Update app.py

app.py

@@ -1,9 +1,9 @@
+from dotenv import load_dotenv
 import gradio as gr
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
 from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from llama_index.core import ChatPromptTemplate, Settings, StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader
 from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-from dotenv import load_dotenv
-import os
+from sentence_transformers import SentenceTransformer
 
 # Load environment variables
 load_dotenv()
@@ -82,34 +82,27 @@ def handle_query(query):
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
-def predict(message, history):
-    messages = [{"role": "system", "content": "You are a helpful assistant."}]
-    for user_message, bot_message in history:
-        if user_message:
-            messages.append({"role": "user", "content": user_message})
-        if bot_message:
-            messages.append({"role": "assistant", "content": bot_message})
-    messages.append({"role": "user", "content": message})
-    
-    response = ""
-    for chunk in Settings.llm.create_chat_completion(
-        stream=True,
-        messages=messages,
-    ):
-        part = chunk["choices"][0]["delta"].get("content", None)
-        if part:
-            response += part
-        yield response
-
-# Create a Gradio chat interface
-demo = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(label="User Input", placeholder="Type your message here..."),
-    outputs=gr.Textbox(label="Bot Response", placeholder="Bot's response will appear here..."),
-    title="RedFernsTech Chatbot",
-    theme="compact",
-    live=True  # Enables real-time updates
+# Define the input and output components for the Gradio interface
+input_component = gr.Textbox(
+    show_label=False,
+    placeholder="Ask me anything about the document..."
+)
+
+output_component = gr.Textbox()
+
+# Function to handle queries
+def chatbot_handler(query):
+    response = handle_query(query)
+    return response
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=chatbot_handler,
+    inputs=input_component,
+    outputs=output_component,
+    title="RedfernsTech Q&A Chatbot",
+    description="Ask me anything about the uploaded document."
 )
 
 # Launch the Gradio interface
-demo.launch()
+interface.launch(share=True)