app.py

import streamlit as st
from transformers import AutoModel, AutoTokenizer, Qwen2VLForConditionalGeneration, AutoProcessor
from surya.ocr import run_ocr
from surya.model.detection.model import load_model as load_det_model, load_processor as load_det_processor
from surya.model.recognition.model import load_model as load_rec_model
from surya.model.recognition.processor import load_processor as load_rec_processor
from PIL import Image
import torch
import tempfile
import os
import re
import json
import base64
from groq import Groq

# Page configuration
st.set_page_config(page_title="DualTextOCRFusion", page_icon="🔍", layout="wide")
device = "cuda" if torch.cuda.is_available() else "cpu"

# Load Surya OCR Models (English + Hindi)
det_processor, det_model = load_det_processor(), load_det_model()
det_model.to(device)
rec_model, rec_processor = load_rec_model(), load_rec_processor()
rec_model.to(device)

# Load GOT Models
@st.cache_resource
def init_got_model():
    tokenizer = AutoTokenizer.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True)
    model = AutoModel.from_pretrained('srimanth-d/GOT_CPU', trust_remote_code=True, use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
    return model.eval(), tokenizer

@st.cache_resource
def init_got_gpu_model():
    tokenizer = AutoTokenizer.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True)
    model = AutoModel.from_pretrained('ucaslcl/GOT-OCR2_0', trust_remote_code=True, low_cpu_mem_usage=True, device_map='cuda', use_safetensors=True, pad_token_id=tokenizer.eos_token_id)
    return model.eval().cuda(), tokenizer

# Load Qwen Model
@st.cache_resource
def init_qwen_model():
    model = Qwen2VLForConditionalGeneration.from_pretrained("Qwen/Qwen2-VL-2B-Instruct", device_map="cpu", torch_dtype=torch.float16)
    processor = AutoProcessor.from_pretrained("Qwen/Qwen2-VL-2B-Instruct")
    return model.eval(), processor

# Text Cleaning AI - Clean spaces, handle dual languages
def clean_extracted_text(text):
    cleaned_text = re.sub(r'\s+', ' ', text).strip()
    cleaned_text = re.sub(r'\s([?.!,])', r'\1', cleaned_text)
    return cleaned_text

# Polish the text using a model
def polish_text_with_ai(cleaned_text):
    prompt = f"Remove unwanted spaces between and inside words to join incomplete words, creating a meaningful sentence in either Hindi, English, or Hinglish without altering any words from the given extracted text. Then, return the corrected text with adjusted spaces, keeping it as close to the original as possible."
    client = Groq(api_key="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")
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": "You are a pedantic sentence corrector. Remove extra spaces between and within words to make the sentence meaningful in English, Hindi, or Hinglish, according to the context of the sentence, without changing any words."
            },
            {
                "role": "user",
                "content": prompt,
            }
        ],
        model="gemma2-9b-it",
    )
    polished_text = chat_completion.choices[0].message.content
    return polished_text

# Extract text using GOT
def extract_text_got(image_file, model, tokenizer):
    return model.chat(tokenizer, image_file, ocr_type='ocr')

# Extract text using Qwen
def extract_text_qwen(image_file, model, processor):
    try:
        image = Image.open(image_file).convert('RGB')
        conversation = [{"role": "user", "content": [{"type": "image"}, {"type": "text", "text": "Extract text from this image."}]}]
        text_prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
        inputs = processor(text=[text_prompt], images=[image], return_tensors="pt")
        output_ids = model.generate(**inputs)
        output_text = processor.batch_decode(output_ids, skip_special_tokens=True)
        return output_text[0] if output_text else "No text extracted from the image."
    except Exception as e:
        return f"An error occurred: {str(e)}"

# Highlight keyword search
def highlight_text(text, search_term):
    if not search_term:
        return text
    pattern = re.compile(re.escape(search_term), re.IGNORECASE)
    return pattern.sub(lambda m: f'<span style="background-color: yellow;">{m.group()}</span>', text)

# Title and UI
st.title("DualTextOCRFusion - 🔍")
st.header("OCR Application - Multimodel Support")
st.write("Upload an image for OCR using various models, with support for English, Hindi, and Hinglish.")

