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import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
# Load the advanced conversational model (e.g., GPT-NeoX-20B or GPT-J-6B)
model_name = "EleutherAI/gpt-j-6B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
chatbot_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, max_length=200)
# Define FAQs for instant responses
faqs = {
"How do I enroll in a course?": "To enroll, go to our website, select the course you're interested in, and click 'Enroll Now'. Follow the on-screen instructions.",
"What is the refund policy?": "We offer a full refund within the first 14 days if you are not satisfied with the course.",
"How can I access my course materials?": "Once you enroll, you can access course materials in the 'My Courses' section of your account.",
"Are there any live sessions available?": "Yes, many courses include live sessions. You can view the schedule in the course overview.",
"How can I get a certificate?": "Complete all modules and pass the final assessment with a minimum score of 70% to receive your certificate.",
# Add more FAQs as needed
}
# Function to generate a response with FAQs and model for other queries
def generate_response(user_input, history=[]):
# Check if input matches any FAQ
if user_input in faqs:
response = faqs[user_input]
else:
# Generate response using the model for non-FAQ queries
prompt = f"Student: {user_input}\nAssistant:"
model_response = chatbot_pipeline(prompt)[0]['generated_text']
response = model_response.split("Assistant:")[-1].strip()
history.append((user_input, response))
return "", history
# Real-time chatbot UI with Gradio
iface = gr.Interface(
fn=generate_response,
inputs=["text", "state"],
outputs=["text", "state"],
live=True,
title="Edutech Student Chatbot",
description="Ask me anything about courses, enrollment, certification, and more!",
theme="compact",
css=".gradio-container { background-color: #f5f5f5; font-family: Arial, sans-serif; }"
)
# Launch the chatbot interface
iface.launch()
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