Update utils.py

utils.py

@@ -1,84 +1,79 @@
-from crewai import Agent, Crew, Process, Task
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
+from langchain_openai import ChatOpenAI
+import streamlit as st
+import json
+import re
 
-def create_research_crew(topic: str, questions: list):
-    search_tool = SerperDevTool()
-    scrape_tool = ScrapeWebsiteTool()
+def update_progress(container, percentage, message=""):
+    if container:
+        progress_bar = container.progress(percentage / 100)
+        container.write(message)
+
+def extract_section(text, section_name):
+    try:
+        pattern = f"{section_name}:?\s*(.*?)(?=\n\n|$)"
+        match = re.search(pattern, text, re.DOTALL | re.IGNORECASE)
+        return match.group(1).strip() if match else ""
+    except:
+        return ""
+
+def extract_data_points(text):
+    llm = ChatOpenAI(temperature=0, model="gpt-4")
+    prompt = """Extract market metrics from this text as JSON:
+    {text}
     
-    researcher = Agent(
-        role='Senior Market Researcher',
-        goal=f'Research {topic} market and address: {", ".join(questions)}',
-        backstory="Expert at gathering and analyzing market data with focus on accuracy and comprehensive coverage.",
-        tools=[search_tool, scrape_tool],
-        verbose=True
-    )
+    Return format:
+    {{"market_size": "value", "cagr": "value", "market_share_leader": "value", "total_players": "value"}}"""
     
-    analyst = Agent(
-        role='Data Analytics Expert',
-        goal='Process market data into quantitative insights',
-        backstory="Data analytics specialist focusing on market metrics, trends, and actionable insights.",
-        tools=[search_tool],
-        verbose=True
-    )
+    response = llm.invoke(prompt.format(text=text))
+    try:
+        json_str = re.search(r'\{.*\}', response.content, re.DOTALL).group()
+        return json.loads(json_str)
+    except:
+        return {}
+
+def expand_content(base_content, topic):
+    llm = ChatOpenAI(temperature=0.7, model="gpt-4")
+    prompt = f"""Expand this market research content for {topic}:
+    {base_content}
     
-    writer = Agent(
-        role='Research Report Writer',
-        goal='Create detailed market research report',
-        backstory="Expert at synthesizing research into clear, structured reports with strategic insights.",
-        verbose=True
-    )
+    Provide detailed analysis covering:
+    1. Current market state
+    2. Growth drivers and barriers
+    3. Competitive dynamics
+    4. Future outlook
     
-    research_task = Task(
-        description=f"""
-        Conduct comprehensive market research for {topic}:
-        Questions to address:
-        {chr(10).join([f'- {q}' for q in questions])}
-        
-        Required sections:
-        1. Market Overview (size, segments, growth)
-        2. Competitive Landscape
-        3. Industry Trends
-        4. Key Metrics
-        
-        Include specific data points and sources.
-        """,
-        agent=researcher,
-        expected_output="Detailed market research data with verifiable metrics and sources"
-    )
+    Use markdown formatting."""
     
-    analysis_task = Task(
-        description="""
-        Analyze research data to provide:
-        1. Market size and growth metrics
-        2. Competitive market shares
-        3. Growth projections
-        4. Trend impact analysis
-        """,
-        agent=analyst,
-        expected_output="Quantitative analysis with key metrics and projections",
-        context=[research_task]
-    )
-    
-    report_task = Task(
-        description="""
-        Create structured report with:
-        1. Executive Summary
-        2. Market Analysis
-        3. Competitive Analysis
-        4. Future Outlook
+    return llm.invoke(prompt).content
+
+def process_crew_output(crew_result, topic):
+    try:
+        final_report = crew_result.get('final_report_task', {})
+        if isinstance(final_report, str):
+            report_text = final_report
+        else:
+            report_text = str(final_report)
+            
+        metrics = extract_data_points(report_text)
+        expanded_content = expand_content(report_text, topic)
         
-        Include all insights from research and analysis.
-        """,
-        agent=writer,
-        expected_output="Complete market research report in markdown format",
-        context=[research_task, analysis_task]
-    )
-    
-    crew = Crew(
-        agents=[researcher, analyst, writer],
-        tasks=[research_task, analysis_task, report_task],
-        process=Process.sequential,
-        verbose=True
-    )
+        return {
+            'metrics': metrics,
+            'content': expanded_content
+        }
+    except Exception as e:
+        st.error(f"Error processing report: {str(e)}")
+        return {'metrics': {}, 'content': ''}
+
+def display_enhanced_report(report):
+    if 'metrics' in report:
+        st.subheader("Key Market Metrics")
+        cols = st.columns(4)
+        metrics = report['metrics']
+        cols[0].metric("Market Size", metrics.get('market_size', 'N/A'))
+        cols[1].metric("CAGR", metrics.get('cagr', 'N/A'))
+        cols[2].metric("Leader Share", metrics.get('market_share_leader', 'N/A'))
+        cols[3].metric("Key Players", metrics.get('total_players', 'N/A'))
     
-    return crew
+    if 'content' in report:
+        st.markdown(report['content'])