Update app.py

app.py

@@ -1,38 +1,30 @@
 import gradio as gr
-from transformers import pipeline
 
-# Set up the question-answering pipeline with DistilBERT
-qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
+def chatbot_response(user_input):
+    # Handle different questions
+    if user_input.lower() in ['hello', 'hi', 'hey']:
+        return "Hi there! How can I help you with your studies today?"
+    elif 'supervised learning' in user_input.lower():
+        return "Supervised learning is a machine learning approach where models are trained using labeled data."
+    elif 'help' in user_input.lower():
+        return "I'm here to assist with academic questions. Please specify what you'd like help with."
+    else:
+        return "I'm here to assist with academic questions. Please specify if you'd like help with any specific subject or topic."
 
-# Define the context for the chatbot to answer academic-related questions
-context = """
-You can ask me anything related to academic topics! I can help explain concepts from math, science, and other subjects. 
-For example, I can explain calculus, biology, physics, programming, and more! Please type in your question, and I will try to answer it as best as I can.
-"""
-
-# Function that uses the QA pipeline to answer questions
-def chatbot_response(question):
-    # Use the QA pipeline to answer the question based on the context
-    result = qa_pipeline(question=question, context=context)
-    return result["answer"]
-
-# Define the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("# Study Assistance Chatbot")
     gr.Markdown("Welcome! Ask me anything related to your academic studies.")
-    
+
     with gr.Row():
         with gr.Column():
             user_input = gr.Textbox(label="Enter your question here:")
             submit_button = gr.Button("Submit")
-        
+
         with gr.Column():
             chatbot_output = gr.Textbox(label="Chatbot Response", interactive=False)
-    
-    # Link the submit button to the chatbot response function
+
     submit_button.click(chatbot_response, inputs=user_input, outputs=chatbot_output)
 
-# Launch the Gradio app
 demo.launch()