app.py

import gradio as gr
from transformers import pipeline
import PyPDF2

# 📌 Load syllabus from PDF
def read_pdf(file_path):
    try:
        with open(file_path, "rb") as file:
            reader = PyPDF2.PdfReader(file)
            text = "\n".join([page.extract_text() for page in reader.pages if page.extract_text()])
        return text
    except Exception as e:
        return f"Error loading syllabus: {str(e)}"

syllabus_text = read_pdf("syllabus.pdf")

# 📌 Load AI Model
chatbot = pipeline("text-generation", model="facebook/blenderbot-400M-distill")

# 📌 Define Chat Function
def chat_response(message):
    if "syllabus" in message.lower():
        return syllabus_text
    response = chatbot(message, max_length=100, do_sample=True)
    return response[0]['generated_text']

# 📌 Create Gradio Interface
iface = gr.Interface(fn=chat_response, inputs="text", outputs="text", title="Bit GPT 0.2.8")

# 📌 Launch App
if __name__ == "__main__":
    iface.launch()