Create app.py

app.py

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+!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