Disposition": f'attachment; filename="{filename}"'},
)
# ============== System Endpoints ==============
@@ -528,6 +813,7 @@ async def health_check():
)
async def analyze_company(
company_name: str,
model: str | None = Query(default=None, description="LLM model to use (e.g. 'openai/gpt-4o'). Defaults to server config."),
_: UserResponse = Depends(get_current_user),
):
"""Analyze a single company's patent portfolio.
@@ -537,14 +823,16 @@ async def analyze_company(
Args:
company_name: Name of the company to analyze (e.g., "nvidia", "intel")
model: Optional LLM model override
Returns:
Analysis results including patent count, AI insights, and success status
"""
_validate_model(model)
if not _analyzer:
raise HTTPException(status_code=503, detail="Analyzer not initialized")
result = _analyzer._analyze_company_safe(company_name)
result = _analyzer._analyze_company_safe(company_name, model=model)
return _convert_result(result)
@@ -600,12 +888,14 @@ async def analyze_companies_batch(
Returns:
Batch results with individual company analyses and summary statistics
"""
_validate_model(request.model)
if not _analyzer:
raise HTTPException(status_code=503, detail="Analyzer not initialized")
result = _analyzer.analyze_companies(
companies=request.companies,
max_workers=request.max_workers,
model=request.model,
)
return _convert_batch_result(result)
@@ -637,7 +927,7 @@ def _job_row_to_status(row: dict) -> JobStatus:
)
def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
def _run_batch_job(job_id: str, companies: list[str], max_workers: int, model: str | None = None):
"""Background task for batch analysis."""
import json as _json
global _analyzer
@@ -662,6 +952,7 @@ def _run_batch_job(job_id: str, companies: list[str], max_workers: int):
companies=companies,
max_workers=max_workers,
progress_callback=progress_callback,
model=model,
)
batch_response = _convert_batch_result(result)
db.update_job(
@@ -708,6 +999,7 @@ async def analyze_companies_async(
Returns:
Job status with job_id for polling
"""
_validate_model(request.model)
global _job_counter
_job_counter += 1
@@ -717,7 +1009,7 @@ async def analyze_companies_async(
job_row = db.create_job(job_id=job_id, total_companies=len(request.companies))
background_tasks.add_task(
_run_batch_job, job_id, request.companies, request.max_workers
_run_batch_job, job_id, request.companies, request.max_workers, request.model
)
return _job_row_to_status(job_row)
+18 -12
View File
@@ -40,12 +40,13 @@ class LLMAnalyzer:
else:
self.client = None
def analyze_patent_content(self, patent_content: str, company_name: str) -> str:
def analyze_patent_content(self, patent_content: str, company_name: str, model: str | None = None) -> str:
"""Analyze patent content to estimate company innovation and performance.
Args:
patent_content: Minimized patent text (abstract, claims, summary)
company_name: Name of the company for context
model: Optional model override (e.g. "openai/gpt-4o"). Defaults to config.
Returns:
Analysis text describing innovation quality and potential impact
@@ -63,6 +64,8 @@ Patent Content:
Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals about the company's technical direction and competitive advantage."""
effective_model = model or self.model
if self.test_mode:
logger.debug("TEST MODE - Prompt that would be sent to LLM:\n%s", prompt)
return "[TEST MODE - No API call made]"
@@ -81,7 +84,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
response=cached["response"],
company_name=company_name,
analysis_type="single_patent",
model=self.model,
model=effective_model,
metadata={
"patent_content_length": len(patent_content),
"cache_hit": True,
@@ -94,7 +97,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
# Call API if no cache hit and client is available
if self.client:
response = self.client.chat.completions.create(
model=self.model,
model=effective_model,
max_tokens=1024,
messages=[{"role": "user", "content": prompt}],
)
@@ -106,7 +109,7 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
response=response_text,
company_name=company_name,
analysis_type="single_patent",
model=self.model,
model=effective_model,
metadata={"patent_content_length": len(patent_content)},
token_usage={
"prompt_tokens": response.usage.prompt_tokens,
@@ -124,13 +127,13 @@ Provide a concise analysis (2-3 paragraphs) focusing on what this patent reveals
response=placeholder,
company_name=company_name,
analysis_type="single_patent",
model=self.model,
model=effective_model,
metadata={"patent_content_length": len(patent_content), "pending": True}
)
return placeholder
def analyze_patent_portfolio(
self, patents_data: list[Dict[str, str]], company_name: str
self, patents_data: list[Dict[str, str]], company_name: str, model: str | None = None
) -> str:
"""Analyze multiple patents to estimate overall company performance.
@@ -165,13 +168,16 @@ Patent Portfolio:
Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the company's innovation strength and performance outlook."""
