app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# ------------------------------
# Load Model & Tokenizer
# ------------------------------
model_name = "kaitchup/Llama-3.2-3B-Instruct-educational-chatbot"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.float16,
    device_map="auto"
)

# ------------------------------
# Define Chat Function
# ------------------------------
def chat_with_ai(user_input, history):
    history = history or []
    messages = [f"User: {msg[0]}\nAI: {msg[1]}" for msg in history]
    messages.append(f"User: {user_input}\nAI:")
    prompt = "\n".join(messages)

    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=256, temperature=0.7, top_p=0.9)
    reply = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Extract the latest AI response
    if "AI:" in reply:
        reply = reply.split("AI:")[-1].strip()

    history.append((user_input, reply))
    return reply, history

# ------------------------------
# Create Gradio Chat Interface
# ------------------------------
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("<h2 style='text-align:center'>🎓 EduBridge AI Chatbot</h2>")
    chatbot = gr.Chatbot()
    msg = gr.Textbox(placeholder="Ask me anything educational...")
    clear = gr.Button("Clear Chat")

    msg.submit(chat_with_ai, [msg, chatbot], [chatbot, chatbot])
    clear.click(lambda: None, None, chatbot, queue=False)

# ------------------------------
# Launch App
# ------------------------------
demo.launch()