diff --git a/dashboard.py b/dashboard.py deleted file mode 100644 index af9f001..0000000 --- a/dashboard.py +++ /dev/null @@ -1,778 +0,0 @@ -"""SPARC Visualization Dashboard. - -A Streamlit-based dashboard for visualizing patent analysis results. -Run with: streamlit run dashboard.py -""" - -import streamlit as st -import plotly.express as px -import plotly.graph_objects as go -import pandas as pd -from datetime import datetime, timedelta - -from SPARC.analyzer import CompanyAnalyzer -from SPARC.database import DatabaseClient -from SPARC import config - - -st.set_page_config( - page_title="SPARC Dashboard", - page_icon="⚡", - layout="wide", - initial_sidebar_state="collapsed", -) - -# Modern CSS styling -st.markdown(""" - -""", unsafe_allow_html=True) - - -@st.cache_resource -def get_analyzer(): - """Get or create the CompanyAnalyzer instance.""" - return CompanyAnalyzer() - - -@st.cache_resource -def get_db_client(): - """Get database client if available.""" - if config.use_database: - try: - client = DatabaseClient() - client.connect() - return client - except Exception: - return None - return None - - -def render_header(): - """Render the modern dashboard header.""" - st.markdown(""" -
-
-

⚡ SPARC

- Semiconductor Patent Analytics -
-
- """, unsafe_allow_html=True) - - -def render_navigation(): - """Render horizontal tab navigation at the top.""" - tabs = st.tabs(["🔍 Company Analysis", "đŸ“Ļ Batch Analysis", "📊 Analytics", "ℹī¸ About"]) - return tabs - - -def render_company_analysis(): - """Render single company analysis page.""" - st.markdown('

Single Company Analysis

', unsafe_allow_html=True) - st.markdown("Analyze a company's patent portfolio using AI-powered insights.") - - st.markdown("") - - # Search card - with st.container(): - col1, col2 = st.columns([3, 1]) - - with col1: - company_name = st.text_input( - "Company Name", - placeholder="Enter company name (e.g., nvidia, intel, amd)", - help="Enter the company name to analyze their patent portfolio", - label_visibility="collapsed", - ) - - with col2: - analyze_btn = st.button("🔍 Analyze", type="primary", use_container_width=True) - - if analyze_btn and company_name: - with st.spinner(f"Analyzing {company_name}..."): - analyzer = get_analyzer() - result = analyzer._analyze_company_safe(company_name) - - if result.success: - st.success(f"✓ Analysis complete for {company_name.upper()}") - - st.markdown("") - - # Metrics row with custom styling - col1, col2, col3 = st.columns(3) - with col1: - st.metric("Patents Found", result.patent_count) - with col2: - st.metric("Analysis Status", "Complete") - with col3: - st.metric("Timestamp", result.timestamp.strftime("%H:%M:%S")) - - st.markdown("") - - # Analysis content in a styled container - st.markdown('

AI Analysis Results

', unsafe_allow_html=True) - with st.container(): - st.markdown(result.analysis) - - else: - st.error(f"Analysis failed: {result.error}") - - elif not company_name and analyze_btn: - st.warning("Please enter a company name to analyze.") - - -def render_batch_analysis(): - """Render batch analysis page.""" - st.markdown('

Batch Company Analysis

', unsafe_allow_html=True) - st.markdown("Analyze multiple companies simultaneously for comparative insights.") - - st.markdown("") - - # Input section - col1, col2 = st.columns([2, 1]) - - with col1: - companies_input = st.text_area( - "Company Names", - placeholder="Enter company names (one per line or comma-separated):\nnvidia\namd\nintel\nqualcomm", - height=150, - label_visibility="collapsed", - ) - - with col2: - st.markdown("**Configuration**") - max_workers = st.slider("Concurrent Workers", 1, 5, 3, help="Number of parallel analysis threads") - st.markdown("") - analyze_btn = st.button( - "🚀 Run Batch Analysis", type="primary", use_container_width=True - ) - - if analyze_btn and companies_input: - # Parse company names - companies = [ - c.strip() - for c in companies_input.replace(",", "\n").split("\n") - if c.strip() - ] - - if not companies: - st.warning("Please enter at least one company name") - return - - st.info(f"🔄 Starting analysis of {len(companies)} companies...") - - # Progress tracking - progress_bar = st.progress(0) - status_text = st.empty() - - analyzer = get_analyzer() - - def update_progress(company: str, completed: int, total: int): - progress = completed / total - progress_bar.progress(progress) - status_text.text(f"Analyzing {company}... ({completed}/{total})") - - result = analyzer.analyze_companies( - companies=companies, - max_workers=max_workers, - progress_callback=update_progress, - ) - - progress_bar.progress(1.0) - status_text.text("✓ Analysis complete!") - - st.markdown("") - - # Summary metrics - st.markdown('

