Update app.py

app.py

@@ -1,14 +1,10 @@
 import streamlit as st
 from transformers import pipeline
 import openai
-import os
+import requests
 
-# Set Hugging Face and OpenAI API keys
-HUGGING_FACE_TOKEN = "Y	hf_...VyhC"  # Replace with your actual Hugging Face token
-OPENAI_API_KEY = "sk-...1-AA"  # Replace with your actual OpenAI API key
-
-# Authenticate with OpenAI API
-openai.api_key = OPENAI_API_KEY
+# Initialize OpenAI API key
+openai.api_key = "sk-...1-AA"  # Replace with your actual OpenAI API key
 
 # Function to get responses from OpenAI
 def get_chat_response(query):
@@ -18,24 +14,18 @@ def get_chat_response(query):
             {"role": "user", "content": query}
         ]
     )
-    return response.choices[0].message['content']
+    return response['choices'][0]['message']['content']
 
-# Class for the local chatbot using Hugging Face Transformers
 class StudyAssistantChatbot:
     def __init__(self):
-        # Load the local model using Hugging Face Transformers
+        # Check if either TensorFlow or PyTorch is installed
         try:
-            self.qa_pipeline = pipeline("text-generation", model="distilgpt2", use_auth_token=HUGGING_FACE_TOKEN)
+            self.qa_pipeline = pipeline("text-generation", model="distilgpt2")
         except RuntimeError as e:
             st.error(f"Error loading the model: {e}")
-            st.error("Please ensure either TensorFlow or PyTorch is installed.")
+            st.error("Please make sure either TensorFlow or PyTorch is installed.")
             raise
 
-    def generate_study_tips(self, query):
-        # Generate study tips using the local model
-        response = self.qa_pipeline(query, max_length=50, num_return_sequences=1)
-        return response[0]['generated_text']
-
 # Initialize Streamlit app
 st.title("Personalized Study Assistant Chatbot")
 
@@ -52,22 +42,11 @@ if st.button("Get Tips and Resources"):
     if query:
         # Get response from OpenAI
         response = get_chat_response(query)
-        if response:
-            st.subheader("OpenAI GPT Response:")
-            st.write(response)
-        else:
-            st.write("Unable to get a response from OpenAI at the moment.")
-        
-        # Get study tips from the local model
-        st.subheader("Study Tips from Local Model:")
-        tips_response = chatbot.generate_study_tips(query)
-        if tips_response:
-            st.write(tips_response)
-        else:
-            st.write("Unable to generate study tips at the moment.")
+        st.write(response)
     else:
         st.write("Please enter a question to get started!")
 
-# Add a sidebar for additional information
+# Add a sidebar for additional options
 st.sidebar.header("About")
-st.sidebar.text("This is a personalized study assistant chatbot that provides responses from OpenAI GPT and study tips generated locally.")
+st.sidebar.text("This is a personalized study assistant chatbot.")
+