Update app.py

app.py

@@ -3,6 +3,10 @@ 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():
@@ -31,69 +35,56 @@ def scrape_website(url):
     except requests.exceptions.RequestException as e:
         return f"Error during scraping: {str(e)}"
 
-# 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]
-)
+# 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
 
 # Orchestrate the workflow
 def orchestrate_workflow(client, url):
-    # Step 1: Scrape the website
+    # Scrape, analyze, and summarize content
     scrape_result = client.run(
-        agent=scraper_agent,
+        agent=Agent(
+            name="Scraper Agent",
+            instructions="Scrape content from websites.",
+            functions=[scrape_website]
+        ),
         messages=[{"role": "user", "content": f"Scrape the following website: {url}"}]
     )
     scraped_content = scrape_result.messages[-1]["content"]
 
-    # Check for any error during scraping
+    # Handle errors during scraping
     if "Error during scraping" in scraped_content:
         return scraped_content
 
-    # Step 2: Analyze the scraped content
-    research_result = client.run(
-        agent=research_agent,
+    # 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]}..."]
+        ),
         messages=[{"role": "user", "content": f"Analyze the following content: {scraped_content}"}]
     )
-    analysis_summary = research_result.messages[-1]["content"]
-
-    # 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}
-    )
+    analysis_summary = analyze_result.messages[-1]["content"]
 
-    final_summary = writer_result.messages[-1]["content"]
-    return final_summary
+    return analysis_summary
 
 # Streamlit App UI
 st.markdown(
@@ -101,7 +92,6 @@ st.markdown(
     <style>
     .title { text-align: center; font-size: 2.5rem; font-weight: bold; }
     .description { text-align: center; font-size: 1.1rem; color: #555; }
-    .button-container { text-align: center; }
     .ack { font-size: 0.95rem; color: #888; text-align: center; }
     </style>
     """,
@@ -111,9 +101,6 @@ st.markdown(
 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.write("")
-st.write("")
-
 fetch_openai_api_key()
 
 # Initialize Swarm client only after API key is set
@@ -124,16 +111,39 @@ 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("")
-
     if st.button("Run Workflow"):
         if url:
             with st.spinner("Running the multi-agent workflow... This may take a moment."):
-                final_report = orchestrate_workflow(client, url)
+                final_summary = orchestrate_workflow(client, url)
+
             st.success("✅ Workflow complete!")
             st.write("### 📜 Final Report:")
-            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"
+            )
         else:
             st.error("❌ Please enter a valid URL.")
 else:
@@ -141,4 +151,11 @@ else:
 
 # Footer with credits
 st.divider()
-st.markdown('<div class="ack">Acknowledgement: </div>', unsafe_allow_html=True)
+st.markdown(
+    """
+    <div class="ack">
+        Acknowledgment: This work is based on <a href="https://github.com/jadouse5/openai-swarm-webscraper" target="_blank">Jad Tounsi El Azzoiani's work</a>.
+    </div>
+    """,
+    unsafe_allow_html=True
+)