Results Summary

', unsafe_allow_html=True) - col1, col2, col3, col4 = st.columns(4) - with col1: - st.metric("Total Companies", result.total_companies) - with col2: - st.metric("Successful", result.successful) - with col3: - st.metric("Failed", result.failed) - with col4: - success_rate = ( - (result.successful / result.total_companies * 100) - if result.total_companies > 0 - else 0 - ) - st.metric("Success Rate", f"{success_rate:.1f}%") - - # Results chart - if result.results: - df = pd.DataFrame( - [ - { - "Company": r.company_name.upper(), - "Patents": r.patent_count, - "Status": "Success" if r.success else "Failed", - } - for r in result.results - ] - ) - - fig = px.bar( - df, - x="Company", - y="Patents", - color="Status", - color_discrete_map={"Success": "#10b981", "Failed": "#ef4444"}, - title="", - ) - fig.update_layout( - plot_bgcolor="rgba(0,0,0,0)", - paper_bgcolor="rgba(0,0,0,0)", - font_color="#94a3b8", - legend=dict( - orientation="h", - yanchor="bottom", - y=1.02, - xanchor="right", - x=1 - ), - xaxis=dict(showgrid=False), - yaxis=dict(showgrid=True, gridcolor="rgba(99, 102, 241, 0.1)"), - ) - st.plotly_chart(fig, use_container_width=True) - - st.markdown("") - - # Individual results - st.markdown('

Detailed Results

', unsafe_allow_html=True) - for r in result.results: - status_icon = "✓" if r.success else "✗" - status_class = "status-success" if r.success else "status-error" - with st.expander( - f"{status_icon} {r.company_name.upper()} — {r.patent_count} patents" - ): - if r.success: - st.markdown(r.analysis) - else: - st.error(r.error) - - -def render_analytics(): - """Render analytics page with database insights.""" - st.markdown('

Analytics Dashboard

', unsafe_allow_html=True) - st.markdown("Track historical analysis data and view insights.") - - db_client = get_db_client() - - if not db_client: - st.markdown("") - st.markdown(""" -
- ⚠ī¸ Database Not Connected
- Set USE_DATABASE=true in your .env file to enable analytics tracking. -
- """, unsafe_allow_html=True) - st.info("Analytics features require storing analysis results in PostgreSQL for historical tracking.") - return - - st.markdown("") - - # Time range selector - col1, col2, col3 = st.columns([1, 2, 1]) - with col1: - days = st.selectbox("Time Range", [7, 14, 30, 90], index=0, format_func=lambda x: f"Last {x} days") - - try: - analytics = db_client.get_analytics(days=days) - - if not analytics: - st.info("No analytics data available yet. Run some analyses first!") - return - - st.markdown("") - - # Summary metrics - col1, col2, col3 = st.columns(3) - - with col1: - total = analytics.get("total_messages", 0) - st.metric("Total Analyses", total) - - with col2: - companies = len(analytics.get("by_company", {})) - st.metric("Companies Analyzed", companies) - - with col3: - types = len(analytics.get("by_type", {})) - st.metric("Analysis Types", types) - - st.markdown("") - - # Charts - col1, col2 = st.columns(2) - - with col1: - by_company = analytics.get("by_company", {}) - if by_company: - df = pd.DataFrame( - [{"Company": k.upper(), "Count": v} for k, v in by_company.items()] - ) - fig = px.pie( - df, values="Count", names="Company", title="Distribution by Company", - hole=0.4, - color_discrete_sequence=px.colors.sequential.Purp_r, - ) - fig.update_layout( - plot_bgcolor="rgba(0,0,0,0)", - paper_bgcolor="rgba(0,0,0,0)", - font_color="#94a3b8", - ) - st.plotly_chart(fig, use_container_width=True) - - with col2: - by_type = analytics.get("by_type", {}) - if by_type: - df = pd.DataFrame( - [{"Type": k, "Count": v} for k, v in by_type.items()] - ) - fig = px.bar(df, x="Type", y="Count", title="Analysis Types", - color_discrete_sequence=["#6366f1"]) - fig.update_layout( - plot_bgcolor="rgba(0,0,0,0)", - paper_bgcolor="rgba(0,0,0,0)", - font_color="#94a3b8", - xaxis=dict(showgrid=False), - yaxis=dict(showgrid=True, gridcolor="rgba(99, 102, 241, 0.1)"), - ) - st.plotly_chart(fig, use_container_width=True) - - st.markdown("") - - # Recent messages - st.markdown('