effective_model = model or self.model
if self.test_mode:
logger.debug("TEST MODE - Portfolio prompt:\n%s", prompt)
return "[TEST MODE]"
metadata = {
"patent_count": len(patents_data),
"patent_ids": [p['patent_id'] for p in patents_data]
"patent_ids": [p['patent_id'] for p in patents_data],
"model": effective_model,
}
# Check cache first
@@ -188,7 +194,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
response=cached["response"],
company_name=company_name,
analysis_type="portfolio",
model=self.model,
model=effective_model,
metadata={
**metadata,
"cache_hit": True,
@@ -202,7 +208,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
if self.client:
try:
response = self.client.chat.completions.create(
model=self.model,
model=effective_model,
max_tokens=2048,
messages=[{"role": "user", "content": prompt}],
)
@@ -215,7 +221,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
response=response_text,
company_name=company_name,
analysis_type="portfolio",
model=self.model,
model=effective_model,
metadata=metadata,
token_usage={
"prompt_tokens": response.usage.prompt_tokens,
@@ -235,7 +241,7 @@ Provide a comprehensive analysis (4-5 paragraphs) with a final verdict on the co
response=placeholder,
company_name=company_name,
analysis_type="portfolio",
model=self.model,
model=effective_model,
metadata={**metadata, "pending": True}
)
return placeholder
+1
View File
@@ -24,6 +24,7 @@ class CompanyAnalysisResult:
patent_count: int
success: bool
error: str | None = None
model: str | None = None
timestamp: datetime = field(default_factory=datetime.now)
+3
View File
@@ -7,6 +7,8 @@
"dev": "vite",
"build": "tsc -b && vite build",
"lint": "eslint .",
"generate": "openapi-typescript http://localhost:8000/api/openapi.json -o src/api/schema.d.ts",
"generate:local": "openapi-typescript src/api/openapi.json -o src/api/schema.d.ts",
"typecheck": "tsc --noEmit",
"preview": "vite preview"
},
@@ -31,6 +33,7 @@
"globals": "^15.8.0",
"postcss": "^8.4.39",
"tailwindcss": "^3.4.4",
"openapi-typescript": "^7.0.0",
"typescript": "~5.5.3",
"typescript-eslint": "^8.0.0",
"vite": "^5.3.3"
+2
View File
@@ -11,6 +11,7 @@ import { Batch } from './pages/Batch';
import { AnalyticsPage } from './pages/Analytics';
import { About } from './pages/About';
import { AdminUsers } from './pages/AdminUsers';
import { Compare } from './pages/Compare';
const queryClient = new QueryClient({
defaultOptions: {
@@ -43,6 +44,7 @@ function App() {
<Route path="/analysis" element={<Analysis />} />
<Route path="/batch" element={<Batch />} />
<Route path="/analytics" element={<AnalyticsPage />} />
<Route path="/compare" element={<Compare />} />
<Route path="/about" element={<About />} />
{/* Admin routes */}
+54 -4
View File
@@ -89,29 +89,53 @@ export const authApi = {
},
};
// Model types
export interface ModelInfo {
id: string;
name: string;
provider: string;
}
export interface ModelsResponse {
models: ModelInfo[];
default: string;
}
// Analysis API
export const analysisApi = {
analyzeCompany: async (companyName: string): Promise<CompanyAnalysis> => {
const response = await api.get<CompanyAnalysis>(`/analyze/${encodeURIComponent(companyName)}`);
analyzeCompany: async (companyName: string, model?: string): Promise<CompanyAnalysis> => {
const params = new URLSearchParams();
if (model) params.append('model', model);
const qs = params.toString();
const response = await api.get<CompanyAnalysis>(
`/analyze/${encodeURIComponent(companyName)}${qs ? `?${qs}` : ''}`
);
return response.data;
},
analyzeBatch: async (companies: string[], maxWorkers = 3): Promise<BatchAnalysisResult> => {
analyzeBatch: async (companies: string[], maxWorkers = 3, model?: string): Promise<BatchAnalysisResult> => {
const response = await api.post<BatchAnalysisResult>('/analyze/batch', {
companies,
max_workers: maxWorkers,
...(model ? { model } : {}),
});
return response.data;
},
analyzeBatchAsync: async (companies: string[], maxWorkers = 3): Promise<JobStatus> => {
analyzeBatchAsync: async (companies: string[], maxWorkers = 3, model?: string): Promise<JobStatus> => {
const response = await api.post<JobStatus>('/analyze/batch/async', {
companies,
max_workers: maxWorkers,
...(model ? { model } : {}),
});
return response.data;
},
listModels: async (): Promise<ModelsResponse> => {
const response = await api.get<ModelsResponse>('/models');
return response.data;
},
getJobStatus: async (jobId: string): Promise<JobStatus> => {
const response = await api.get<JobStatus>(`/jobs/${jobId}`);
return response.data;
@@ -141,14 +165,40 @@ export const exportApi = {
link.remove();
window.URL.revokeObjectURL(url);
},
exportPdf: async (companyName: string): Promise<void> => {
const response = await api.get(`/export/${encodeURIComponent(companyName)}/pdf`, {
responseType: 'blob',
});
const safeName = companyName.toLowerCase().replace(/\s+/g, '_');
const date = new Date().toISOString().split('T')[0];
const url = window.URL.createObjectURL(new Blob([response.data], { type: 'application/pdf' }));
const link = document.createElement('a');
link.href = url;
link.setAttribute('download', `${safeName}-analysis-${date}.pdf`);
document.body.appendChild(link);
link.click();
link.remove();
window.URL.revokeObjectURL(url);
},
};
// Analytics API
export interface TrendData {
by_month: Array<{ month: string; company_name: string; count: number }>;
by_type_over_time: Array<{ month: string; analysis_type: string; count: number }>;
period_days: number;
}
export const analyticsApi = {
getAnalytics: async (days = 30): Promise<Analytics> => {
const response = await api.get<Analytics>(`/analytics?days=${days}`);
return response.data;
},
getTrends: async (days = 90): Promise<TrendData> => {
const response = await api.get<TrendData>(`/analytics/trends?days=${days}`);
return response.data;
},
};
// Admin API
File diff suppressed because it is too large Load Diff
+975
View File
@@ -0,0 +1,975 @@
/**
* This file was auto-generated by openapi-typescript.
* Do not make direct changes to the file.
*/
export interface paths {
"/auth/register": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
/**
* Register
* @description Register a new user.
*
* The first registered user automatically becomes an admin.
*/
post: operations["register_auth_register_post"];
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/auth/login": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
/**
* Login
* @description Authenticate user and return JWT tokens.
*/
post: operations["login_auth_login_post"];
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/auth/refresh": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
/**
* Refresh Token
* @description Refresh access token using refresh token.
*/
post: operations["refresh_token_auth_refresh_post"];
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/auth/me": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* Get Me
* @description Get current authenticated user.
*/
get: operations["get_me_auth_me_get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/admin/users": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* List Users
* @description List all users (admin only).
*/
get: operations["list_users_admin_users_get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/admin/users/{user_id}/role": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
/**
* Update User Role
* @description Update a user's role (admin only).
*/
patch: operations["update_user_role_admin_users__user_id__role_patch"];
trace?: never;
};
"/admin/users/{user_id}": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
post?: never;
/**
* Delete User
* @description Delete a user (admin only).
