app.py

import gradio as gr
from transformers import pipeline
import PyPDF2
import json

# 📌 Step 1: Extract text 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")

# 📌 Step 2: Extract subjects and topics
def extract_subjects_and_topics(text):
    subjects = {}
    current_subject = None

    for line in text.split("\n"):
        line = line.strip()
        if line.isupper():  # Assuming subject names are in uppercase
            current_subject = line
            subjects[current_subject] = []
        elif current_subject and line:
            subjects[current_subject].append(line)

    return subjects

subjects_data = extract_subjects_and_topics(syllabus_text)

# 📌 Step 3: Convert to JSON format for easy searching
subjects_json = json.dumps(subjects_data, indent=4)

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

# 📌 Step 4: Chat Function
def chat_response(message):
    message = message.lower()

    # If user asks for subjects
    if "subjects" in message:
        return "📚 Available Subjects:\n\n" + "\n".join(subjects_data.keys())

    # If user asks for topics under a subject
    for subject, topics in subjects_data.items():
        if subject.lower() in message:
            return f"📖 Topics under {subject}:\n\n" + "\n".join(topics)

    # If chatbot response is needed
    response = chatbot(message, max_length=100, do_sample=True)
    return response[0]['generated_text']

# 📌 Step 5: Create Gradio Interface
iface = gr.Interface(
    fn=chat_response,
    inputs="text",
    outputs="text",
    title="Bit GPT 0.2.8",
    description="Ask me about syllabus subjects, topics, or general questions!"
)

# 📌 Step 6: Launch App
if __name__ == "__main__":
    iface.launch()