Update test.py

test.py

@@ -1,160 +1,40 @@
-import streamlit as st
-from crewai import Agent, Task, Crew
-import os
-from langchain_groq import ChatGroq
-from fpdf import FPDF
-import pandas as pd
-import plotly.express as px
-import time
-
-# Title and Sidebar
-st.title("Multi-Agent Business Consultant")
-
-st.sidebar.write("About:")
-st.sidebar.write(
-    "This Business Consultant is built using Multi-Agent system. "
-    "Use this application to generate actionable business insights and data-driven analysis. "
-)
-
-# User Inputs
-business = st.text_input('Enter The Required Business Search Area', value="Artificial Intelligence")
-stakeholder = st.text_input('Enter The Stakeholder Team', value="Executives")
-
-# Optional Customization
-enable_customization = st.sidebar.checkbox("Enable Advanced Agent Customization")
-if enable_customization:
-    st.sidebar.markdown("### Customize Agent Goals")
-    planner_goal = st.sidebar.text_area(
-        "Planner Goal",
-        value="Plan engaging and factually accurate content about the topic."
-    )
-    writer_goal = st.sidebar.text_area(
-        "Writer Goal",
-        value="Write insightful and engaging content based on the topic."
-    )
-    analyst_goal = st.sidebar.text_area(
-        "Analyst Goal",
-        value="Perform statistical analysis to extract actionable insights."
-    )
-else:
-    planner_goal = "Plan engaging and factually accurate content about the topic."
-    writer_goal = "Write insightful and engaging content based on the topic."
-    analyst_goal = "Perform statistical analysis to extract actionable insights."
-
-#=================
-# LLM Object and API Key
-#=================
-llm = ChatGroq(groq_api_key=os.getenv("GROQ_API_KEY"), model="groq/llama-3.3-70b-versatile")
-
-#=================
-# Crew Agents
-#=================
-
-planner = Agent(
-    role="Business Consultant",
-    goal=planner_goal,
-    backstory=(
-        "You're tasked with providing insights about {topic} to the stakeholder: {stakeholder}. "
-        "Your work will form the foundation for the Business Writer and Data Analyst."
-    ),
-    allow_delegation=False,
-    verbose=True,
-    llm=llm
-)
-
-writer = Agent(
-    role="Business Writer",
-    goal=writer_goal,
-    backstory=(
-        "You will write a professional insights document about {topic}, "
-        "based on the Business Consultant's plan and the Data Analyst's results."
-    ),
-    allow_delegation=False,
-    verbose=True,
-    llm=llm
-)
-
-analyst = Agent(
-    role="Data Analyst",
-    goal=analyst_goal,
-    backstory=(
-        "You will perform statistical analysis on {topic}, based on the Business Consultant's plan. "
-        "Your analysis will support the Business Writer's final document for {stakeholder}."
-    ),
-    allow_delegation=False,
-    verbose=True,
-    llm=llm
-)
-
-#=================
-# Crew Tasks
-#=================
-
-plan = Task(
-    description=(
-        "1. Research trends, key players, and noteworthy news for {topic}.\n"
-        "2. Provide structured insights and actionable recommendations.\n"
-        "3. Suggest strategies for dealing with international operators.\n"
-        "4. Limit content to 500 words."
-    ),
-    expected_output="A comprehensive consultancy document with insights and recommendations.",
-    agent=planner
-)
-
-write = Task(
-    description=(
-        "1. Use the Business Consultant's plan to write a professional document for {topic}.\n"
-        "2. Structure the content with engaging sections and visuals.\n"
-        "3. Ensure alignment with the stakeholder's goals.\n"
-        "4. Limit the document to 200 words."
-    ),
-    expected_output="A professional document tailored for {stakeholder}.",
-    agent=writer
-)
-
-analyse = Task(
-    description=(
-        "1. Perform statistical analysis to provide actionable insights for {topic}.\n"
-        "2. Collaborate with the Business Consultant and Writer to align on key metrics.\n"
-        "3. Present findings in a format suitable for inclusion in the final document."
-    ),
-    expected_output="A data-driven analysis tailored for {stakeholder}.",
-    agent=analyst
-)
-
-#=================
-# Execution
-#=================
-
-crew = Crew(
-    agents=[planner, analyst, writer],
-    tasks=[plan, analyse, write],
-    verbose=True
-)
-
-def generate_pdf_report(result):
-    """Generate a professional PDF report from the Crew output."""
-    pdf = FPDF()
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
-    pdf.set_auto_page_break(auto=True, margin=15)
-
-    # Title
-    pdf.set_font("Arial", size=16, style="B")
-    pdf.cell(200, 10, txt="AI Business Consultant Report", ln=True, align="C")
-    pdf.ln(10)
-
-    # Content
-    pdf.set_font("Arial", size=12)
-    pdf.multi_cell(0, 10, txt=result)
-
-    # Save PDF
-    report_path = "Business_Insights_Report.pdf"
-    pdf.output(report_path)
-    return report_path
+def display_analysis_results(result):
+    """
+    Process and display analysis results from agents' output.
+    """
+    try:
+        # Display the result text
+        st.markdown("#### Detailed Insights")
+        st.text(result)
+        
+        # Parse the result into segments if formatted
+        if isinstance(result, str) and "\n" in result:
+            lines = result.split("\n")
+            insights = [line for line in lines if line.strip()]
+            
+            # Show insights as bullet points
+            st.markdown("#### Key Insights (Parsed)")
+            for insight in insights:
+                st.write(f"- {insight}")
+
+        # Generate a visualization if result contains numerical data
+        st.markdown("#### Example Visualization (If Applicable)")
+        if "data:" in result.lower():
+            # Example parsing numerical data from result (adapt to your agent's output)
+            data_lines = [line for line in result.split("\n") if "data:" in line.lower()]
+            data_points = [
+                {"Category": f"Point {i+1}", "Value": float(line.split(":")[-1].strip())}
+                for i, line in enumerate(data_lines) if line.split(":")[-1].strip().replace('.', '', 1).isdigit()
+            ]
+            if data_points:
+                df = pd.DataFrame(data_points)
+                fig = px.bar(df, x="Category", y="Value", title="Extracted Data Visualization")
+                st.plotly_chart(fig)
+    except Exception as e:
+        st.error(f"Error processing results: {e}")
 
 if st.button("Run Analysis"):
-    with st.spinner('Executing analysis...'):
+    with st.spinner('Executing...'):
         try:
             start_time = time.time()
             result = crew.kickoff(inputs={"topic": business, "stakeholder": stakeholder})
@@ -162,15 +42,12 @@ if st.button("Run Analysis"):
 
             # Display Results
             st.markdown("### Insights and Analysis")
-            st.write(result)
+            display_analysis_results(result)
 
-            # Display Execution Time
+            # Execution Time
             st.success(f"Analysis completed in {execution_time:.2f} seconds!")
 
-            #fig = px.bar(data, x="Metric", y="Value", title="Sample Metrics for Analysis")
-            #st.plotly_chart(fig)
-
-            # Generate and Provide PDF Report
+            # Generate PDF Report
             report_path = generate_pdf_report(result)
             with open(report_path, "rb") as file:
                 st.download_button(
@@ -179,6 +56,5 @@ if st.button("Run Analysis"):
                     file_name="Business_Insights_Report.pdf",
                     mime="application/pdf"
                 )
-
         except Exception as e:
-            st.error(f"An error occurred during execution: {e}")
+            st.error(f"An error occurred during execution: {e}")