*/
delete: operations["delete_user_admin_users__user_id__delete"];
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/analytics": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* Get Analytics
* @description Get analytics data (authenticated users only).
*/
get: operations["get_analytics_analytics_get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/health": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* Health Check
* @description Check API health status.
*/
get: operations["health_check_health_get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/analyze/{company_name}": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* Analyze Company
* @description Analyze a single company's patent portfolio.
*
* This endpoint retrieves recent patents for the specified company,
* parses them, and uses AI to generate a comprehensive analysis.
*
* Args:
* company_name: Name of the company to analyze (e.g., "nvidia", "intel")
*
* Returns:
* Analysis results including patent count, AI insights, and success status
*/
get: operations["analyze_company_analyze__company_name__get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/analyze/batch": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
/**
* Analyze Companies Batch
* @description Analyze multiple companies' patent portfolios.
*
* Processes companies concurrently for improved performance.
* Limited to 20 companies per request.
*
* Args:
* request: List of company names and optional worker count
*
* Returns:
* Batch results with individual company analyses and summary statistics
*/
post: operations["analyze_companies_batch_analyze_batch_post"];
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/analyze/batch/async": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
get?: never;
put?: never;
/**
* Analyze Companies Async
* @description Start an asynchronous batch analysis job.
*
* Returns immediately with a job ID that can be used to poll for status.
* Useful for large batch analyses that may take a long time.
*
* Args:
* request: List of company names and optional worker count
*
* Returns:
* Job status with job_id for polling
*/
post: operations["analyze_companies_async_analyze_batch_async_post"];
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/jobs/{job_id}": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* Get Job Status
* @description Get the status of a background analysis job.
*
* Args:
* job_id: The job ID returned from the async batch endpoint
*
* Returns:
* Current job status including progress and results when complete
*/
get: operations["get_job_status_jobs__job_id__get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
"/jobs": {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
/**
* List Jobs
* @description List all analysis jobs.
*
* Args:
* status: Optional filter by job status
* limit: Maximum number of jobs to return (default 10, max 100)
*
* Returns:
* List of job statuses
*/
get: operations["list_jobs_jobs_get"];
put?: never;
post?: never;
delete?: never;
options?: never;
head?: never;
patch?: never;
trace?: never;
};
}
export type webhooks = Record<string, never>;
export interface components {
schemas: {
/**
* AnalyticsResponse
* @description Analytics response model.
*/
AnalyticsResponse: {
/** Total Messages */
total_messages: number;
/** By Company */
by_company: {
[key: string]: unknown;
}[];
/** By Type */
by_type: {
[key: string]: unknown;
}[];
/** Period Days */
period_days: number;
};
/**
* BatchAnalysisRequest
* @description Request model for batch company analysis.
*/
BatchAnalysisRequest: {
/**
* Companies
* @description List of company names to analyze
*/
companies: string[];
/**
* Max Workers
* @description Max concurrent analyses
* @default 3
*/
max_workers: number;
};
/**
* BatchAnalysisResponse
* @description Response model for batch company analysis.
*/
BatchAnalysisResponse: {
/** Results */
results: components["schemas"]["CompanyAnalysisResponse"][];
/** Total Companies */
total_companies: number;
/** Successful */
successful: number;
/** Failed */
failed: number;
/**
* Timestamp
* Format: date-time
*/
timestamp: string;
};
/**
* CompanyAnalysisResponse
* @description Response model for single company analysis.
*/
CompanyAnalysisResponse: {
/** Company Name */
company_name: string;
/** Analysis */
analysis: string;
/** Patent Count */
patent_count: number;
/** Success */
success: boolean;
/** Error */
error?: string | null;
/**
* Timestamp
* Format: date-time
*/
timestamp: string;
};
/** HTTPValidationError */
HTTPValidationError: {
/** Detail */
detail?: components["schemas"]["ValidationError"][];
};
/**
* HealthResponse
* @description Health check response.
*/
HealthResponse: {
/** Status */
status: string;
/** Version */
version: string;
/**
* Timestamp
* Format: date-time
*/
timestamp: string;
};
/**
* JobStatus
* @description Status of a background analysis job.
*/
JobStatus: {
/** Job Id */
job_id: string;
/** Status */
status: string;
/** Progress */
progress: number;
/** Total Companies */
total_companies: number;
/** Completed Companies */
completed_companies: number;
result?: components["schemas"]["BatchAnalysisResponse"] | null;
/** Error */
error?: string | null;
};
/**
* LoginRequest
* @description User login request.
*/
LoginRequest: {
/**
* Email
* Format: email
*/
email: string;
/** Password */
password: string;
};
/**
* RefreshRequest
* @description Token refresh request.
*/
RefreshRequest: {
/** Refresh Token */
refresh_token: string;
};
/**
* RegisterRequest
* @description User registration request.
*/
RegisterRequest: {
/**
* Email
* Format: email
*/
email: string;
/**
* Password
* @description Password (min 8 characters)
*/
password: string;
};
/**
* TokenResponse
* @description Token response model.
*/
TokenResponse: {
/** Access Token */
access_token: string;
/** Refresh Token */
refresh_token: string;
/**
* Token Type
* @default bearer
*/
token_type: string;
};
/**
* UpdateRoleRequest
* @description Update user role request.
*/
UpdateRoleRequest: {
/** Role */
role: string;
};
/**
* UserResponse
* @description User response model.
