app.py

import gradio as gr
import google.generativeai as genai
from PyPDF2 import PdfReader
from paddleocr import PaddleOCR
import os

# Step 1: Gemini API Key (must be set in Hugging Face Secrets)
genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
model = genai.GenerativeModel('gemini-pro')

# Step 2: OCR Setup
ocr_model = PaddleOCR(use_angle_cls=True, lang='en')
documents = []

def extract_text(file):
    ext = os.path.splitext(file.name)[1].lower()
    text = ""
    if ext == ".pdf":
        reader = PdfReader(file)
        for page in reader.pages:
            text += page.extract_text() or ""
    elif ext in [".jpg", ".jpeg", ".png"]:
        result = ocr_model.ocr(file.name)
        text = " ".join([line[1][0] for line in result[0]])
    return text

def process_files(files):
    global documents
    documents = []
    for f in files:
        text = extract_text(f)
        documents.append({"filename": f.name, "text": text})
    return f"{len(files)} files processed and stored."

def answer_query(query):
    if not documents:
        return "Please upload and process files first."

    prompt = "You are a research assistant. Analyze the following documents and answer the query.\n"
    for i, doc in enumerate(documents):
        prompt += f"\nDocument {i+1} ({doc['filename']}):\n{doc['text'][:2000]}\n"
    prompt += f"\n\nQuestion: {query}\nAnswer with key themes and cite document numbers."

    response = model.generate_content(prompt)
    return response.text

# Step 3: Gradio Interface
with gr.Blocks() as demo:
    gr.Markdown("# 📄 Gemini Document Research & Theme Identification Chatbot")

    with gr.Row():
        file_input = gr.File(file_types=[".pdf", ".jpg", ".png"], file_count="multiple", label="Upload Documents")
        process_btn = gr.Button("Process Documents")

    process_output = gr.Textbox(label="Processing Status")

    with gr.Row():
        query_input = gr.Textbox(label="Ask a question")
        query_btn = gr.Button("Get Answer")

    answer_output = gr.Textbox(label="Answer with Themes and Citations", lines=10)

    process_btn.click(fn=process_files, inputs=[file_input], outputs=[process_output])
    query_btn.click(fn=answer_query, inputs=[query_input], outputs=[answer_output])

demo.launch()