Update app.py

app.py

@@ -1,116 +1,116 @@
-import streamlit as st
-from typing import Dict, Any, List
-import tempfile
-import os
-
-from graph.workflow import LangGraphWorkflow
-from utils.document_loader import DocumentLoader
-from models.vector_store import VectorStore
-
-from dotenv import load_dotenv
-
-load_dotenv()
-
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-OPENWEATHERMAP_API_KEY = os.getenv("OPENWEATHERMAP_API_KEY")
-LANGSMITH_TRACING= True
-LANGSMITH_ENDPOINT= os.getenv("LANGSMITH_ENDPOINT")
-LANGSMITH_API_KEY= os.getenv("LANGSMITH_API_KEY")
-LANGSMITH_PROJECT= os.getenv("LANGSMITH_PROJECT")
-GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
-db_url = os.getenv("db_url")
-db_api = os.getenv("db_api")
-
-
-def main():
-    st.title("AI Pipeline with LangChain & LangGraph")
-    
-    # Initialize components
-    doc_loader = DocumentLoader()
-    vector_store = VectorStore()
-    workflow = LangGraphWorkflow()
-    
-    # Sidebar - Document Upload
-    st.sidebar.header("Upload Documents")
-    uploaded_file = st.sidebar.file_uploader("Upload a PDF document", type="pdf")
-    
-    if uploaded_file:
-        with st.spinner("Processing document..."):
-            # Save the uploaded file
-            pdf_path = doc_loader.save_uploaded_pdf(uploaded_file)
-            
-            if pdf_path:
-                # Load and process the document
-                documents = doc_loader.load_pdf(pdf_path)
-                
-                if documents:
-                    # Add documents to vector store
-                    success = vector_store.add_documents(documents)
-                    
-                    if success:
-                        st.sidebar.success(f"Document '{uploaded_file.name}' processed and indexed successfully!")
-                    else:
-                        st.sidebar.error("Failed to index the document.")
-                else:
-                    st.sidebar.error("Failed to process the document.")
-    
-    # Available documents
-    st.sidebar.header("Available Documents")
-    documents = doc_loader.get_available_documents()
-    if documents:
-        st.sidebar.write(", ".join(documents))
-    else:
-        st.sidebar.write("No documents available")
-    
-    # Chat interface
-    st.header("Chat Interface")
-    
-    # Initialize chat history
-    if "messages" not in st.session_state:
-        st.session_state.messages = []
-    
-    # Display chat history
-    for message in st.session_state.messages:
-        with st.chat_message(message["role"]):
-            st.write(message["content"])
-    
-    # User input
-    user_query = st.chat_input("Ask about weather or document information")
-    
-    if user_query:
-        # Add user message to chat history
-        st.session_state.messages.append({"role": "user", "content": user_query})
-        
-        # Display user message
-        with st.chat_message("user"):
-            st.write(user_query)
-        
-        # Process query
-        with st.spinner("Thinking..."):
-            result = workflow.invoke(user_query)
-            
-            # Add assistant message to chat history
-            st.session_state.messages.append({"role": "assistant", "content": result["response"]})
-            
-            # Display assistant message
-            with st.chat_message("assistant"):
-                st.write(result["response"])
-                
-                # Additional debug info in expander
-                with st.expander("Debug Information"):
-                    st.write(f"Action: {result['action']}")
-                    
-                    if result['action'] == 'weather' and result['city']:
-                        st.write(f"City: {result['city']}")
-                    
-                    if result['action'] == 'document' and result['context']:
-                        st.write("Retrieved Context:")
-                        for i, ctx in enumerate(result['context']):
-                            st.write(f"Document {i+1}:")
-                            st.write(ctx['page_content'])
-                    
-                    st.write("Evaluation Metrics:")
-                    st.write(result['evaluation'])
-
-if __name__ == "__main__":
+import streamlit as st
+from typing import Dict, Any, List
+import tempfile
+import os
+
+from graph.workflow import LangGraphWorkflow
+from utils.document_loader import DocumentLoader
+from models.vector_store import VectorStore
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+OPENWEATHERMAP_API_KEY = os.getenv("OPENWEATHERMAP_API_KEY")
+LANGSMITH_TRACING= True
+LANGSMITH_ENDPOINT= os.getenv("LANGSMITH_ENDPOINT")
+LANGSMITH_API_KEY= os.getenv("LANGSMITH_API_KEY")
+LANGSMITH_PROJECT= os.getenv("LANGSMITH_PROJECT")
+GEMINI_API_KEY = os.getenv("GEMINI_API_KEY")
+db_url = os.getenv("db_url")
+db_api = os.getenv("db_api")
+
+
+def main():
+    st.title("Doc weather Bot")
+    
+    # Initialize components
+    doc_loader = DocumentLoader()
+    vector_store = VectorStore()
+    workflow = LangGraphWorkflow()
+    
+    # Sidebar - Document Upload
+    st.sidebar.header("Upload Documents")
+    uploaded_file = st.sidebar.file_uploader("Upload a PDF document", type="pdf")
+    
+    if uploaded_file:
+        with st.spinner("Processing document..."):
+            # Save the uploaded file
+            pdf_path = doc_loader.save_uploaded_pdf(uploaded_file)
+            
+            if pdf_path:
+                # Load and process the document
+                documents = doc_loader.load_pdf(pdf_path)
+                
+                if documents:
+                    # Add documents to vector store
+                    success = vector_store.add_documents(documents)
+                    
+                    if success:
+                        st.sidebar.success(f"Document '{uploaded_file.name}' processed and indexed successfully!")
+                    else:
+                        st.sidebar.error("Failed to index the document.")
+                else:
+                    st.sidebar.error("Failed to process the document.")
+    
+    # Available documents
+    st.sidebar.header("Available Documents")
+    documents = doc_loader.get_available_documents()
+    if documents:
+        st.sidebar.write(", ".join(documents))
+    else:
+        st.sidebar.write("No documents available")
+    
+    # Chat interface
+    st.header("Chat Interface")
+    
+    # Initialize chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.write(message["content"])
+    
+    # User input
+    user_query = st.chat_input("Ask about weather or document information")
+    
+    if user_query:
+        # Add user message to chat history
+        st.session_state.messages.append({"role": "user", "content": user_query})
+        
+        # Display user message
+        with st.chat_message("user"):
+            st.write(user_query)
+        
+        # Process query
+        with st.spinner("Thinking..."):
+            result = workflow.invoke(user_query)
+            
+            # Add assistant message to chat history
+            st.session_state.messages.append({"role": "assistant", "content": result["response"]})
+            
+            # Display assistant message
+            with st.chat_message("assistant"):
+                st.write(result["response"])
+                
+                # Additional debug info in expander
+                with st.expander("Debug Information"):
+                    st.write(f"Action: {result['action']}")
+                    
+                    if result['action'] == 'weather' and result['city']:
+                        st.write(f"City: {result['city']}")
+                    
+                    if result['action'] == 'document' and result['context']:
+                        st.write("Retrieved Context:")
+                        for i, ctx in enumerate(result['context']):
+                            st.write(f"Document {i+1}:")
+                            st.write(ctx['page_content'])
+                    
+                    st.write("Evaluation Metrics:")
+                    st.write(result['evaluation'])
+
+if __name__ == "__main__":
     main()