*/
UserResponse: {
/** Id */
id: number;
/** Email */
email: string;
/** Role */
role: string;
/**
* Created At
* Format: date-time
*/
created_at: string;
};
/** ValidationError */
ValidationError: {
/** Location */
loc: (string | number)[];
/** Message */
msg: string;
/** Error Type */
type: string;
/** Input */
input?: unknown;
/** Context */
ctx?: Record<string, never>;
};
};
responses: never;
parameters: never;
requestBodies: never;
headers: never;
pathItems: never;
}
export type $defs = Record<string, never>;
export interface operations {
register_auth_register_post: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["RegisterRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["UserResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
login_auth_login_post: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["LoginRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["TokenResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
refresh_token_auth_refresh_post: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["RefreshRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["TokenResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
get_me_auth_me_get: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["UserResponse"];
};
};
};
};
list_users_admin_users_get: {
parameters: {
query?: {
limit?: number;
offset?: number;
};
header?: never;
path?: never;
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["UserResponse"][];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
update_user_role_admin_users__user_id__role_patch: {
parameters: {
query?: never;
header?: never;
path: {
user_id: number;
};
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["UpdateRoleRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["UserResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
delete_user_admin_users__user_id__delete: {
parameters: {
query?: never;
header?: never;
path: {
user_id: number;
};
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": unknown;
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
get_analytics_analytics_get: {
parameters: {
query?: {
days?: number;
};
header?: never;
path?: never;
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["AnalyticsResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
health_check_health_get: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HealthResponse"];
};
};
};
};
analyze_company_analyze__company_name__get: {
parameters: {
query?: never;
header?: never;
path: {
company_name: string;
};
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["CompanyAnalysisResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
analyze_companies_batch_analyze_batch_post: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["BatchAnalysisRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["BatchAnalysisResponse"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
analyze_companies_async_analyze_batch_async_post: {
parameters: {
query?: never;
header?: never;
path?: never;
cookie?: never;
};
requestBody: {
content: {
"application/json": components["schemas"]["BatchAnalysisRequest"];
};
};
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["JobStatus"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
get_job_status_jobs__job_id__get: {
parameters: {
query?: never;
header?: never;
path: {
job_id: string;
};
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["JobStatus"];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
list_jobs_jobs_get: {
parameters: {
query?: {
/** @description Filter by status: pending, running, completed, failed */
status?: string | null;
limit?: number;
};
header?: never;
path?: never;
cookie?: never;
};
requestBody?: never;
responses: {
/** @description Successful Response */
200: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["JobStatus"][];
};
};
/** @description Validation Error */
422: {
headers: {
[name: string]: unknown;
};
content: {
"application/json": components["schemas"]["HTTPValidationError"];
};
};
};
};
}
+2 -1
View File
@@ -1,7 +1,7 @@
import { Outlet, NavLink, useNavigate } from 'react-router-dom';
import { useAuth } from '../context/AuthContext';
import { useTheme } from '../context/ThemeContext';
import { Search, Layers, BarChart3, Info, Users, LogOut, Sun, Moon } from 'lucide-react';
import { Search, Layers, BarChart3, Info, Users, LogOut, GitCompareArrows, Sun, Moon } from 'lucide-react';
export function Layout() {
const { user, isAdmin, logout } = useAuth();
@@ -17,6 +17,7 @@ export function Layout() {
{ to: '/analysis', icon: Search, label: 'Analysis' },
{ to: '/batch', icon: Layers, label: 'Batch' },
{ to: '/analytics', icon: BarChart3, label: 'Analytics' },
{ to: '/compare', icon: GitCompareArrows, label: 'Compare' },
{ to: '/about', icon: Info, label: 'About' },
];
+75 -34
View File
@@ -1,15 +1,21 @@
import { useState } from 'react';
import { useMutation } from '@tanstack/react-query';
import { useMutation, useQuery } from '@tanstack/react-query';
import { analysisApi, exportApi } from '../api/client';
import { Search, CheckCircle, AlertCircle, Clock, FileText, Download } from 'lucide-react';
import { Search, CheckCircle, AlertCircle, Clock, FileText, Download, ChevronDown } from 'lucide-react';
import type { CompanyAnalysis } from '../types';
export function Analysis() {
const [companyName, setCompanyName] = useState('');
const [selectedModel, setSelectedModel] = useState('');
const [result, setResult] = useState<CompanyAnalysis | null>(null);
const modelsQuery = useQuery({
queryKey: ['models'],
queryFn: () => analysisApi.listModels(),
});
const mutation = useMutation({
mutationFn: (name: string) => analysisApi.analyzeCompany(name),
mutationFn: (name: string) => analysisApi.analyzeCompany(name, selectedModel || undefined),
onSuccess: (data) => setResult(data),
});
@@ -33,31 +39,57 @@ export function Analysis() {
</div>
{/* Search Form */}
<form onSubmit={handleSubmit} className="flex gap-4">
<div className="flex-1 relative">
<Search className="absolute left-4 top-1/2 -translate-y-1/2 text-text-secondary" size={18} />
<input
type="text"
value={companyName}
onChange={(e) => setCompanyName(e.target.value)}
placeholder="Enter company name (e.g., nvidia, intel, amd)"
className="w-full bg-bg-card/80 border border-primary/30 rounded-xl pl-12 pr-4 py-3 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
/>
<form onSubmit={handleSubmit} className="space-y-4">
<div className="flex gap-4">
<div className="flex-1 relative">
<Search className="absolute left-4 top-1/2 -translate-y-1/2 text-text-secondary" size={18} />
<input
type="text"
value={companyName}
onChange={(e) => setCompanyName(e.target.value)}
