Update app.py

app.py

@@ -1,36 +1,74 @@
-
+import os
 import streamlit as st
-from gpt_researcher.agent import GPTResearcher
+import asyncio
+import nest_asyncio
+from gpt_researcher import GPTResearcher
+
+# Allow async in Streamlit (important for notebooks + event loops)
+nest_asyncio.apply()
 
-st.set_page_config(page_title="GPT Researcher UI", layout="wide")
-st.title("🤖 GPT Researcher — Streamlit UI")
+# Streamlit page config
+st.set_page_config(page_title="GPT Researcher Async UI", layout="wide")
+st.title("📘 GPT Researcher (Async Streamlit Interface)")
 
-# --- Sidebar inputs ---
+# Sidebar Inputs
 with st.sidebar:
-    st.header("🧠 Research Configuration")
-    topic = st.text_input("💡 Research Topic", "AI in climate change")
-    report_type = st.selectbox("📄 Report Type", ["summary", "detailed", "academic"])
-    report_format = st.selectbox("📜 Format", ["markdown", "text"])
-    report_source = st.selectbox("🌐 Sources", ["web", "arxiv", "semantic-scholar", "hybrid"])
-    tone = st.selectbox("🎯 Tone", ["objective", "persuasive", "informative"])
-    start = st.button("🚀 Start Research")
-
-# --- Run GPTResearcher ---
-if start and topic:
-    st.markdown("### ⏳ Running Autonomous Research Agent...")
-    with st.spinner("Gathering knowledge, synthesizing insights..."):
-        agent = GPTResearcher(
-            query=topic,
-            report_type=report_type,
-            report_format=report_format,
-            report_source=report_source,
-            tone=tone
-        )
-        output = agent.run()
-
-    st.success("✅ Research Complete!")
-
-    st.markdown("### 📄 Final Report")
-    st.markdown(output, unsafe_allow_html=True)
-
-    st.download_button("💾 Download Markdown", output, file_name="report.md", mime="text/markdown")
+    st.header("🧠 Research Agent Setup")
+    query = st.text_input("🔍 Research Topic", "Should I invest in Nvidia?")
+    report_type = st.selectbox("📄 Report Type", [
+        "research_report", "summary", "detailed_report"
+    ])
+    run_button = st.button("🚀 Run Research Agent")
+
+# Async wrapper function
+async def run_gpt_researcher(query: str, report_type: str):
+    researcher = GPTResearcher(query, report_type)
+    
+    # Phase 1: Planning + Research
+    await researcher.conduct_research()
+    
+    # Phase 2: Writing
+    report = await researcher.write_report()
+
+    # Additional context
+    context = researcher.get_research_context()
+    costs = researcher.get_costs()
+    images = researcher.get_research_images()
+    sources = researcher.get_research_sources()
+
+    return report, context, costs, images, sources
+
+# Main Execution
+if run_button and query:
+    st.info("🤖 Autonomous agent is researching... Please wait.")
+    
+    # Run async function in Streamlit
+    report, context, costs, images, sources = asyncio.run(run_gpt_researcher(query, report_type))
+
+    st.success("✅ Research completed!")
+
+    # Show final report
+    st.subheader("📄 Final Report")
+    st.markdown(report, unsafe_allow_html=True)
+
+    # Show sources
+    if sources:
+        st.subheader("📚 Sources Used")
+        for src in sources:
+            title = src.get("title", "Untitled")
+            url = src.get("url", "#")
+            st.markdown(f"- [{title}]({url})")
+
+    # Show research cost
+    if costs:
+        st.subheader("💰 Cost Breakdown")
+        st.json(costs)
+
+    # Show related images
+    if images:
+        st.subheader("🖼️ Related Images")
+        for img in images:
+            st.image(img, use_column_width=True)
+
+    # Download button for the report
+    st.download_button("💾 Download Report", data=report, file_name="report.md", mime="text/markdown")