Update app.py

app.py

@@ -1,23 +1,22 @@
 import gradio as gr
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+from transformers import pipeline
 
-# Initialize pre-trained model and tokenizer
-model_name = "gpt2"  # You can change this to another model if needed
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+# Set up the question-answering pipeline with DistilBERT
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
 
-# Create a pipeline for text generation
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
+# 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.
+"""
 
-# Chatbot response function
-def chatbot_response(user_input):
-    # Generate a response using the model
-    response = generator(user_input, max_length=100, num_return_sequences=1, temperature=0.7, top_k=50)
-    
-    # Extract and return the generated text
-    return response[0]['generated_text']
+# 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"]
 
-# Create the Gradio interface
+# 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.")
@@ -26,14 +25,18 @@ with gr.Blocks() as demo:
         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()
 
 
 
 
 
+