placeholder="Enter company name (e.g., nvidia, intel, amd)"
className="w-full bg-bg-card/80 border border-primary/30 rounded-xl pl-12 pr-4 py-3 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
/>
</div>
<button
type="submit"
disabled={mutation.isPending || !companyName.trim()}
className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-3 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
>
{mutation.isPending ? (
<div className="animate-spin rounded-full h-5 w-5 border-t-2 border-b-2 border-white"></div>
) : (
<>
<Search size={18} />
Analyze
</>
)}
</button>
</div>
{/* Model Selector */}
<div className="flex items-center gap-3">
<label className="text-sm font-medium text-text-secondary whitespace-nowrap">
LLM Model
</label>
<div className="relative flex-1 max-w-xs">
<select
value={selectedModel}
onChange={(e) => setSelectedModel(e.target.value)}
className="w-full appearance-none bg-bg-card/80 border border-primary/30 rounded-lg pl-3 pr-8 py-2 text-sm text-text-primary focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all cursor-pointer"
>
<option value="">
{modelsQuery.data ? `Default (${modelsQuery.data.default})` : 'Default'}
</option>
{modelsQuery.data?.models.map((m) => (
<option key={m.id} value={m.id}>
{m.name} ({m.provider})
</option>
))}
</select>
<ChevronDown className="absolute right-2 top-1/2 -translate-y-1/2 text-text-secondary pointer-events-none" size={16} />
</div>
</div>
<button
type="submit"
disabled={mutation.isPending || !companyName.trim()}
className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-3 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
>
{mutation.isPending ? (
<div className="animate-spin rounded-full h-5 w-5 border-t-2 border-b-2 border-white"></div>
) : (
<>
<Search size={18} />
Analyze
</>
)}
</button>
</form>
{/* Error */}
@@ -110,13 +142,22 @@ export function Analysis() {
<h3 className="text-lg font-semibold text-text-primary">
AI Analysis Results
</h3>
<button
onClick={() => exportApi.exportCsv(result.company_name)}
className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
>
<Download size={14} />
Export CSV
</button>
<div className="flex items-center gap-2">
<button
onClick={() => exportApi.exportCsv(result.company_name)}
className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
>
<Download size={14} />
Export CSV
</button>
<button
onClick={() => exportApi.exportPdf(result.company_name)}
className="flex items-center gap-2 text-sm bg-primary/20 hover:bg-primary/30 text-primary font-medium px-3 py-1.5 rounded-lg transition-colors"
>
<FileText size={14} />
Export PDF
</button>
</div>
</div>
<div className="prose prose-invert max-w-none">
<div className="text-text-primary whitespace-pre-wrap leading-relaxed">
+114 -1
View File
@@ -2,7 +2,7 @@ import { useState } from 'react';
import { useQuery } from '@tanstack/react-query';
import { analyticsApi } from '../api/client';
import { AlertCircle, Database } from 'lucide-react';
import { PieChart, Pie, Cell, BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Legend } from 'recharts';
import { PieChart, Pie, Cell, BarChart, Bar, LineChart, Line, XAxis, YAxis, Tooltip, ResponsiveContainer, Legend } from 'recharts';
const COLORS = ['#6366f1', '#0ea5e9', '#10b981', '#f59e0b', '#ef4444', '#8b5cf6', '#ec4899', '#14b8a6'];
@@ -14,6 +14,11 @@ export function AnalyticsPage() {
queryFn: () => analyticsApi.getAnalytics(days),
});
const trendsQuery = useQuery({
queryKey: ['analytics-trends', days],
queryFn: () => analyticsApi.getTrends(days),
});
if (isLoading) {
return (
<div className="space-y-6">
@@ -189,6 +194,114 @@ export function AnalyticsPage() {
</div>
)}
</div>
{/* Trend Charts */}
{trendsQuery.data && (
<div className="space-y-6">
<h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2">
Trends Over Time
</h3>
<div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
{/* Patent count over time per company (line chart) */}
{trendsQuery.data.by_month.length > 0 && (() => {
// Pivot data: each month as a row, companies as columns
const companies = [...new Set(trendsQuery.data!.by_month.map(d => d.company_name))];
const months = [...new Set(trendsQuery.data!.by_month.map(d => d.month))].sort();
const pivoted = months.map(month => {
const row: Record<string, string | number> = { month };
for (const c of companies) {
const entry = trendsQuery.data!.by_month.find(d => d.month === month && d.company_name === c);
row[c] = entry?.count || 0;
}
return row;
});
return (
<div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6">
<h4 className="text-md font-semibold text-text-primary mb-4">Analyses per Company Over Time</h4>
<ResponsiveContainer width="100%" height={300}>
<LineChart data={pivoted}>
<XAxis dataKey="month" stroke="#94a3b8" fontSize={12} />
<YAxis stroke="#94a3b8" fontSize={12} />
<Tooltip
contentStyle={{
backgroundColor: '#1e293b',
border: '1px solid rgba(99, 102, 241, 0.3)',
borderRadius: '8px',
}}
labelStyle={{ color: '#f8fafc' }}
/>
<Legend />
{companies.map((company, idx) => (
<Line
key={company}
type="monotone"
dataKey={company}
stroke={COLORS[idx % COLORS.length]}
strokeWidth={2}
dot={{ r: 4 }}
name={company.toUpperCase()}
/>
))}
</LineChart>
</ResponsiveContainer>
</div>
);
})()}
{/* Analysis type distribution over time (stacked bar) */}
{trendsQuery.data.by_type_over_time.length > 0 && (() => {
const types = [...new Set(trendsQuery.data!.by_type_over_time.map(d => d.analysis_type))];
const months = [...new Set(trendsQuery.data!.by_type_over_time.map(d => d.month))].sort();
const pivoted = months.map(month => {
const row: Record<string, string | number> = { month };
for (const t of types) {
const entry = trendsQuery.data!.by_type_over_time.find(d => d.month === month && d.analysis_type === t);
row[t] = entry?.count || 0;
}
return row;
});
return (
<div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6">
<h4 className="text-md font-semibold text-text-primary mb-4">Analysis Types Over Time</h4>
<ResponsiveContainer width="100%" height={300}>
<BarChart data={pivoted}>
<XAxis dataKey="month" stroke="#94a3b8" fontSize={12} />
<YAxis stroke="#94a3b8" fontSize={12} />
<Tooltip
contentStyle={{
backgroundColor: '#1e293b',
border: '1px solid rgba(99, 102, 241, 0.3)',
borderRadius: '8px',
}}
labelStyle={{ color: '#f8fafc' }}
/>
<Legend />
{types.map((type, idx) => (
<Bar
key={type}
dataKey={type}
stackId="types"
fill={COLORS[idx % COLORS.length]}
name={type}
/>
))}
</BarChart>
</ResponsiveContainer>
</div>
);
})()}
</div>
{trendsQuery.data.by_month.length === 0 && (
<div className="text-text-secondary text-center py-8">
No trend data available yet. Run analyses over multiple days to see trends.
