Create app.py

app.py

@@ -0,0 +1,336 @@
+import streamlit as st
+import requests
+import json
+import plotly.graph_objects as go
+import plotly.express as px
+from typing import Dict, List, Any
+import time
+import pandas as pd
+
+# Page configuration
+st.set_page_config(
+    page_title="Market Research & Analysis Platform",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom styling (updated for dark mode)
+st.markdown("""
+    <style>
+    .main {
+        background-color: #121212;
+        color: #ffffff;
+    }
+    .insight-card {
+        background-color: #1e1e1e;
+        padding: 1.5rem;
+        border-radius: 8px;
+        box-shadow: 0 2px 4px rgba(255,255,255,0.1);
+        margin: 1rem 0;
+    }
+    .metric-card {
+        background-color: #1e1e1e;
+        padding: 1rem;
+        border-radius: 4px;
+        margin: 0.5rem 0;
+    }
+    .source-card {
+        background-color: #2e2e2e;
+        padding: 0.5rem;
+        border-radius: 4px;
+        font-size: 0.9rem;
+        margin-top: 0.5rem;
+        color: #ffffff;
+    }
+    .highlight-text {
+        color: #4c6ef5;
+        font-weight: bold;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+def query_perplexity(query: str, context: Dict) -> Dict:
+    url = "https://api.perplexity.ai/chat/completions"
+    headers = {
+        "Authorization": f"Bearer {st.secrets['PERPLEXITY_API_KEY']}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "model": "llama-3.1-sonar-small-128k-online",
+        "messages": [
+            {"role": "system", "content": get_system_prompt(context)},
+            {"role": "user", "content": query}
+        ],
+        "temperature": 0.2,
+        "max_tokens": 4096,
+        "top_p": 0.9,
+        "search_domain_filter": ["perplexity.ai"],
+        "return_images": False,
+        "return_related_questions": False,
+        "search_recency_filter": context.get('timeframe', 'month')
+    }
+    try:
+        response = requests.post(url, headers=headers, json=payload)
+        if response.status_code == 200:
+            return {
+                "status": "success",
+                "data": response.json(),
+                "citations": response.json().get("citations", [])
+            }
+        else:
+            return {"status": "error", "message": f"API Error: {response.status_code}"}
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+def get_system_prompt(context: Dict) -> str:
+    # Adjust the prompt if Executive style and Comprehensive depth are selected.
+    if context.get("style", "").lower() == "executive" and context.get("depth", "").lower() == "comprehensive":
+        return f"""You are an expert market research analyst focusing on {context['focus_area']}.
+Provide a high-level executive summary with key insights and concrete metrics.
+Analysis style: Executive
+Depth: Comprehensive
+Timeline: Past {context['timeframe']}
+
+Format your response with clear sections:
+1. Executive Summary
+2. Key Metrics
+3. Market Position
+4. Growth Analysis
+5. Strategic Recommendations
+
+Include specific numbers, percentages, and actionable insights."""
+    else:
+        return f"""You are an expert market research analyst focusing on {context['focus_area']}.
+Provide detailed analysis with concrete metrics and specific insights.
+Analysis style: {context['style']}
+Depth: {context['depth']}
+Timeline: Past {context['timeframe']}
+
+Format your response with clear sections:
+1. Key Metrics
+2. Market Position
+3. Growth Analysis
+4. Competitive Insights
+5. Strategic Recommendations
+
+Include specific numbers, percentages, and actionable insights."""
