Optimize for HF Spaces - clean Dockerfile and enhanced UI

.streamlit/config.toml

@@ -1,12 +0,0 @@
-[theme]
-primaryColor="#FF4B4B"
-backgroundColor="#FFFFFF"
-secondaryBackgroundColor="#F0F2F6"
-textColor="#262730"
-font="sans serif"
-
-[server]
-headless = true
-port = 7860
-enableCORS = false
-enableXsrfProtection = false

Dockerfile

@@ -5,24 +5,28 @@ WORKDIR /app
 # Install system dependencies
 RUN apt-get update && apt-get install -y \
     build-essential \
-    curl \
-    software-properties-common \
     && rm -rf /var/lib/apt/lists/*
 
-# Copy requirements first for better caching
+# Copy requirements and install dependencies
 COPY requirements.txt .
-
-# Install Python dependencies
 RUN pip install --no-cache-dir -r requirements.txt
 
-# Copy application code
-COPY . .
+# Copy application files
+COPY main.py .
+COPY src/ ./src/
 
-# Expose port 7860 (HF Spaces default)
+# Expose HF Spaces port
 EXPOSE 7860
 
-# Health check
-HEALTHCHECK CMD curl --fail http://localhost:7860/_stcore/health
-
-# Run the application
-CMD ["streamlit", "run", "main.py", "--server.port=7860", "--server.address=0.0.0.0"]
+# Set environment variables for Streamlit
+ENV STREAMLIT_SERVER_PORT=7860
+ENV STREAMLIT_SERVER_ADDRESS=0.0.0.0
+ENV STREAMLIT_SERVER_HEADLESS=true
+ENV STREAMLIT_BROWSER_GATHER_USAGE_STATS=false
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "main.py", \
+    "--server.port=7860", \
+    "--server.address=0.0.0.0", \
+    "--server.headless=true", \
+    "--browser.gatherUsageStats=false"]

main.py

@@ -1,24 +1,43 @@
 import streamlit as st
 import uuid
+import os
 from dotenv import load_dotenv
 from src.graph import app
 
-# Load environment variables from.env file
+# Load environment variables (works with .env file or HF Spaces secrets)
 load_dotenv()
 
 # --- Streamlit UI Configuration ---
-st.set_page_config(page_title="Autonomous Research Agent", page_icon="🤖", layout="wide")
-st.title("Autonomous Research Agent")
+st.set_page_config(
+    page_title="Autonomous Research Agent",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="collapsed"
+)
+
+st.title("🤖 Autonomous Research Agent")
+st.markdown("""
+Welcome! This autonomous agent will research any topic for you using AI-powered web search and analysis.
+
+**How it works:**
+1. Enter your research topic below
+2. The agent will search the web, evaluate sources, and summarize findings
+3. Get a comprehensive research report in minutes
+
+*Powered by LangGraph, Groq (Llama 3.3), and Tavily AI*
+""")
+
+# Check for API keys
+if not os.getenv("GROQ_API_KEY") or not os.getenv("TAVILY_API_KEY"):
+    st.error("⚠️ API keys not configured. Please add GROQ_API_KEY and TAVILY_API_KEY in Space Settings → Repository Secrets.")
+    st.stop()
 
 # --- Session State Management ---
-# This ensures that each user session has a unique thread_id
-# and that the message history is maintained across reruns.
 if "thread_id" not in st.session_state:
     st.session_state.thread_id = str(uuid.uuid4())
-    # FIX 1: Initialize with an empty list
-    st.session_state.messages =[]
+    st.session_state.messages = []
 
-# Display the chat history
+# Display chat history
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
         st.markdown(message["content"])
@@ -33,33 +52,32 @@ if prompt := st.chat_input("What topic should I research for you?"):
     # Prepare to display the agent's response
     with st.chat_message("assistant"):
         # Use a status container to show the agent's progress
-        with st.status("Researching...", expanded=True) as status:
+        with st.status("🔍 Researching...", expanded=True) as status:
             final_report = ""
             
             # LangGraph configuration for the specific session
             config = {"configurable": {"thread_id": st.session_state.thread_id}}
-            # FIX 2: Initialize summaries with an empty list
-            initial_state = {"topic": prompt, "summaries":[]}
+            initial_state = {"topic": prompt, "summaries": []}
 
             # Stream events from the LangGraph agent
             for event in app.stream(initial_state, config=config):
                 for key, value in event.items():
                     if key == "search":
-                        status.write("Searching for relevant articles...")
-                    elif key == "scrape_and_evaluate":
+                        status.write("🔍 Searching for relevant articles...")
+                    elif key == "scrape_and_summarize":
                         if value.get("scraped_content"):
                             url = value['scraped_content'].get('url', 'Unknown URL')
                             is_relevant = value['scraped_content'].get('is_relevant', 'Unknown')
-                            status.write(f"Evaluating URL: {url} - Relevant: {is_relevant}")
+                            status.write(f"📄 Evaluating: {url} - Relevant: {is_relevant}")
                     elif key == "summarize":
-                        status.write("Summarizing relevant content...")
+                        status.write("📝 Summarizing relevant content...")
                     elif key == "compile_report":
-                        status.write("Compiling the final report...")
+                        status.write("📊 Compiling the final report...")
                         if value.get("report"):
                             final_report = value["report"]
             
             # Update the status to "complete" when done
-            status.update(label="Research complete!", state="complete", expanded=False)
+            status.update(label="✅ Research complete!", state="complete", expanded=False)
         
         # Display the final report
         st.markdown(final_report)