Update app.py

app.py

@@ -4,7 +4,6 @@ import time
 import streamlit as st
 from dotenv import load_dotenv
 from crewai import Agent, Crew, Process, Task
-from crewai_tools import SerperDevTool, ScrapeWebsiteTool
 
 # Load environment variables
 load_dotenv()
@@ -16,7 +15,7 @@ st.set_page_config(
     layout="wide"
 )
 
-# Add custom CSS for agent chat bubbles and animations
+# Add custom CSS
 st.markdown("""
     <style>
         .stButton>button {
@@ -29,44 +28,32 @@ st.markdown("""
             border-radius: 10px;
             margin-top: 20px;
         }
-        .agent-bubble {
+        .agent-message {
             padding: 10px 15px;
             border-radius: 15px;
             margin: 5px 0;
             max-width: 80%;
-            animation: fadeIn 0.5s ease-in;
+            line-height: 1.4;
         }
         .researcher {
             background-color: #E3F2FD;
-            margin-right: 20%;
+            border-left: 4px solid #1976D2;
         }
         .analyst {
             background-color: #F3E5F5;
-            margin-right: 20%;
+            border-left: 4px solid #9C27B0;
         }
         .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;
+            border-left: 4px solid #43A047;
         }
     </style>
 """, unsafe_allow_html=True)
 
-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 = {
+def display_agent_message(container, role: str, message: str):
+    """Display an agent message in the container"""
+    class_name = role.lower().replace(" ", "-")
+    icons = {
         "Research Analyst": "🔍",
         "Data Analyst": "📊",
         "Report Writer": "✍️"
@@ -74,8 +61,8 @@ def create_agent_message(agent_role: str, message: str, container):
     
     container.markdown(
         f"""
-        <div class="agent-bubble {class_name}">
-            <div class="agent-name">{agent_icons.get(agent_role, '')} {agent_role}</div>
+        <div class="agent-message {class_name}">
+            <b>{icons.get(role, '')} {role}:</b><br/>
             {message}
         </div>
         """,
@@ -84,34 +71,11 @@ def create_agent_message(agent_role: str, message: str, container):
 
 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
+        # Create agents without external tools for now
         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
         )
         
@@ -119,7 +83,6 @@ def run_market_research(topic: str, chat_container):
             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
         )
         
@@ -129,8 +92,15 @@ def run_market_research(topic: str, chat_container):
             backstory='You are a professional writer specializing in creating clear and compelling business reports.',
             verbose=True
         )
+
+        # Display initial agent messages
+        display_agent_message(
+            chat_container,
+            "Research Analyst",
+            f"Starting research on {topic}..."
+        )
         
-        # Define tasks with expected outputs
+        # Define tasks
         research_task = Task(
             description=f"""
             Conduct comprehensive market research on {topic}.
@@ -139,11 +109,15 @@ def run_market_research(topic: str, chat_container):
             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."
+            expected_output="A comprehensive research document with market analysis."
+        )
+        
+        display_agent_message(
+            chat_container,
+            "Data Analyst",
+            "Preparing to analyze the research findings..."
         )
         
         analysis_task = Task(
@@ -153,14 +127,18 @@ def run_market_research(topic: str, chat_container):
             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.",
+            expected_output="An analytical report with key insights and strategic implications.",
             context=[research_task]
         )
         
+        display_agent_message(
+            chat_container,
+            "Report Writer",
+            "Getting ready to compile the final report..."
+        )
+        
         report_task = Task(
             description=f"""
             Create a detailed market research report that includes:
@@ -168,15 +146,12 @@ def run_market_research(topic: str, chat_container):
             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.",
+            expected_output="A complete market research report in markdown format.",
             context=[research_task, analysis_task]
         )
-
+        
         # Create and run the crew
         crew = Crew(
             agents=[researcher, analyst, writer],
@@ -184,65 +159,66 @@ def run_market_research(topic: str, chat_container):
             verbose=True,
             process=Process.sequential
         )
-
-        # Add callback to crew
-        crew.callback = AgentCallback()
         
         result = crew.kickoff()
-        return result
+        
+        # Format the final result
+        if hasattr(result, 'raw_output'):
+            final_report = str(result.raw_output)
+        else:
+            final_report = str(result)
+            
+        # Display completion message
+        display_agent_message(
+            chat_container,
+            "Report Writer",
+            "Report generation completed! ✨"
+        )
+        
+        return final_report
 
     except Exception as e:
-        st.error(f"Error details: {str(e)}")
+        st.error(f"Error: {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. 
-    Watch the agents collaborate in real-time below!
-    """)
     
     # Create two columns
     col1, col2 = st.columns([2, 3])
     
     with col1:
-        st.subheader("Research Topic")
+        st.subheader("Enter Research Topic")
         topic = st.text_input(
-            "Enter the market or product you want to research",
+            "What market would you like to research?",
             placeholder="e.g., Electric Vehicles Market",
             key="research_topic"
         )
         
-        start_button = 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
-        
-        # 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:
-            with st.session_state.report_container:
-                st.subheader("📑 Generated Report")
+        if st.button("Generate Report", type="primary"):
+            if not topic:
+                st.error("Please enter a research topic")
+                return
+            
+            # Clear previous chat
+            if "chat_container" in st.session_state:
+                st.session_state.chat_container.empty()
+            
+            # Create new chat container
+            st.session_state.chat_container = col2.container()
+            
+            # Run the research
+            with st.session_state.chat_container:
+                st.subheader("🤖 Live Agent Updates")
+                report = run_market_research(topic, st.session_state.chat_container)
+            
+            if report:
+                st.subheader("📊 Generated Report")
                 st.markdown('<div class="report-container">', unsafe_allow_html=True)
                 st.markdown(report)
                 st.markdown('</div>', unsafe_allow_html=True)
                 
+                # Download button
                 st.download_button(
                     label="Download Report",
                     data=report,