app.py

!pip install transformers torch torchvision timm easyocr pytesseract gradio datasets huggingface_hub

import gradio as gr
import torch
from transformers import TrOCRProcessor, VisionEncoderDecoderModel, pipeline
from PIL import Image
import requests

# ✅ Load TrOCR model (Pretrained on Handwritten OCR)
MODEL_NAME = "microsoft/trocr-base-handwritten"

# ✅ Check if GPU is available
device = "cuda" if torch.cuda.is_available() else "cpu"

# ✅ Cache the model to prevent reloading on every request
processor = TrOCRProcessor.from_pretrained(MODEL_NAME)
model = VisionEncoderDecoderModel.from_pretrained(MODEL_NAME).to(device)

# ✅ Function to extract text
def extract_text(image):
    image = Image.open(image).convert("RGB")
    
    # Convert Image to Model Format
    pixel_values = processor(images=image, return_tensors="pt").pixel_values.to(device)

    # Generate Text from Model
    generated_ids = model.generate(pixel_values)
    extracted_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
    
    return extracted_text

# ✅ Load NLP Pipeline for Structuring
nlp_pipeline = pipeline("ner", model="dslim/bert-base-NER")

# ✅ Function to Structure Extracted Text
def structure_text(text):
    ner_results = nlp_pipeline(text)
    structured_output = []
    for entity in ner_results:
        structured_output.append(f"{entity['word']} ({entity['entity']})")
    return " ".join(structured_output)

# ✅ Function to process document (OCR + NLP)
def process_document(image):
    extracted_text = extract_text(image)
    structured_text = structure_text(extracted_text)
    return extracted_text, structured_text

# ✅ Launch Gradio App
iface = gr.Interface(
    fn=process_document,
    inputs="image",
    outputs=["text", "text"],
    title="TransformoDocs - AI Document Processor",
    description="Upload a scanned document or handwritten note. The AI will extract and structure the text.",
)

iface.launch(share=True)  # ✅ Use 'share=True' for public link