Update app.py

app.py

@@ -6,16 +6,16 @@ import nest_asyncio
 from gpt_researcher import GPTResearcher
 from dotenv import load_dotenv
 
-# Allow async execution in Streamlit
 nest_asyncio.apply()
 load_dotenv()
 
-# Inject Tavily API key (used internally by gpt-researcher if configured)
+# Inject Tavily API key
 os.environ["TAVILY_API_KEY"] = "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"
 
 st.set_page_config(page_title="🧠 Super Deep Research Agent", layout="wide")
 st.title("📚 GPT-Powered Super Deep Research Assistant")
 
+# --- Sidebar UI ---
 with st.sidebar:
     st.header("🔍 Setup Research Agent")
     query = st.text_input("📌 Research Topic", "Is AI a threat to creative jobs?")
@@ -26,28 +26,40 @@ with st.sidebar:
     start = st.button("🚀 Start Deep Research")
 
 # Async wrapper
-async def run_research(query, report_type, source, tone, fmt):
+async def run_research_with_logs(query, report_type, source, tone, fmt, log_callback):
     agent = GPTResearcher(
         query=query,
         report_type=report_type,
         report_source=source,
         report_format=fmt,
-        tone=tone
+        tone=tone,
+        log_fn=log_callback
     )
     await agent.conduct_research()
     report = await agent.write_report()
     context = agent.get_research_context()
     sources = agent.get_research_sources()
-    costs = agent.get_costs()
     images = agent.get_research_images()
-    return report, context, sources, costs, images
+    return report, context, sources, images
 
-# Run and show results
+# --- Main Run ---
 if start and query:
     st.info("🤖 Running super deep research...")
-    report, context, sources, costs, images = asyncio.run(run_research(query, report_type, source_type, tone, output_format))
+
+    log_placeholder = st.empty()
+    log_text = ""
+
+    def stream_log(msg):
+        nonlocal log_text
+        log_text += f"🟢 {msg}\n"
+        log_placeholder.code(log_text, language="text")
+
+    report, context, sources, images = asyncio.run(
+        run_research_with_logs(query, report_type, source_type, tone, output_format, log_callback=stream_log)
+    )
 
     st.success("✅ Report generation complete!")
+
     st.subheader("📄 Final Report")
     st.markdown(report, unsafe_allow_html=True)
 
@@ -55,9 +67,6 @@ if start and query:
     for s in sources:
         st.markdown(f"- [{s.get('title', 'Untitled')}]({s.get('url', '#')})")
 
-    st.subheader("💰 Token Usage & Cost")
-    st.json(costs)
-
     if images:
         st.subheader("🖼️ Relevant Images")
         for img in images: