Update app.py

app.py

@@ -3,10 +3,6 @@ from swarm import Swarm, Agent
 from bs4 import BeautifulSoup
 import requests
 import os
-from io import BytesIO
-from reportlab.lib.pagesizes import letter
-from reportlab.pdfgen import canvas
-import json
 
 # Function to fetch OpenAI API key
 def fetch_openai_api_key():
@@ -35,71 +31,88 @@ def scrape_website(url):
     except requests.exceptions.RequestException as e:
         return f"Error during scraping: {str(e)}"
 
-# Generate PDF report
-def generate_pdf(report):
-    """Generate a PDF file from the report."""
-    buffer = BytesIO()
-    pdf_canvas = canvas.Canvas(buffer, pagesize=letter)
-    pdf_canvas.drawString(100, 750, "Generated Summary Report")
-    pdf_canvas.drawString(100, 735, "-" * 50)
-    
-    lines = report.split('\n')
-    y = 700  # Start position for the text
-    for line in lines:
-        if y < 50:  # Create a new page if content exceeds one page
-            pdf_canvas.showPage()
-            y = 750
-        pdf_canvas.drawString(100, y, line)
-        y -= 15
-    
-    pdf_canvas.save()
-    buffer.seek(0)
-    return buffer
+# Scraper Agent
+scraper_agent = Agent(
+    name="Scraper Agent",
+    instructions="You are an agent that scrapes content from websites.",
+    functions=[scrape_website]
+)
+
+# Define the analysis function
+def analyze_content(content):
+    """Analyzes the scraped content for key points."""
+    summary = f"Summary of content: {content[:200]}..."  # A simple placeholder summarization
+    return summary
+
+# Research Agent
+research_agent = Agent(
+    name="Research Agent",
+    instructions="You are an agent that analyzes content and extracts key insights.",
+    functions=[analyze_content]
+)
+
+# Define the writing function
+def write_summary(context_variables):
+    """Writes a summary based on the analysis."""
+    analysis = context_variables.get('analysis', '')
+    summary = f"Here's a detailed report based on the research: {analysis}"
+    return summary
+
+# Writer Agent
+writer_agent = Agent(
+    name="Writer Agent",
+    instructions="You are an agent that writes summaries of research.",
+    functions=[write_summary]
+)
 
 # Orchestrate the workflow
 def orchestrate_workflow(client, url):
-    # Scrape, analyze, and summarize content
+    # Step 1: Scrape the website
     scrape_result = client.run(
-        agent=Agent(
-            name="Scraper Agent",
-            instructions="Scrape content from websites.",
-            functions=[scrape_website]
-        ),
+        agent=scraper_agent,
         messages=[{"role": "user", "content": f"Scrape the following website: {url}"}]
     )
     scraped_content = scrape_result.messages[-1]["content"]
 
-    # Handle errors during scraping
+    # Check for any error during scraping
     if "Error during scraping" in scraped_content:
         return scraped_content
 
-    # Analyze and summarize the content
-    analyze_result = client.run(
-        agent=Agent(
-            name="Research Agent",
-            instructions="Analyze content and extract insights.",
-            functions=[lambda content: f"Summary: {content[:700]}..."]
-        ),
+    # Step 2: Analyze the scraped content
+    research_result = client.run(
+        agent=research_agent,
         messages=[{"role": "user", "content": f"Analyze the following content: {scraped_content}"}]
     )
-    analysis_summary = analyze_result.messages[-1]["content"]
+    analysis_summary = research_result.messages[-1]["content"]
 
-    return analysis_summary
+    # Step 3: Write the summary based on the analysis
+    writer_result = client.run(
+        agent=writer_agent,
+        messages=[{"role": "user", "content": f"Write a summary based on this analysis: {analysis_summary}"}],
+        context_variables={"analysis": analysis_summary}
+    )
+
+    final_summary = writer_result.messages[-1]["content"]
+    return final_summary
 
 # Streamlit App UI
 st.markdown(
     """
     <style>
-    .title { text-align: center; font-size: 2.5rem; font-weight: bold; }
-    .description { text-align: center; font-size: 1.1rem; color: #555; }
-    .ack { font-size: 0.95rem; color: #888; text-align: center; }
+    .title { text-align: center; font-size: 3rem; font-weight: bold; }
+    .description { text-align: center; font-size: 1.2rem; color: #555; }
+    .button-container { text-align: center; }
+    .ack { font-size: 0.9rem; color: #888; text-align: center; }
     </style>
     """,
     unsafe_allow_html=True,
 )
 
-st.markdown('<div class="title">🔎 Swarm-based Web Content Analyzer</div>', unsafe_allow_html=True)
-st.markdown('<div class="description">Effortlessly extract, analyze, and summarize web content.</div>', unsafe_allow_html=True)
+st.markdown('<div class="title">🔎 Swarm-based Multi-Agent Web Content Analyzer</div>', unsafe_allow_html=True)
+st.markdown('<div class="description">Effortlessly extract, analyze, and summarize web content using AI-powered multi-agent systems.</div>', unsafe_allow_html=True)
+
+st.write("")
+st.write("")
 
 fetch_openai_api_key()
 
@@ -111,39 +124,20 @@ if 'OPENAI_API_KEY' in os.environ and os.environ['OPENAI_API_KEY']:
     st.subheader("🌍 Enter the Website URL")
     url = st.text_input("Enter the URL of the website you want to scrape", placeholder="https://example.com")
 
+    # Run Workflow button
+    st.write("")
+    st.markdown(
+        '<div class="button-container"><button style="padding: 10px 20px; font-size: 16px; background-color: #4CAF50; color: white; border: none; border-radius: 5px; cursor: pointer;">🚀 Run Workflow</button></div>',
+        unsafe_allow_html=True,
+    )
+
     if st.button("Run Workflow"):
         if url:
             with st.spinner("Running the multi-agent workflow... This may take a moment."):
-                final_summary = orchestrate_workflow(client, url)
-
+                final_report = orchestrate_workflow(client, url)
             st.success("✅ Workflow complete!")
             st.write("### 📜 Final Report:")
-            st.write(final_summary)
-
-            # Download options
-            json_data = json.dumps({"summary": final_summary}, indent=4)
-            txt_data = final_summary
-            pdf_data = generate_pdf(final_summary)
-
-
-            st.download_button(
-                label="Download Report as TXT",
-                data=txt_data,
-                file_name="report.txt",
-                mime="text/plain"
-            )
-            st.download_button(
-                label="Download Report as PDF",
-                data=pdf_data,
-                file_name="report.pdf",
-                mime="application/pdf"
-            )
-            st.download_button(
-                label="Download Report as JSON",
-                data=json_data,
-                file_name="report.json",
-                mime="application/json"
-            )
+            st.write(final_report)
         else:
             st.error("❌ Please enter a valid URL.")
 else: