app.py

import gradio as gr
import faiss
import numpy as np
import fitz
import docx
from pptx import Presentation

from sentence_transformers import SentenceTransformer

embed_model = SentenceTransformer(
    "sentence-transformers/all-MiniLM-L6-v2"
)

from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

gen_model_name="google/flan-t5-small"

gen_tokenizer=AutoTokenizer.from_pretrained(gen_model_name)
gen_model=AutoModelForSeq2SeqLM.from_pretrained(gen_model_name)


chunks_store=[]
index=None

def extract_text(file):

    name=file.name.lower()

    text=""

    if name.endswith(".pdf"):
        pdf=fitz.open(file.name)
        for page in pdf:
            text += page.get_text()

    elif name.endswith(".docx"):
        doc=docx.Document(file.name)
        for p in doc.paragraphs:
            text += p.text + "\n"

    elif name.endswith(".pptx"):
        prs=Presentation(file.name)

        for slide in prs.slides:
            for shape in slide.shapes:
                if hasattr(shape,"text"):
                    text += shape.text + "\n"

    else:
        text="Unsupported file format"

    return text
    
def build_kb(file):

    global chunks_store,index

    text=extract_text(file)

    if not text.strip():
        return "No text extracted from file."

    chunk_size=500
    overlap=100

    chunks=[]
    for i in range(
        0,
        len(text),
        chunk_size-overlap
    ):
        chunks.append(
            text[i:i+chunk_size]
        )

    if len(chunks)==0:
        return "No chunks created."

    chunks_store=chunks

    embeddings=embed_model.encode(chunks)

    dim=embeddings.shape[1]

    index=faiss.IndexFlatL2(dim)

    index.add(
      np.array(
          embeddings
      ).astype("float32")
    )

    return f"Knowledge Base Created with {len(chunks)} chunks"

def ask_question(question):

    global index, chunks_store

    if index is None:
        return "Upload knowledge first.",""

    q_emb = embed_model.encode([question])

    D,I=index.search(
        np.array(q_emb).astype("float32"),
        k=2
    )

    retrieved="\n\n".join(
        [chunks_store[i] for i in I[0]]
    )

    prompt=f"""
Use only the provided context.

If the answer is not found in the context, reply:
Information not found in document.

Context:
{retrieved}

Question:
{question}

Answer in one concise sentence:
"""

    inputs=gen_tokenizer(
        prompt,
        return_tensors="pt",
        truncation=True,
        max_length=512
    )

    outputs=gen_model.generate(
        **inputs,
        max_new_tokens=35,
        num_beams=4,
        do_sample=False,
        early_stopping=True
    )

    answer=gen_tokenizer.decode(
        outputs[0],
        skip_special_tokens=True
    ).strip()

    if "." in answer:
        answer = answer.split(".")[0] + "."

    return answer,retrieved


with gr.Blocks() as demo:

    gr.Markdown(
"""
# 📚 RAG Question Answering System
Ask questions over your own documents
"""
)

    with gr.Tab("Build Knowledge Base"):

        doc=gr.File(
            label="Upload PDF / DOCX / PPTX"
        )

        status=gr.Textbox(label="Status")

        build_btn=gr.Button("Create Knowledge Base")

        build_btn.click(
            build_kb,
            inputs=doc,
            outputs=status
        )


    with gr.Tab("Ask Questions"):

        question=gr.Textbox(
            label="Ask a Question"
        )

        answer=gr.Textbox(
            label="Grounded Answer",
            lines=6
        )

        sources=gr.Textbox(
            label="Sources",
            lines=8
        )

        ask_btn=gr.Button("Ask")

        ask_btn.click(
            ask_question,
            inputs=question,
            outputs=[answer,sources]
        )


demo.launch()