</div>
)}
</div>
)}
</div>
);
}
+181 -10
View File
@@ -1,20 +1,34 @@
import { useState } from 'react';
import { useMutation } from '@tanstack/react-query';
import { useMutation, useQuery } from '@tanstack/react-query';
import { analysisApi } from '../api/client';
import { Rocket, CheckCircle, AlertCircle, ChevronDown, ChevronUp } from 'lucide-react';
import { Rocket, CheckCircle, AlertCircle, ChevronDown, ChevronUp, RefreshCw, Inbox } from 'lucide-react';
import { BarChart, Bar, XAxis, YAxis, Tooltip, ResponsiveContainer, Cell } from 'recharts';
import type { BatchAnalysisResult } from '../types';
export function Batch() {
const [companiesInput, setCompaniesInput] = useState('');
const [maxWorkers, setMaxWorkers] = useState(3);
const [selectedModel, setSelectedModel] = useState('');
const [result, setResult] = useState<BatchAnalysisResult | null>(null);
const [expandedItems, setExpandedItems] = useState<Set<string>>(new Set());
const modelsQuery = useQuery({
queryKey: ['models'],
queryFn: () => analysisApi.listModels(),
});
const jobsQuery = useQuery({
queryKey: ['jobs'],
queryFn: () => analysisApi.listJobs(undefined, 20),
});
const mutation = useMutation({
mutationFn: ({ companies, workers }: { companies: string[]; workers: number }) =>
analysisApi.analyzeBatch(companies, workers),
onSuccess: (data) => setResult(data),
analysisApi.analyzeBatch(companies, workers, selectedModel || undefined),
onSuccess: (data) => {
setResult(data);
jobsQuery.refetch();
},
});
const handleSubmit = (e: React.FormEvent) => {
@@ -85,6 +99,29 @@ export function Batch() {
<div className="text-center text-text-primary font-semibold">{maxWorkers}</div>
</div>
<div>
<label className="block text-sm font-medium text-text-secondary mb-2">
LLM Model
</label>
<div className="relative">
<select
value={selectedModel}
onChange={(e) => setSelectedModel(e.target.value)}
className="w-full appearance-none bg-bg-card/80 border border-primary/30 rounded-lg pl-3 pr-8 py-2 text-sm text-text-primary focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all cursor-pointer"
>
<option value="">
{modelsQuery.data ? `Default (${modelsQuery.data.default})` : 'Default'}
</option>
{modelsQuery.data?.models.map((m) => (
<option key={m.id} value={m.id}>
{m.name} ({m.provider})
</option>
))}
</select>
<ChevronDown className="absolute right-2 top-1/2 -translate-y-1/2 text-text-secondary pointer-events-none" size={16} />
</div>
</div>
<button
type="submit"
disabled={mutation.isPending || !companiesInput.trim()}
@@ -123,12 +160,29 @@ export function Batch() {
{mutation.error instanceof Error ? mutation.error.message : 'An unexpected error occurred.'}
{' '}Check your connection and try again.
</p>
<button
onClick={() => mutation.reset()}
className="ml-7 mt-2 text-sm text-primary hover:text-primary-dark underline"
>
Dismiss
</button>
<div className="ml-7 mt-2 flex items-center gap-3">
<button
onClick={() => {
const companies = companiesInput
.split(/[,\n]/)
.map((c) => c.trim())
.filter((c) => c.length > 0);
if (companies.length > 0) {
mutation.mutate({ companies, workers: maxWorkers });
}
}}
className="text-sm text-primary hover:text-primary-dark underline flex items-center gap-1"
>
<RefreshCw size={14} />
Retry
</button>
<button
onClick={() => mutation.reset()}
className="text-sm text-text-secondary hover:text-text-primary underline"
>
Dismiss
</button>
</div>
</div>
)}
@@ -230,6 +284,123 @@ export function Batch() {
</div>
</div>
)}
{/* Job History */}
<div>
<h3 className="text-lg font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-4">
Job History
</h3>
{/* Loading skeleton */}
{jobsQuery.isLoading && (
<div className="space-y-3">
{[...Array(3)].map((_, i) => (
<div
key={i}
className="bg-bg-card/60 border border-primary/15 rounded-xl p-4 animate-pulse"
>
<div className="flex items-center justify-between">
<div className="flex items-center gap-3">
<div className="h-5 w-5 rounded-full bg-primary/20" />
<div className="h-4 w-32 rounded bg-primary/20" />
<div className="h-4 w-20 rounded bg-primary/10" />
</div>
<div className="h-6 w-20 rounded-full bg-primary/15" />
</div>
<div className="mt-3 flex gap-4">
<div className="h-3 w-24 rounded bg-primary/10" />
<div className="h-3 w-16 rounded bg-primary/10" />
</div>
</div>
))}
</div>
)}
{/* Job history error */}
{jobsQuery.isError && (
<div className="bg-error/10 border border-error/20 rounded-xl px-4 py-3">
<div className="flex items-center gap-2 text-error">
<AlertCircle size={18} />
<span className="font-semibold">Failed to load job history</span>
</div>
<p className="text-text-secondary text-sm mt-1 ml-7">
{jobsQuery.error instanceof Error ? jobsQuery.error.message : 'Could not retrieve past jobs.'}
</p>
<button
onClick={() => jobsQuery.refetch()}
className="ml-7 mt-2 text-sm text-primary hover:text-primary-dark underline flex items-center gap-1"
>
<RefreshCw size={14} />
Retry
</button>
</div>
)}
{/* Empty state */}
{jobsQuery.isSuccess && jobsQuery.data.length === 0 && !result && (
<div className="bg-bg-card/60 border border-primary/15 border-dashed rounded-xl p-8 text-center">
<Inbox className="mx-auto text-text-secondary/40 mb-3" size={40} />
<p className="text-text-secondary font-medium">No batch jobs yet</p>
<p className="text-text-secondary/70 text-sm mt-1">
Submit a batch analysis above to get started. Your job history will appear here.