+
+def parse_perplexity_response(response: Dict) -> Dict:
+    try:
+        content = response['data']['choices'][0]['message']['content']
+        citations = response.get('citations', [])
+        sections = {
+            'key_metrics': [],
+            'market_position': [],
+            'growth_analysis': [],
+            'competitive_insights': [],
+            'recommendations': []
+        }
+        current_section = None
+        for line in content.split('\n'):
+            line = line.strip()
+            if not line:
+                continue
+            lower_line = line.lower()
+            if 'key metric' in lower_line:
+                current_section = 'key_metrics'
+            elif 'market position' in lower_line:
+                current_section = 'market_position'
+            elif 'growth' in lower_line:
+                current_section = 'growth_analysis'
+            elif 'competiti' in lower_line:
+                current_section = 'competitive_insights'
+            elif 'recommend' in lower_line:
+                current_section = 'recommendations'
+            elif current_section and line.startswith(('-', '•', '*')):
+                sections[current_section].append(line.lstrip('-•* '))
+        return {"status": "success", "sections": sections, "citations": citations}
+    except Exception as e:
+        return {"status": "error", "message": str(e)}
+
+def extract_metrics(content: Dict) -> Dict:
+    metrics = {
+        'market_share': [],
+        'growth_rate': [],
+        'competitive_position': [],
+        'innovation_score': []
+    }
+    try:
+        for section in content['sections'].values():
+            for line in section:
+                if '%' in line or any(char.isdigit() for char in line):
+                    if 'market share' in line.lower():
+                        metrics['market_share'].append(extract_number(line))
+                    elif 'growth' in line.lower():
+                        metrics['growth_rate'].append(extract_number(line))
+                    elif 'position' in line.lower():
+                        metrics['competitive_position'].append(extract_number(line))
+                    elif 'innovation' in line.lower():
+                        metrics['innovation_score'].append(extract_number(line))
+        return metrics
+    except Exception as e:
+        st.error(f"Error extracting metrics: {str(e)}")
+        return metrics
+
+def extract_number(text: str) -> float:
+    import re
+    numbers = re.findall(r'[-+]?\d*\.?\d+%?', text)
+    if numbers:
+        number = numbers[0]
+        return float(number.replace('%', '')) if '%' in number else float(number)
+    return 0.0
+
+def create_visualizations(metrics: Dict, context: Dict) -> Dict:
+    charts = {}
+    if metrics['market_share'] and metrics['competitive_position']:
+        fig = go.Figure()
+        categories = ['Market Share', 'Growth Rate', 'Competitive Position', 'Innovation Score']
+        values = [
+            metrics['market_share'][0] if metrics['market_share'] else 0,
+            metrics['growth_rate'][0] if metrics['growth_rate'] else 0,
+            metrics['competitive_position'][0] if metrics['competitive_position'] else 0,
+            metrics['innovation_score'][0] if metrics['innovation_score'] else 0
+        ]
+        fig.add_trace(go.Scatterpolar(
+            r=values,
+            theta=categories,
+            fill='toself',
+            name=context['company_name']
+        ))
+        fig.update_layout(
+            polar=dict(radialaxis=dict(visible=True, range=[0, 100])),
+            showlegend=False,
+            title=f"Market Position Analysis - {context['company_name']}"
+        )
+        charts['market_position'] = fig
+    if metrics['growth_rate']:
+        fig = go.Figure()
+        fig.add_trace(go.Scatter(
+            y=metrics['growth_rate'],
+            mode='lines+markers',
+            name='Growth Rate'
+        ))
+        fig.update_layout(
+            title=f"Growth Trend Analysis - {context['company_name']}",
+            yaxis_title='Growth Rate (%)',
+            showlegend=True
+        )
+        charts['growth_trend'] = fig
+    return charts
+
+def format_insights(content: Dict) -> str:
+    """Format detailed analysis with sub-titles for each section"""
+    formatted = ""
+    section_titles = {
+        'key_metrics': '📊 Key Metrics',
+        'market_position': '🎯 Market Position',
+        'growth_analysis': '📈 Growth Analysis',
+        'competitive_insights': '🔍 Competitive Insights',
+        'recommendations': '💡 Strategic Recommendations'
+    }
+    for section, title in section_titles.items():
+        if content['sections'].get(section):
+            formatted += f"\n### {title}\n\n"
+            for idx, point in enumerate(content['sections'][section], start=1):
+                formatted += f"- **{idx}.** {point}\n"
