Update app.py

app.py

@@ -1,7 +1,6 @@
 import os
 import shutil
 import gradio as gr
-from qdrant_client.http import models
 import qdrant_client
 from getpass import getpass
 
@@ -63,120 +62,9 @@ def process_upload(files):
     global client, vector_store, storage_context, index, query_engine, memory, chat_engine
     client = qdrant_client.QdrantClient(location=":memory:")
     
-    # vector_store = QdrantVectorStore(
-    #     collection_name="paper",
-    #     client=client,
-    #     enable_hybrid=True,
-    #     batch_size=20,
-    # )
-    collection_name = "paper"
-    # client = qdrant_client.QdrantClient(
-    #     path="./qdrant_db",  
-    #     prefer_grpc=True
-    # )
-    existing_collections = {col.name for col in client.get_collections().collections}
-    if collection_name not in existing_collections:
-        client.create_collection(
-            collection_name=collection_name,
-            vectors_config=models.VectorParams(
-                size=1536,  
-                distance=models.Distance.COSINE
-            )
-        )
     vector_store = QdrantVectorStore(
+        collection_name="paper",
         client=client,
-        collection_name=collection_name,
-    )
-    
-    storage_context = StorageContext.from_defaults(vector_store=vector_store)
-    
-    index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
-    
-    query_engine = index.as_query_engine(vector_store_query_mode="hybrid")
-    
-    memory = ChatMemoryBuffer.from_defaults(token_limit=3000)
-    
-    chat_engine = index.as_chat_engine(
-        chat_mode="context",
-        memory=memory,
-        system_prompt=(
-            "You are an AI assistant who answers the user questions, "
-            "use the schema fields to generate appropriate and valid json queries"
-        ),
-    )
-    
-    return "Documents uploaded and index built successfully!"
-
-# -------------------------------------------------------
-# Chat function that uses the built chat engine.
-# -------------------------------------------------------
-def chat_with_ai(user_input, chat_history):
-    global chat_engine
-    # Check if the chat engine is initialized.
-    if chat_engine is None:
-        return chat_history, "Please upload documents first."
-    
-    response = chat_engine.chat(user_input)
-    references = response.source_nodes
-    ref, pages = [], []
-    
-    # Extract file names from the source nodes (if available)
-    for node in references:
-        file_name = node.metadata.get('file_name')
-        if file_name and file_name not in ref:
-            ref.append(file_name)
-    
-    complete_response = str(response) + "\n\n"
-    if ref or pages:
-        chat_history.append((user_input, complete_response))
-    else:
-        chat_history.append((user_input, str(response)))
-    return chat_history, ""
-
-# -------------------------------------------------------
-# Function to clear the chat history.
-# -------------------------------------------------------
-def clear_history():
-    return [], ""
-
-# -------------------------------------------------------
-# Build the Gradio interface.
-# -------------------------------------------------------
-def gradio_interface():
-    with gr.Blocks() as demo:
-        gr.Markdown("# Chat Interface for LlamaIndex with File Upload")
-        
-        # Use Tabs to separate the file upload and chat interfaces.
-        with gr.Tab("Upload Documents"):
-            gr.Markdown("Upload PDF, Excel, CSV, DOC/DOCX, or TXT files below:")
-            # The file upload widget: we specify allowed file types.
-            file_upload = gr.File(
-                label="Upload Files",
-                file_count="multiple",
-                file_types=[".pdf", ".csv", ".txt", ".xlsx", ".xls", ".doc", ".docx"],
-                type="filepath"  # returns file paths
-            )
-            upload_status = gr.Textbox(label="Upload Status", interactive=False)
-            upload_button = gr.Button("Process Upload")
-            
-            upload_button.click(process_upload, inputs=file_upload, outputs=upload_status)
-        
-        with gr.Tab("Chat"):
-            chatbot = gr.Chatbot(label="LlamaIndex Chatbot")
-            user_input = gr.Textbox(
-                placeholder="Ask a question...", label="Enter your question"
-            )
-            submit_button = gr.Button("Send")
-            btn_clear = gr.Button("Clear History")
-            
-            # A State to hold the chat history.
-            chat_history = gr.State([])
-            
-            submit_button.click(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
-            user_input.submit(chat_with_ai, inputs=[user_input, chat_history], outputs=[chatbot, user_input])
-            btn_clear.click(clear_history, outputs=[chatbot, user_input])
-    
-    return demo
-
-# Launch the Gradio app.
-gradio_interface().launch(debug=True)
+        enable_hybrid=True,
+        batch_size=20,
+    )