Update app.py

app.py

@@ -5,6 +5,7 @@ from langchain.embeddings import HuggingFaceEmbeddings
 from langchain.vectorstores import FAISS
 from langchain.llms import HuggingFaceHub
 from langchain.chains import ConversationalRetrievalChain
+from pathlib import Path
 
 # Load the HuggingFace language model and embeddings
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
@@ -16,29 +17,14 @@ embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-b
 vector_store = None
 retriever = None
 
-def update_documents(text_input):
+def update_documents(documents):
     global vector_store, retriever
-    # Split the input text into individual documents based on newlines or other delimiters
-    documents = text_input.split("\n")
-    
     # Update the FAISS vector store with new documents
     vector_store = FAISS.from_texts(documents, embeddings)
-    
-    # Set the retriever to use the new vector store
     retriever = vector_store.as_retriever()
     return f"{len(documents)} documents successfully added to the vector store."
 
-# Set up ConversationalRetrievalChain
-rag_chain = None
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
     global rag_chain, retriever
     
     if retriever is None:
@@ -68,27 +54,21 @@ def respond(
     # Return the model's response
     return response['answer']
 
-def upload_file(filepath):
-    name = Path(filepath).name
-    return [gr.UploadButton(visible=False), gr.DownloadButton(label=f"Download {name}", value=filepath, visible=True)]
-
-def download_file():
-    return [gr.UploadButton(visible=True), gr.DownloadButton(visible=False)]
+def upload_file(file):
+    text = file.read().decode("utf-8")  # Read file content
+    documents = text.split("\n")  # Split into documents
+    return update_documents(documents)
 
 # Gradio interface setup
 demo = gr.Blocks()
 
 with demo:
     with gr.Row():
-        # upload_button = gr.Button("Upload Documents")
-        with gr.Row():
-            u = gr.UploadButton("Upload a file", file_count="single")
-            d = gr.DownloadButton("Download the file", visible=False)
-
-    u.upload(upload_file, u, [u, d])
-    d.click(download_file, None, [u, d])
+        u = gr.UploadButton("Upload a file (txt)", file_count="single", file_types=[".txt"])
         
-    
+    # Process the uploaded file
+    u.upload(upload_file, u, gr.Textbox(label="Status", visible=True))
+
     with gr.Row():
         # Chat interface for the RAG system
         chat = gr.ChatInterface(
@@ -101,8 +81,5 @@ with demo:
             ],
         )
 
-    # Bind button to update the document vector store
-    # upload_button.click(update_documents, inputs=[doc_input], outputs=gr.Textbox(label="Status"))
-
 if __name__ == "__main__":
     demo.launch()