Update app.py

app.py

@@ -1,5 +1,6 @@
 # app.py
 import os
+import time
 import streamlit as st
 from dotenv import load_dotenv
 from crewai import Agent, Crew, Process, Task
@@ -15,7 +16,7 @@ st.set_page_config(
     layout="wide"
 )
 
-# Add custom CSS
+# Add custom CSS for agent chat bubbles and animations
 st.markdown("""
     <style>
         .stButton>button {
@@ -28,143 +29,220 @@ st.markdown("""
             border-radius: 10px;
             margin-top: 20px;
         }
+        .agent-bubble {
+            padding: 10px 15px;
+            border-radius: 15px;
+            margin: 5px 0;
+            max-width: 80%;
+            animation: fadeIn 0.5s ease-in;
+        }
+        .researcher {
+            background-color: #E3F2FD;
+            margin-right: 20%;
+        }
+        .analyst {
+            background-color: #F3E5F5;
+            margin-right: 20%;
+        }
+        .writer {
+            background-color: #E8F5E9;
+            margin-right: 20%;
+        }
+        @keyframes fadeIn {
+            from { opacity: 0; transform: translateY(10px); }
+            to { opacity: 1; transform: translateY(0); }
+        }
+        .agent-name {
+            font-weight: bold;
+            margin-bottom: 5px;
+        }
+        .agent-thinking {
+            color: #666;
+            font-style: italic;
+        }
     </style>
 """, unsafe_allow_html=True)
 
-def run_market_research(topic: str):
-    with st.status("🤖 AI Crew working on your report...", expanded=True) as status:
-        try:
-            status.update(label="🔍 Creating AI agents...")
-            
-            # Initialize tools
-            search_tool = SerperDevTool()
-            scrape_tool = ScrapeWebsiteTool()
-            
-            # Create agents
-            researcher = Agent(
-                role='Research Analyst',
-                goal=f'Conduct thorough market research about {topic}',
-                backstory='You are an experienced market research analyst with expertise in data analysis and trend identification.',
-                tools=[search_tool, scrape_tool],
-                verbose=True
-            )
-            
-            analyst = Agent(
-                role='Data Analyst',
-                goal='Analyze research findings and identify key insights',
-                backstory='You are a skilled data analyst with expertise in interpreting market research and creating actionable insights.',
-                tools=[search_tool],
-                verbose=True
-            )
-            
-            writer = Agent(
-                role='Report Writer',
-                goal='Create comprehensive and engaging market research reports',
-                backstory='You are a professional writer specializing in creating clear and compelling business reports.',
-                verbose=True
-            )
-            
-            status.update(label="📋 Defining research tasks...")
-            
-            # Define tasks with expected outputs
-            research_task = Task(
-                description=f"""
-                Conduct comprehensive market research on {topic}.
-                Focus on:
-                1. Current market size and growth projections
-                2. Key players and their market share
-                3. Consumer adoption trends
-                4. Regulatory environment
-                
-                Output should be a detailed research findings document with data and sources.
-                """,
-                agent=researcher,
-                expected_output="A comprehensive research document containing market data, trends, and competitor analysis with clear citations and sources."
-            )
-            
-            analysis_task = Task(
-                description=f"""
-                Analyze the research findings for {topic} and identify:
-                1. Key market opportunities
-                2. Potential challenges
-                3. Growth drivers
-                4. Competitive dynamics
-                
-                Use the research provided by the Research Analyst to create an analytical report.
-                """,
-                agent=analyst,
-                expected_output="An analytical report with key insights, opportunities, challenges, and strategic implications based on the research data.",
-                context=[research_task]
-            )
-            
-            report_task = Task(
-                description=f"""
-                Create a detailed market research report that includes:
-                1. Executive summary
-                2. Market overview
-                3. Key findings
-                4. Strategic recommendations
-                
-                Base the report on the research and analysis provided by the other agents.
-                The report should be well-structured, professional, and actionable.
-                """,
-                agent=writer,
-                expected_output="A complete market research report in markdown format with executive summary, findings, and recommendations.",
-                context=[research_task, analysis_task]
-            )
+def create_agent_message(agent_role: str, message: str, container):
+    """Create a styled message bubble for an agent"""
+    class_name = agent_role.lower().replace(" ", "-")
+    agent_icons = {
+        "Research Analyst": "🔍",
+        "Data Analyst": "📊",
+        "Report Writer": "✍️"
+    }
+    
+    container.markdown(
+        f"""
+        <div class="agent-bubble {class_name}">
+            <div class="agent-name">{agent_icons.get(agent_role, '')} {agent_role}</div>
+            {message}
+        </div>
+        """,
+        unsafe_allow_html=True
+    )
+
+def run_market_research(topic: str, chat_container):
+    try:
+        # Initialize tools
+        search_tool = SerperDevTool()
+        scrape_tool = ScrapeWebsiteTool()
+
+        # Create custom callback for agent interactions
+        class AgentCallback:
+            def on_agent_start(self, agent):
+                create_agent_message(
+                    agent.role,
+                    f"<div class='agent-thinking'>Starting my task to {agent.goal.lower()}...</div>",
+                    chat_container
+                )
+                time.sleep(1)  # Add slight delay for visual effect
+
+            def on_agent_end(self, agent, result):
+                create_agent_message(
+                    agent.role,
+                    f"I've completed my analysis. Here are the key points:\n\n{result[:200]}...",
+                    chat_container
+                )
+                time.sleep(1)
+
+        # Create agents
+        researcher = Agent(
+            role='Research Analyst',
+            goal=f'Conduct thorough market research about {topic}',
+            backstory='You are an experienced market research analyst with expertise in data analysis and trend identification.',
+            tools=[search_tool, scrape_tool],
+            verbose=True
+        )
+        
+        analyst = Agent(
+            role='Data Analyst',
+            goal='Analyze research findings and identify key insights',
+            backstory='You are a skilled data analyst with expertise in interpreting market research and creating actionable insights.',
+            tools=[search_tool],
+            verbose=True
+        )
+        
+        writer = Agent(
+            role='Report Writer',
+            goal='Create comprehensive and engaging market research reports',
+            backstory='You are a professional writer specializing in creating clear and compelling business reports.',
+            verbose=True
+        )
+        
+        # Define tasks with expected outputs
+        research_task = Task(
+            description=f"""
+            Conduct comprehensive market research on {topic}.
+            Focus on:
+            1. Current market size and growth projections
+            2. Key players and their market share
+            3. Consumer adoption trends
+            4. Regulatory environment
             