+    return formatted
+
+def main():
+    st.title("Market Research & Analysis Platform")
+    st.markdown("Real-time market insights with data-driven analysis")
+
+    with st.sidebar:
+        st.header("Analysis Parameters")
+        company_name = st.text_input("Company/Product Name", placeholder="e.g., Tesla, OpenAI, Snowflake")
+        industry = st.selectbox("Industry", ["Technology", "AI/ML", "SaaS", "Fintech", "E-commerce", "Healthcare", "Energy", "Other"])
+        st.markdown("### Analysis Configuration")
+        focus_area = st.multiselect("Focus Areas", ["Market Position", "Growth Trajectory", "Technology Stack", "Competitive Analysis", "Innovation Trends", "Investment Outlook"], default=["Market Position", "Growth Trajectory"])
+        timeframe = st.select_slider("Analysis Timeframe", options=["week", "month", "quarter", "year"], value="month")
+        depth = st.select_slider("Analysis Depth", options=["Brief", "Detailed", "Comprehensive"], value="Detailed")
+        style = st.selectbox("Report Style", ["Technical", "Business", "Executive"], index=1)
+        competitors = st.text_input("Key Competitors (optional)", placeholder="Comma-separated names")
+
+    if st.button("Generate Analysis", type="primary"):
+        if not company_name:
+            st.warning("Please enter a company name.")
+            return
+
+        analysis_context = {
+            "company_name": company_name,
+            "industry": industry,
+            "focus_area": ", ".join(focus_area),
+            "timeframe": timeframe,
+            "depth": depth,
+            "style": style,
+            "competitors": competitors
+        }
+        
+        with st.spinner("Generating market analysis..."):
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+            try:
+                status_text.text("Gathering market intelligence...")
+                progress_bar.progress(20)
+                research_response = query_perplexity(
+                    f"Provide a detailed market analysis for {company_name} in the {industry} industry, focusing on {', '.join(focus_area)}",
+                    analysis_context
+                )
+                if research_response["status"] != "success":
+                    st.error("Failed to gather market intelligence.")
+                    return
+
+                status_text.text("Processing insights...")
+                progress_bar.progress(40)
+                parsed_content = parse_perplexity_response(research_response)
+                if parsed_content["status"] != "success":
+                    st.error("Failed to process insights.")
+                    return
+
+                status_text.text("Generating visualizations...")
+                progress_bar.progress(60)
+                metrics = extract_metrics(parsed_content)
+                charts = create_visualizations(metrics, analysis_context)
+
+                status_text.text("Preparing analysis report...")
+                progress_bar.progress(80)
+                tabs = st.tabs(["Overview", "Detailed Analysis", "Visualizations"])
+                
+                with tabs[0]:
+                    st.markdown("## Executive Summary", unsafe_allow_html=True)
+                    if metrics:
+                        cols = st.columns(len(metrics))
+                        for col, (metric, values) in zip(cols, metrics.items()):
+                            if values:
+                                col.metric(metric.replace('_', ' ').title(), f"{values[0]:.1f}%")
+                    if parsed_content["citations"]:
+                        st.markdown("### Sources")
+                        for citation in parsed_content["citations"]:
+                            st.markdown(f'<div class="source-card">{citation}</div>', unsafe_allow_html=True)
+                
+                with tabs[1]:
+                    st.markdown(format_insights(parsed_content), unsafe_allow_html=True)
+                
+                with tabs[2]:
+                    for chart_name, fig in charts.items():
+                        st.plotly_chart(fig, use_container_width=True)
+                
+                progress_bar.progress(100)
+                status_text.text("Analysis complete!")
+                
+                st.download_button(
+                    "Download Analysis",
+                    data=json.dumps({
+                        "context": analysis_context,
+                        "insights": parsed_content["sections"],
+                        "metrics": metrics,
+                        "citations": parsed_content["citations"]
+                    }, indent=2),
+                    file_name=f"market_analysis_{company_name}.json",
+                    mime="application/json"
+                )
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")
+                return
+
+if __name__ == "__main__":
+    main()