</p>
</div>
)}
{/* Job list */}
{jobsQuery.isSuccess && jobsQuery.data.length > 0 && (
<div className="space-y-3">
{jobsQuery.data.map((job) => (
<div
key={job.job_id}
className="bg-bg-card/60 border border-primary/15 rounded-xl p-4"
>
<div className="flex items-center justify-between">
<div className="flex items-center gap-3">
{job.status === 'completed' && <CheckCircle className="text-success" size={18} />}
{job.status === 'failed' && <AlertCircle className="text-error" size={18} />}
{(job.status === 'pending' || job.status === 'running') && (
<div className="animate-spin rounded-full h-[18px] w-[18px] border-t-2 border-b-2 border-secondary" />
)}
<span className="font-mono text-sm text-text-primary">{job.job_id.slice(0, 8)}</span>
<span className="text-text-secondary text-sm">
{job.total_companies} {job.total_companies === 1 ? 'company' : 'companies'}
</span>
</div>
<span
className={`text-xs font-semibold px-2.5 py-1 rounded-full ${
job.status === 'completed'
? 'bg-success/15 text-success'
: job.status === 'failed'
? 'bg-error/15 text-error'
: 'bg-secondary/15 text-secondary'
}`}
>
{job.status}
</span>
</div>
{(job.status === 'running' || job.status === 'pending') && job.total_companies > 0 && (
<div className="mt-3">
<div className="flex items-center justify-between text-xs text-text-secondary mb-1">
<span>Progress</span>
<span>{job.completed_companies}/{job.total_companies}</span>
</div>
<div className="h-1.5 bg-bg-dark rounded-full overflow-hidden">
<div
className="h-full bg-gradient-to-r from-primary to-secondary rounded-full transition-all duration-300"
style={{ width: `${(job.completed_companies / job.total_companies) * 100}%` }}
/>
</div>
</div>
)}
{job.status === 'failed' && job.error && (
<p className="mt-2 text-sm text-error/80">{job.error}</p>
)}
</div>
))}
</div>
)}
</div>
</div>
);
}
+161
View File
@@ -0,0 +1,161 @@
import { useState } from 'react';
import { useSearchParams } from 'react-router-dom';
import { useQuery } from '@tanstack/react-query';
import { analysisApi } from '../api/client';
import { GitCompareArrows, AlertCircle, FileText, Clock } from 'lucide-react';
import type { CompanyAnalysis } from '../types';
function CompanyPanel({ data, isLoading, isError }: { data?: CompanyAnalysis; isLoading: boolean; isError: boolean }) {
if (isLoading) {
return (
<div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6 animate-pulse">
<div className="h-6 w-32 bg-primary/20 rounded mb-4" />
<div className="space-y-3">
<div className="h-4 bg-primary/10 rounded w-full" />
<div className="h-4 bg-primary/10 rounded w-3/4" />
<div className="h-4 bg-primary/10 rounded w-5/6" />
</div>
</div>
);
}
if (isError) {
return (
<div className="bg-error/10 border border-error/20 rounded-2xl p-6">
<div className="flex items-center gap-2 text-error">
<AlertCircle size={18} />
<span>Failed to load analysis. Check the company name and try again.</span>
</div>
</div>
);
}
if (!data) return null;
return (
<div className="bg-bg-card/60 border border-primary/15 rounded-2xl p-6 space-y-4">
<h3 className="text-lg font-bold text-text-primary border-b-2 border-primary/30 pb-2">
{data.company_name.toUpperCase()}
</h3>
<div className="grid grid-cols-2 gap-3">
<div className="bg-primary/10 rounded-lg p-3 text-center">
<FileText className="mx-auto mb-1 text-primary" size={18} />
<div className="text-xl font-bold text-text-primary">{data.patent_count}</div>
<div className="text-xs text-text-secondary uppercase">Patents</div>
</div>
<div className="bg-primary/10 rounded-lg p-3 text-center">
<Clock className="mx-auto mb-1 text-primary" size={18} />
<div className="text-sm font-medium text-text-primary">
{new Date(data.timestamp).toLocaleDateString()}
</div>
<div className="text-xs text-text-secondary uppercase">Analyzed</div>
</div>
</div>
{data.success && data.analysis ? (
<div className="text-text-primary whitespace-pre-wrap leading-relaxed text-sm">
{data.analysis}
</div>
) : (
<div className="text-error text-sm">{data.error || 'Analysis not available'}</div>
)}
</div>
);
}
export function Compare() {
const [searchParams, setSearchParams] = useSearchParams();
const [companyA, setCompanyA] = useState(searchParams.get('a') || '');
const [companyB, setCompanyB] = useState(searchParams.get('b') || '');
const queryA = searchParams.get('a') || '';
const queryB = searchParams.get('b') || '';
const resultA = useQuery({
queryKey: ['analyze', queryA],
queryFn: () => analysisApi.analyzeCompany(queryA),
enabled: !!queryA,
});
const resultB = useQuery({
queryKey: ['analyze', queryB],
queryFn: () => analysisApi.analyzeCompany(queryB),
enabled: !!queryB,
});
const handleCompare = (e: React.FormEvent) => {
e.preventDefault();
const a = companyA.trim();
const b = companyB.trim();
if (a && b) {
setSearchParams({ a, b });
}
};
return (
<div className="space-y-6">
{/* Header */}
<div>
<h2 className="text-xl font-semibold text-text-primary border-b-2 border-primary/30 pb-2 mb-2">
Portfolio Comparison
</h2>
<p className="text-text-secondary">
Compare patent portfolios of two companies side by side.