Recent Analyses

', unsafe_allow_html=True) - messages = db_client.get_messages(limit=10) - - if messages: - for msg in messages: - with st.expander( - f"📄 {msg.get('company_name', 'Unknown').upper()} — {msg.get('analysis_type', 'N/A')} ({msg.get('timestamp', 'N/A')})" - ): - st.markdown(f"**Model:** `{msg.get('model', 'N/A')}`") - if msg.get("response"): - st.markdown(msg["response"][:500] + "...") - - except Exception as e: - st.error(f"Error fetching analytics: {e}") - - -def render_about(): - """Render about page.""" - st.markdown('

About SPARC

', unsafe_allow_html=True) - - col1, col2 = st.columns([2, 1]) - - with col1: - st.markdown(""" - **SPARC** (Semiconductor Patent & Analytics Report Core) is an AI-powered patent analysis - platform that evaluates company performance by analyzing their patent portfolios - with cutting-edge language models. - """) - - st.markdown("") - st.markdown("**Key Features**") - - features = [ - ("🔍", "Patent Retrieval", "Automated collection via SerpAPI's Google Patents"), - ("📄", "Intelligent Parsing", "Extracts key sections from patent documents"), - ("🤖", "AI Analysis", "Deep analysis powered by Claude 3.5 Sonnet"), - ("⚡", "Batch Processing", "Analyze multiple companies concurrently"), - ("🌐", "REST API", "FastAPI web service for seamless integration"), - ("📊", "Analytics", "Track and visualize historical analysis data"), - ] - - for icon, title, desc in features: - st.markdown(f""" -
- {icon} -
- {title}
- {desc} -
-
- """, unsafe_allow_html=True) - - with col2: - st.markdown("**Technology Stack**") - st.markdown(""" -
-
-
Backend
Python, FastAPI
-
AI Model
Claude 3.5 Sonnet
-
Database
PostgreSQL
-
Dashboard
Streamlit, Plotly
-
Data Source
SerpAPI Patents
-
-
- """, unsafe_allow_html=True) - - st.markdown("") - st.markdown("**API Endpoints**") - st.code("http://localhost:8000/docs", language=None) - st.code("http://localhost:8000/health", language=None) - - st.markdown("") - st.markdown("") - - # System status - st.markdown('

System Status

', unsafe_allow_html=True) - - col1, col2, col3 = st.columns(3) - - with col1: - db_client = get_db_client() - if db_client: - st.markdown(""" -
-
●
-
Database
-
Connected
-
- """, unsafe_allow_html=True) - else: - st.markdown(""" -
-
●
-
Database
-
Not Configured
-
- """, unsafe_allow_html=True) - - with col2: - analyzer = get_analyzer() - if analyzer: - st.markdown(""" -
-
●
-
Analyzer
-
Ready
-
- """, unsafe_allow_html=True) - else: - st.markdown(""" -
-
●
-
Analyzer
-
Not Initialized
-
- """, unsafe_allow_html=True) - - with col3: - st.markdown(""" -
-
●
-
Dashboard
-
Online
-
- """, unsafe_allow_html=True) - - -def main(): - """Main dashboard entry point.""" - render_header() - tabs = render_navigation() - - with tabs[0]: - render_company_analysis() - with tabs[1]: - render_batch_analysis() - with tabs[2]: - render_analytics() - with tabs[3]: - render_about() - - -if __name__ == "__main__": - main()