Update app.py

app.py

@@ -1,9 +1,9 @@
-import os
-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
 
 # Load environment variables
 load_dotenv()
@@ -82,35 +82,34 @@ def handle_query(query):
 print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
 data_ingestion_from_directory()
 
-# Define the input and output components for the Gradio interface
-input_component = gr.Textbox(
-    label="User:",
-    placeholder="Type your message...",
-    lines=2
-)
-
-output_component = gr.Output(
-    label="Bot:",
-    type="text",
-    initial="Bot's response will appear here...",
-)
-
-# Function to handle queries
-def chatbot_handler(query):
-    with output_component:
-        response = handle_query(query)
-        print(f"User: {query}\nBot: {response}\n")
-        return response
-
-# Create the Gradio interface with chat-like settings
-interface = gr.Interface(
-    fn=chatbot_handler,
-    inputs=input_component,
-    outputs=output_component,
-    title="RedfernsTech Chatbot",
+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...", readonly=True),
+    title="RedFernsTech Chatbot",
     theme="compact",
     live=True  # Enables real-time updates
 )
 
 # Launch the Gradio interface
-interface.launch()
+demo.launch()