</p>
</div>
{/* Input Form */}
<form onSubmit={handleCompare} className="flex flex-col sm:flex-row gap-3 items-end">
<div className="flex-1">
<label className="block text-sm font-medium text-text-secondary mb-1">Company A</label>
<input
type="text"
value={companyA}
onChange={(e) => setCompanyA(e.target.value)}
placeholder="e.g. nvidia"
className="w-full bg-bg-card/80 border border-primary/30 rounded-xl px-4 py-2.5 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
/>
</div>
<div className="flex-1">
<label className="block text-sm font-medium text-text-secondary mb-1">Company B</label>
<input
type="text"
value={companyB}
onChange={(e) => setCompanyB(e.target.value)}
placeholder="e.g. intel"
className="w-full bg-bg-card/80 border border-primary/30 rounded-xl px-4 py-2.5 text-text-primary placeholder-text-secondary/50 focus:outline-none focus:border-primary focus:ring-2 focus:ring-primary/20 transition-all"
/>
</div>
<button
type="submit"
disabled={!companyA.trim() || !companyB.trim() || resultA.isLoading || resultB.isLoading}
className="bg-gradient-to-r from-primary to-primary-dark text-white font-semibold py-2.5 px-6 rounded-xl hover:shadow-lg hover:shadow-primary/30 transition-all disabled:opacity-50 disabled:cursor-not-allowed flex items-center gap-2"
>
<GitCompareArrows size={18} />
Compare
</button>
</form>
{/* Comparison Panels */}
{(queryA || queryB) && (
<div className="grid grid-cols-1 lg:grid-cols-2 gap-6">
{queryA && (
<CompanyPanel
data={resultA.data}
isLoading={resultA.isLoading}
isError={resultA.isError}
/>
)}
{queryB && (
<CompanyPanel
data={resultB.data}
isLoading={resultB.isLoading}
isError={resultB.isError}
/>
)}
</div>
)}
</div>
);
}
+28 -42
View File
@@ -1,46 +1,32 @@
export interface User {
id: number;
email: string;
role: 'admin' | 'user';
created_at: string;
}
/**
* Application types derived from the auto-generated OpenAPI schema.
*
* Run `npm run generate:local` (or `npm run generate` with the API running)
* to regenerate `src/api/schema.d.ts` from the backend OpenAPI spec.
*
* These aliases keep the rest of the codebase stable while the source of
* truth lives in the generated file.
*/
export interface TokenResponse {
access_token: string;
refresh_token: string;
token_type: string;
}
import type { components } from '../api/schema';
export interface CompanyAnalysis {
company_name: string;
analysis: string;
patent_count: number;
success: boolean;
error: string | null;
timestamp: string;
}
export interface BatchAnalysisResult {
results: CompanyAnalysis[];
total_companies: number;
successful: number;
failed: number;
timestamp: string;
}
export interface JobStatus {
job_id: string;
status: 'pending' | 'running' | 'completed' | 'failed';
progress: number;
total_companies: number;
completed_companies: number;
result: BatchAnalysisResult | null;
error: string | null;
}
export interface Analytics {
total_messages: number;
// Re-export schema types under the names the rest of the app expects.
export type User = components['schemas']['UserResponse'];
export type TokenResponse = components['schemas']['TokenResponse'];
export type CompanyAnalysis = components['schemas']['CompanyAnalysisResponse'];
export type BatchAnalysisResult = components['schemas']['BatchAnalysisResponse'];
export type JobStatus = components['schemas']['JobStatus'];
export type Analytics = Omit<components['schemas']['AnalyticsResponse'], 'by_company' | 'by_type'> & {
by_company: Array<{ company_name: string; count: number }>;
by_type: Array<{ analysis_type: string; count: number }>;
period_days: number;
}
};
// Additional generated types that may be useful elsewhere.
export type RegisterRequest = components['schemas']['RegisterRequest'];
export type LoginRequest = components['schemas']['LoginRequest'];
export type RefreshRequest = components['schemas']['RefreshRequest'];
export type UpdateRoleRequest = components['schemas']['UpdateRoleRequest'];
export type HealthResponse = components['schemas']['HealthResponse'];
export type BatchAnalysisRequest = components['schemas']['BatchAnalysisRequest'];
export type ValidationError = components['schemas']['ValidationError'];
export type HTTPValidationError = components['schemas']['HTTPValidationError'];
+1
View File
@@ -17,3 +17,4 @@ PyJWT
slowapi
apscheduler
boto3
reportlab
+44
View File
@@ -182,3 +182,47 @@ class TestJobEndpoints:
"""Test listing jobs with status filter."""
response = client.get("/jobs?status=completed")
assert response.status_code == 200
class TestModelValidation:
"""Test that unsupported model identifiers are rejected."""
def test_analyze_rejects_unsupported_model(self, client, mock_analyzer):
"""GET /analyze/{company} with unsupported model returns 400."""
response = client.get("/analyze/nvidia?model=fake/nonexistent-model")
assert response.status_code == 400
assert "Unsupported model" in response.json()["detail"]
def test_analyze_accepts_supported_model(self, client, mock_analyzer):
"""GET /analyze/{company} with a supported model succeeds."""
mock_result = CompanyAnalysisResult(
company_name="nvidia",
analysis="test",
patent_count=1,
success=True,
timestamp=datetime.now(),
model="anthropic/claude-3.5-sonnet",
)
mock_analyzer._analyze_company_safe.return_value = mock_result
response = client.get("/analyze/nvidia?model=anthropic/claude-3.5-sonnet")
assert response.status_code == 200
def test_batch_rejects_unsupported_model(self, client, mock_analyzer):
"""POST /analyze/batch with unsupported model returns 400."""
response = client.post(
"/analyze/batch",
json={"companies": ["nvidia"], "model": "fake/nonexistent-model"},
)
assert response.status_code == 400
assert "Unsupported model" in response.json()["detail"]
def test_list_models_returns_supported(self, client):
"""GET /models returns the allow-list."""
response = client.get("/models")
assert response.status_code == 200
data = response.json()
assert "models" in data
assert "default" in data
assert len(data["models"]) > 0
assert all("id" in m and "name" in m and "provider" in m for m in data["models"])