Create app.py

app.py

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+import os
+import gradio as gr
+from dotenv import load_dotenv
+import asyncio
+
+# Import the new deep research logic
+from research_agent import deep_research, ResearchReport
+
+# Load environment variables
+load_dotenv()
+
+async def interact(user_message, history):
+    """
+    Handle user interaction for Deep Research.
+    """
+    if not user_message:
+        yield history, ""
+        return
+
+    # Append user message to Gradio history
+    history = history or []
+    history.append({"role": "user", "content": user_message})
+    
+    # Yield initial state
+    yield history, ""
+
+    # Call the deep_research generator
+    # It yields status strings (progress updates) and finally a ResearchReport object
+    async for update in deep_research(user_message):
+        if isinstance(update, str):
+            # It's a status update
+            # We can show this as a temporary system message or just log it
+            # For a chat interface, we can append a system message that updates
+            if history[-1]["role"] != "assistant":
+                history.append({"role": "assistant", "content": update})
+            else:
+                history[-1]["content"] = update
+            yield history, ""
+            
+        elif isinstance(update, ResearchReport):
+            # Final report
+            report_md = f"# Research Report: {user_message}\n\n"
+            report_md += f"## Executive Summary\n{update.executive_summary}\n\n"
+            
+            for section in update.sections:
+                report_md += f"### {section.title}\n{section.content}\n\n"
+                if section.sources:
+                    report_md += "**Sources:**\n" + "\n".join([f"- {s}" for s in section.sources]) + "\n\n"
+            
+            report_md += f"## Risks & Uncertainties\n{update.risks_uncertainties}\n\n"
+            report_md += "## What to Watch Next\n" + "\n".join([f"- {item}" for item in update.what_to_watch_next])
+            
+            # Replace the last status message with the final report
+            history[-1]["content"] = report_md
+            yield history, ""
+
+# Create the Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Pydantic AI Deep Research Agent")
+    gr.Markdown("Enter a stock ticker (e.g., NVDA) or a research topic to generate a detailed report.")
+    
+    chatbot = gr.Chatbot(label="Agent", height=700)
+    msg = gr.Textbox(placeholder="Enter ticker or topic...", label="Research Query")
+    
+    # Submit handler
+    msg.submit(
+        interact,
+        inputs=[msg, chatbot],
+        outputs=[chatbot, msg]
+    )
+
+if __name__ == "__main__":
+    demo.launch()