-            status.update(label="🚀 Assembling the crew and starting research...")
+            Output should be a detailed research findings document with data and sources.
+            """,
+            agent=researcher,
+            expected_output="A comprehensive research document containing market data, trends, and competitor analysis with clear citations and sources."
+        )
+        
+        analysis_task = Task(
+            description=f"""
+            Analyze the research findings for {topic} and identify:
+            1. Key market opportunities
+            2. Potential challenges
+            3. Growth drivers
+            4. Competitive dynamics
             
-            # Create and run the crew with boolean verbose flag
-            crew = Crew(
-                agents=[researcher, analyst, writer],
-                tasks=[research_task, analysis_task, report_task],
-                verbose=True,  # Changed from 2 to True
-                process=Process.sequential
-            )
+            Use the research provided by the Research Analyst to create an analytical report.
+            """,
+            agent=analyst,
+            expected_output="An analytical report with key insights, opportunities, challenges, and strategic implications based on the research data.",
+            context=[research_task]
+        )
+        
+        report_task = Task(
+            description=f"""
+            Create a detailed market research report that includes:
+            1. Executive summary
+            2. Market overview
+            3. Key findings
+            4. Strategic recommendations
             
-            result = crew.kickoff()
-            status.update(label="✅ Report generated successfully!", state="complete")
-            return result
+            Base the report on the research and analysis provided by the other agents.
+            The report should be well-structured, professional, and actionable.
+            """,
+            agent=writer,
+            expected_output="A complete market research report in markdown format with executive summary, findings, and recommendations.",
+            context=[research_task, analysis_task]
+        )
 
-        except Exception as e:
-            status.update(label=f"❌ Error: {str(e)}", state="error")
-            st.error(f"Error details: {str(e)}")
-            return None
+        # Create and run the crew
+        crew = Crew(
+            agents=[researcher, analyst, writer],
+            tasks=[research_task, analysis_task, report_task],
+            verbose=True,
+            process=Process.sequential
+        )
+
+        # Add callback to crew
+        crew.callback = AgentCallback()
+        
+        result = crew.kickoff()
+        return result
+
+    except Exception as e:
+        st.error(f"Error details: {str(e)}")
+        return None
 
 def main():
     st.title("🤖 AI Market Research Generator")
     st.markdown("""
     This tool uses a crew of AI agents to generate comprehensive market research reports. 
-    Each report is created through a collaborative process involving:
-    - 🔍 A Research Analyst who gathers market data
-    - 📊 A Data Analyst who processes the findings
-    - ✍️ A Report Writer who creates the final document
+    Watch the agents collaborate in real-time below!
     """)
     
-    # Input section
-    st.subheader("Research Topic")
-    topic = st.text_input(
-        "Enter the market or product you want to research",
-        placeholder="e.g., Electric Vehicles Market",
-        key="research_topic"
-    )
+    # Create two columns
+    col1, col2 = st.columns([2, 3])
+    
+    with col1:
+        st.subheader("Research Topic")
+        topic = st.text_input(
+            "Enter the market or product you want to research",
+            placeholder="e.g., Electric Vehicles Market",
+            key="research_topic"
+        )
+        
+        start_button = st.button("Generate Report", type="primary")
     
-    if st.button("Generate Report", type="primary"):
+    # Initialize or get the chat container from session state
+    if "chat_container" not in st.session_state:
+        st.session_state.chat_container = col2.container()
+    
+    # Initialize or get the report container from session state
+    if "report_container" not in st.session_state:
+        st.session_state.report_container = st.container()
+    
+    if start_button:
         if not topic:
             st.error("Please enter a research topic")
             return
-            
-        report = run_market_research(topic)
+        
+        # Clear previous chat messages
+        st.session_state.chat_container.empty()
+        
+        # Show the agent conversation
+        with st.session_state.chat_container:
+            st.subheader("🤖 Agents at Work")
+            report = run_market_research(topic, st.session_state.chat_container)
         
         if report:
-            st.subheader("📑 Generated Report")
-            with st.container():
+            with st.session_state.report_container:
+                st.subheader("📑 Generated Report")
                 st.markdown('<div class="report-container">', unsafe_allow_html=True)
                 st.markdown(report)
                 st.markdown('</div>', unsafe_allow_html=True)
                 
-                # Add download button
                 st.download_button(
                     label="Download Report",
                     data=report,