feat: Add FastAPI deployment files, Dockerfile, and Hugging Face sync GitHub Action

.dockerignore

@@ -0,0 +1,6 @@
+venv/
+__pycache__/
+*.pyc
+.env
+.git
+.gitignore

.github/workflows/hf_sync.yml

@@ -0,0 +1,22 @@
+name: Sync to Hugging Face Space
+
+on:
+  push:
+    branches: [main]
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+
+      - name: Push to HF
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: |
+          git push --force https://Dhruvsoni4125:$HF_TOKEN@huggingface.co/spaces/Dhruvsoni4125/Multi-Agent-Rag-System main

Dockerfile

@@ -0,0 +1,7 @@
+FROM python:3.11-slim
+WORKDIR /code
+COPY ./requirement.txt /code/requirements.txt
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+COPY . .
+# Hugging Face runs on port 7860 by default
+CMD ["uvicorn", "main:app", "--host", "0.0.0.0", "--port", "7860"]

app.py

@@ -0,0 +1,76 @@
+import streamlit as st
+import os
+from pipeline import run_research_pipeline
+from dotenv import load_dotenv
+
+# Load local environment variables if present
+load_dotenv()
+
+# Map Gemini_API_KEY to GOOGLE_API_KEY for LangChain if necessary
+if "Gemini_API_KEY" in os.environ and "GOOGLE_API_KEY" not in os.environ:
+    os.environ["GOOGLE_API_KEY"] = os.environ["Gemini_API_KEY"]
+
+st.set_page_config(
+    page_title="Multi-Agent Research Assistant",
+    page_icon="🤖",
+    layout="wide"
+)
+
+# Custom Styling
+st.markdown("""
+<style>
+    .main-title {
+        font-size: 3rem;
+        font-weight: 700;
+        color: #1E3A8A;
+        margin-bottom: 0.5rem;
+    }
+    .subtitle {
+        font-size: 1.2rem;
+        color: #4B5563;
+        margin-bottom: 2rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+st.markdown('<div class="main-title">🤖 Multi-Agent RAG Research Assistant</div>', unsafe_allow_html=True)
+st.markdown('<div class="subtitle">Enter a topic. The system will search the web, scrape articles, compile a report, and critique it.</div>', unsafe_allow_html=True)
+
+topic = st.text_input("What would you like to research?", placeholder="e.g., Advancements in Quantum Computing")
+
+if st.button("Start Agent Collaboration", type="primary"):
+    if not topic.strip():
+        st.error("Please enter a research topic first.")
+    else:
+        # Visual collaboration status
+        with st.status("Agents are collaborating...", expanded=True) as status_box:
+            st.write("🔍 Search Agent: Searching the web via Tavily...")
+            
+            # Run the research pipeline
+            try:
+                result = run_research_pipeline(topic)
+                status_box.update(label="Research Complete!", state="complete", expanded=False)
+                st.success("Research completed successfully!")
+                
+                # Show results in nice clean tabs
+                tab1, tab2, tab3 = st.tabs(["📄 Final Report", "⭐ Critic Review", "🔧 Technical Data"])
+                
+                with tab1:
+                    st.markdown("### Drafted Report")
+                    st.markdown(result.get("report", "No report generated."))
+                    
+                with tab2:
+                    st.markdown("### Critic Score and Feedback")
+                    st.markdown(result.get("feedback", "No feedback generated."))
+                    
+                with tab3:
+                    col1, col2 = st.columns(2)
+                    with col1:
+                        st.subheader("Web Search Output")
+                        st.code(result.get("search_results", "None"))
+                    with col2:
+                        st.subheader("Scraped Content Summary")
+                        st.code(result.get("scraped_content", "None"))
+            except Exception as e:
+                status_box.update(label="Pipeline Failed", state="error")
+                st.error(f"Error executing pipeline: {str(e)}")

main.py

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+import os
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from pipeline import run_research_pipeline
+from dotenv import load_dotenv
+
+load_dotenv()
+
+# Map Gemini_API_KEY to GOOGLE_API_KEY for LangChain if necessary
+if "Gemini_API_KEY" in os.environ and "GOOGLE_API_KEY" not in os.environ:
+    os.environ["GOOGLE_API_KEY"] = os.environ["Gemini_API_KEY"]
+
+app = FastAPI(
+    title="Multi-Agent RAG System API",
+    description="REST API for the multi-agent research pipeline.",
+    version="1.0.0"
+)
+
+# Enable CORS for frontend integration
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+class ResearchRequest(BaseModel):
+    topic: str
+
+@app.get("/")
+def home():
+    return {"status": "online", "docs": "/docs"}
+
+@app.post("/research")
+def research(request: ResearchRequest):
+    if not request.topic.strip():
+        raise HTTPException(status_code=400, detail="Topic is empty")
+    try:
+        return run_research_pipeline(request.topic)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))

requirement.txt

@@ -20,4 +20,5 @@ fastapi
 uvicorn
 
 langsmith
-pytest
+pytest
+streamlit