Update app.py

app.py

@@ -1,56 +1,37 @@
 import gradio as gr
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# 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)
+
+# Create a pipeline for text generation
+generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
+# Chatbot response function
 def chatbot_response(user_input):
-    # Handle basic greeting
-    if user_input.lower() in ["hello", "hi"]:
-        return "Hello! How can I assist you today?"
-    
-    # Add more conditions for different queries here
-    elif "supervised learning" in user_input.lower():
-        return "Supervised learning is a machine learning approach where models are trained using labeled data."
+    # Generate a response using the model
+    response = generator(user_input, max_length=100, num_return_sequences=1, temperature=0.7, top_k=50)
     
-    # If no predefined match, ask for more clarification
-    else:
-        return "I'm here to assist with academic questions. Please specify what you'd like help with."
+    # Extract and return the generated text
+    return response[0]['generated_text']
 
+# Create the Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("### Study Assistance Chatbot")
+    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)
-    
+
     submit_button.click(chatbot_response, inputs=user_input, outputs=chatbot_output)
 
 demo.launch()
-from datasets import load_dataset
-
-# Load a sample dataset from Hugging Face
-dataset = load_dataset("squad")  # you can replace "squad" with any dataset you're using
-
-# Print the first few entries to verify that it’s loaded
-print(dataset["train"][0])  # Prints the first example from the training set
-from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-# Load pre-trained GPT-2 model and tokenizer from Hugging Face
-model_name = "gpt2"  # You can use other models such as 'distilgpt2' for faster responses
-
-# Initialize tokenizer and model
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
-
-# Create a pipeline for text generation
-generator = pipeline("text-generation", model=model, tokenizer=tokenizer)
-def chatbot_response(user_input):
-    # Generate a response using the model
-    response = generator(user_input, max_length=100, num_return_sequences=1)
-    
-    # Extract and return the generated text
-    return response[0]['generated_text']