Deploy from anycoder - streamlit_app.py

streamlit_app.py

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+import streamlit as st
+import os
+from datetime import datetime
+import tempfile
+from pathlib import Path
+
+# Import our modules
+from utils import initialize_rag_system, clear_session_state, format_sources
+from config import MODEL_NAME, EMBEDDING_MODEL
+
+# Page config
+st.set_page_config(
+    page_title="AI RAG Assistant",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .main-header {font-size: 2.5rem; font-weight: 700; color: #1f77b4;}
+    .chat-message {padding: 1rem; border-radius: 1rem; margin: 1rem 0;}
+    .user-message {background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;}
+    .assistant-message {background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%);}
+    .source-container {background: #f8f9fa; border-left: 4px solid #007bff; padding: 1rem;}
+</style>
+""", unsafe_allow_html=True)
+
+# Header
+st.markdown('<h1 class="main-header">🤖 AI RAG Assistant</h1>', unsafe_allow_html=True)
+st.markdown("Upload your documents and chat with your data using advanced RAG powered by Llama-4-Scout")
+
+# Footer with attribution
+st.markdown(
+    "**Built with** [![anycoder](https://img.shields.io/badge/Built%20with-anycoder-3b82f6?style=for-the-badge&logo=huggingface)](https://huggingface.co/spaces/akhaliq/anycoder)"
+)
+
+# Sidebar
+with st.sidebar:
+    st.header("⚙️ Settings")
+    
+    # Model selection
+    model_name = st.selectbox(
+        "Response Model", 
+        [MODEL_NAME],
+        help="Llama-4-Scout for powerful reasoning"
+    )
+    
+    embedding_model = st.selectbox(
+        "Embedding Model",
+        [EMBEDDING_MODEL],
+        help="bge-m3: State-of-the-art multilingual embeddings"
+    )
+    
+    # Similarity threshold
+    similarity_threshold = st.slider(
+        "Similarity Threshold", 0.5, 0.95, 0.8,
+        help="Minimum similarity score for relevant chunks"
+    )
+    
+    # Max new tokens
+    max_new_tokens = st.slider("Max Tokens", 200, 2000, 1000)
+    
+    st.divider()
+    if st.button("🗑️ Clear Chat & Memory", type="secondary"):
+        clear_session_state()
+        st.rerun()
+
+# Initialize session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+if "rag_system" not in st.session_state:
+    st.session_state.rag_system = None
+if "documents_processed" not in st.session_state:
+    st.session_state.documents_processed = 0
+
+# File upload section
+uploaded_files = st.file_uploader(
+    "📁 Upload Documents",
+    type=['pdf', 'txt', 'md', 'docx', 'doc', 'pptx', 'ppt'],
+    accept_multiple_files=True,
+    help="Supports PDF, TXT, MD, DOCX, PPTX and more"
+)
+
+# Process uploaded files
+if uploaded_files:
+    with st.spinner("Processing documents... This may take a moment."):
+        try:
+            temp_dir = tempfile.mkdtemp()
+            for file in uploaded_files:
+                file_path = Path(temp_dir) / file.name
+                with open(file_path, "wb") as f:
+                    f.write(file.getbuffer())
+            
+            # Initialize or update RAG system
+            st.session_state.rag_system = initialize_rag_system(
+                temp_dir,
+                model_name,
+                embedding_model,
+                similarity_threshold
+            )
+            st.session_state.documents_processed = len(uploaded_files)
+            
+            st.success(f"✅ Processed {len(uploaded_files)} documents successfully!")
+            st.info(f"📊 {st.session_state.documents_processed} documents indexed and ready for querying")
+            
+        except Exception as e:
+            st.error(f"❌ Error processing documents: {str(e)}")
+
+# Status indicator
+if st.session_state.rag_system is not None:
+    col1, col2 = st.columns([3, 1])
+    with col1:
+        st.success(f"✅ Ready! {st.session_state.documents_processed} documents loaded")
+    with col2:
+        st.caption(f"Model: {model_name}")
+
+# Chat interface
+st.markdown("---")
+
+# Display chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+        
+        # Display sources for assistant messages
+        if message["role"] == "assistant" and "sources" in message:
+            with st.expander("📚 Sources", expanded=False):
+                st.markdown(format_sources(message["sources"]))
+
+# Chat input
+if prompt := st.chat_input("Ask a question about your documents..."):
+    # Add user message
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Generate response
+    if st.session_state.rag_system is not None:
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                try:
+                    # Query RAG system
+                    response = st.session_state.rag_system.query(prompt)
+                    
+                    # Display response
+                    st.markdown(response.response)
+                    
+                    # Store full response with sources
+                    full_message = {
+                        "role": "assistant",
+                        "content": response.response,
+                        "sources": response.source_nodes
+                    }
+                    st.session_state.messages.append(full_message)
+                    
+                except Exception as e:
+                    st.error(f"Error generating response: {str(e)}")
+    else:
+        with st.chat_message("assistant"):
+            st.warning("👆 Please upload and process documents first!")
+
+# Instructions
+with st.expander("ℹ️ How to use", expanded=False):
+    st.markdown("""
+    1. **Upload documents** (PDF, TXT, MD, DOCX, PPTX supported)
+    2. **Wait for processing** (indexing happens automatically)
+    3. **Ask questions** about your documents
+    4. **Click sources** to see exact references
+    
+    **Features:**
+    - Multi-document support
+    - Advanced semantic search
+    - Source citations
+    - Adjustable similarity threshold
+    - Streaming responses
+    """)
+
+# Performance metrics
+if st.session_state.rag_system is not None:
+    with st.expander("📈 System Info", expanded=False):
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.metric("Documents", st.session_state.documents_processed)
+        with col2:
+            st.metric("Model", MODEL_NAME.split('/')[-1])
+        with col3:
+            st.metric("Embedding", EMBEDDING_MODEL.split('/')[-1])