Create app.py

app.py

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+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import JSONResponse
+from transformers import AutoProcessor, AutoModelForCausalLM
+from PIL import Image
+import torch
+import io
+import logging
+
+# Set up logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# --- 1. Initialize FastAPI App ---
+app = FastAPI(title="Mixed-Content OCR API", description="An API to extract text from images containing both printed and handwritten text.")
+
+# --- 2. Load the Model and Processor (at startup) ---
+# This is a critical step. We load the model only once when the app starts.
+# This prevents reloading the model on every API call, which would be very slow.
+try:
+    logger.info("Loading model and processor...")
+    # Use the large model for better accuracy
+    model_id = "microsoft/Florence-2-large"
+    # NOTE: We need to trust remote code for Florence-2
+    model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True)
+    processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
+    logger.info("Model and processor loaded successfully.")
+except Exception as e:
+    logger.error(f"Error loading model: {e}")
+    # If the model fails to load, the API is not usable. We can't proceed.
+    model = None
+    processor = None
+
+# --- 3. Define the OCR Task Function ---
+def run_ocr(image: Image.Image) -> str:
+    """
+    Performs OCR on a given PIL Image using the Florence-2 model.
+    """
+    if model is None or processor is None:
+        raise RuntimeError("Model is not available. Check logs for loading errors.")
+
+    # Ensure image is in RGB format
+    if image.mode != "RGB":
+        image = image.convert("RGB")
+
+    # Define the task prompt
+    prompt = "<OCR>"
+    
+    # Preprocess the image and prompt
+    inputs = processor(text=prompt, images=image, return_tensors="pt")
+    
+    # Generate text from the image
+    # Note: max_new_tokens can be adjusted based on expected text length
+    generated_ids = model.generate(
+        input_ids=inputs["input_ids"],
+        pixel_values=inputs["pixel_values"],
+        max_new_tokens=4096, # Increased token limit for long documents
+        do_sample=False, # Use greedy decoding for deterministic output
+        num_beams=3 # Use beam search for potentially better results
+    )
+    
+    # Decode the generated IDs to a string
+    generated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]
+    
+    # Post-process the output to get the clean text
+    # The model's output for OCR is typically in the format: <OCR>extracted_text</s>
+    parsed_text = processor.post_process_generation(generated_text, task="<OCR>", image_size=(image.width, image.height))
+    
+    return parsed_text.get("<OCR>", "Error: Could not parse OCR output.")
+
+
+# --- 4. Create the API Endpoint ---
+@app.post("/ocr", summary="Extract Text from Image")
+async def perform_ocr(file: UploadFile = File(..., description="Image file to perform OCR on.")):
+    """
+    Takes an image file, extracts both printed and handwritten text,
+    and returns it as a JSON object.
+    """
+    if model is None:
+        raise HTTPException(status_code=503, detail="Model is not loaded or unavailable.")
+
+    # Validate file type
+    if not file.content_type.startswith("image/"):
+        raise HTTPException(status_code=400, detail="Invalid file type. Please upload an image.")
+    
+    try:
+        # Read the image content from the uploaded file
+        contents = await file.read()
+        image = Image.open(io.BytesIO(contents))
+        
+        # Run the OCR task
+        logger.info("Running OCR on the uploaded image...")
+        extracted_text = run_ocr(image)
+        logger.info("OCR completed successfully.")
+        
+        # Return the result
+        return JSONResponse(
+            content={"filename": file.filename, "text": extracted_text}
+        )
+        
+    except Exception as e:
+        logger.error(f"An error occurred during OCR processing: {e}")
+        raise HTTPException(status_code=500, detail=f"An internal error occurred: {str(e)}")
+
+@app.get("/", summary="Health Check")
+def read_root():
+    """
+    A simple health check endpoint to confirm the API is running.
+    """
+    return {"status": "ok", "model_loaded": model is not None}