# Sidebar Configuration
st.sidebar.header("Configuration")
model_choice = st.sidebar.selectbox("Select OCR Model:", ("GOT_CPU", "GOT_GPU", "Qwen", "Surya (English+Hindi)"))

# Upload Section
uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["png", "jpg", "jpeg"])

# Input from clipboard
if st.sidebar.button("Paste from Clipboard"):
    try:
        clipboard_data = st.experimental_get_clipboard()
        if clipboard_data:
            image_data = base64.b64decode(clipboard_data)
            uploaded_file = tempfile.NamedTemporaryFile(delete=False, suffix=".png")
            uploaded_file.write(image_data)
            uploaded_file.seek(0)
    except Exception as e:
        st.sidebar.warning(f"Clipboard data is not an image or an error occurred: {str(e)}")

# Input from camera
camera_file = st.sidebar.camera_input("Capture from Camera")
if camera_file:
    uploaded_file = camera_file

# Predict button
predict_button = st.sidebar.button("Predict")

# Main columns
col1, col2 = st.columns([2, 1])

# Display image preview
if uploaded_file:
    image = Image.open(uploaded_file)
    with col1:
        col1.image(image, caption='Uploaded Image', use_column_width=False, width=300)

    # Save uploaded image to 'images' folder
    images_dir = 'images'
    os.makedirs(images_dir, exist_ok=True)
    image_path = os.path.join(images_dir, uploaded_file.name)
    with open(image_path, 'wb') as f:
        f.write(uploaded_file.getvalue())

    # Check if the result already exists
    results_dir = 'results'
    os.makedirs(results_dir, exist_ok=True)
    result_path = os.path.join(results_dir, f"{uploaded_file.name}_result.json")

    # Handle predictions
    if predict_button:
        if os.path.exists(result_path):
            with open(result_path, 'r') as f:
                result_data = json.load(f)
            extracted_text = result_data["polished_text"]
        else:
            with st.spinner("Processing..."):
                if model_choice == "GOT_CPU":
                    got_model, tokenizer = init_got_model()
                    extracted_text = extract_text_got(image_path, got_model, tokenizer)

                elif model_choice == "GOT_GPU":
                    got_gpu_model, tokenizer = init_got_gpu_model()
                    extracted_text = extract_text_got(image_path, got_gpu_model, tokenizer)

                elif model_choice == "Qwen":
                    qwen_model, qwen_processor = init_qwen_model()
                    extracted_text = extract_text_qwen(image_path, qwen_model, qwen_processor)

                elif model_choice == "Surya (English+Hindi)":
                    langs = ["en", "hi"]
                    predictions = run_ocr([image], [langs], det_model, det_processor, rec_model, rec_processor)
                    text_list = re.findall(r"text='(.*?)'", str(predictions[0]))
                    extracted_text = ' '.join(text_list)

                # Clean and polish extracted text
                cleaned_text = clean_extracted_text(extracted_text)
                polished_text = polish_text_with_ai(cleaned_text) if model_choice in ["GOT_CPU", "GOT_GPU"] else cleaned_text

                # Save results to JSON file
                result_data = {"polished_text": polished_text}
                with open(result_path, 'w') as f:
                    json.dump(result_data, f)

        # Display extracted text
        st.subheader("Extracted Text (Cleaned & Polished)")
        st.markdown(extracted_text, unsafe_allow_html=True)

        # Search functionality
        def update_search():
            if search_query:
                highlighted_text = highlight_text(extracted_text, search_query)
                st.session_state["highlighted_result"] = highlighted_text
            else:
                st.session_state["highlighted_result"] = extracted_text

        search_query = st.text_input(
            "Search in extracted text:",
            key="search_query",
            placeholder="Type to search...",
            on_change=update_search,
            disabled=not uploaded_file
        )

        if "highlighted_result" in st.session_state:
            st.markdown("### Highlighted Search Results:")
            st.markdown(st.session_state["highlighted_result"], unsafe_allow_html=True)