Update app.py

app.py

@@ -1,8 +1,23 @@
 import streamlit as st
-from transformers import pipeline
-
-# Load the model with text-generation task
-chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium")
+import requests
+
+# Set your Hugging Face API key here
+API_KEY = 'your_huggingface_api_key'
+MODEL_URL = "https://api-inference.huggingface.co/models/microsoft/DialoGPT-medium"
+
+# Function to get chatbot response from Hugging Face API
+def query_huggingface_api(message):
+    headers = {"Authorization": f"Bearer {API_KEY}"}
+    payload = {"inputs": message}
+    
+    response = requests.post(MODEL_URL, headers=headers, json=payload)
+    response_json = response.json()
+    
+    # Extract and return the chatbot response
+    if isinstance(response_json, list):
+        return response_json[0]['generated_text']
+    else:
+        return "Error: Unable to get response from the model."
 
 # Set up Streamlit UI
 st.title("Learning Chatbot")
@@ -22,10 +37,10 @@ if user_message:
     # Combine the history for context in the conversation
     conversation_history = " ".join(st.session_state.history)
 
-    # Generate the response
-    bot_response = chatbot(conversation_history, max_length=1000, num_return_sequences=1)[0]['generated_text']
+    # Query the Hugging Face API to get the response
+    bot_response = query_huggingface_api(conversation_history)
 
-    # Display the bot's response
+    # Append the bot's response to the history
     st.session_state.history.append(f"Bot: {bot_response}")
 
     # Show conversation history