app.py

import textwrap
import warnings

import faiss
import numpy as np
import torch

warnings.filterwarnings("ignore")
import gradio as gr
import pytesseract
from pdf2image import convert_from_path
from pdfminer.high_level import extract_text
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer


# ================== PDF Handling Functions ==================
def pdf_to_text(path):
    try:
        txt = extract_text(path) or ""
    except Exception:
        txt = ""
    if len(txt.strip()) < 200:
        try:
            pages = convert_from_path(path, dpi=200)
            ocr_all = [pytesseract.image_to_string(img) for img in pages]
            txt = "\n".join(ocr_all)
        except Exception:
            txt = ""
    return txt

def chunk_text(text, max_chars=800):
    paras = [p.strip() for p in text.split("\n") if p.strip()]
    chunks, buf = [], ""
    for p in paras:
        if len(p) > max_chars:
            for piece in textwrap.wrap(p, width=max_chars, break_long_words=False):
                chunks.append(piece.strip())
        else:
            if len(buf) + len(p) + 1 <= max_chars:
                buf = (buf + "\n" + p).strip()
            else:
                if buf: chunks.append(buf)
                buf = p
    if buf: chunks.append(buf)
    return [c for c in chunks if len(c) > 80]

# ================== Load Embeddings + Model ==================
embed_model = SentenceTransformer("all-MiniLM-L6-v2")

model_id = "google/flan-t5-base"
tok = AutoTokenizer.from_pretrained(model_id)
gen_model = AutoModelForSeq2SeqLM.from_pretrained(model_id)
device = "cuda" if torch.cuda.is_available() else "cpu"
gen_model.to(device)

# ================== Chat Function ==================
def chat_fn(message, history=None):
    prompt = f"Answer clearly and exam-ready:\n\nQuestion:\n{message}"
    inputs = tok(prompt, return_tensors="pt", truncation=True, padding=True, max_length=1024).to(device)
    out = gen_model.generate(**inputs, max_new_tokens=120, num_beams=4, do_sample=False)
    return tok.decode(out[0], skip_special_tokens=True).strip()

# ================== Gradio Interface ==================
iface = gr.ChatInterface(
    fn=chat_fn,
    title="💬 Practical Chatbot",
    description="Ask about Physics & Chemistry Practicals (Class 9–10)."
)

iface.launch()