import os
from transformers import AutoModel, AutoTokenizer
import torch

# Load model and tokenizer
# model_name = "ucaslcl/GOT-OCR2_0"
model_name = "srimanth-d/GOT_CPU"
tokenizer = AutoTokenizer.from_pretrained(
    model_name, trust_remote_code=True, return_tensors='pt'
)

# Load the model
model = AutoModel.from_pretrained(
    model_name,
    trust_remote_code=True,
    low_cpu_mem_usage=True,
    use_safetensors=True,
    pad_token_id=tokenizer.eos_token_id,
)

# Ensure the model is in evaluation mode and loaded on CPU
device = torch.device("cpu")
dtype = torch.float32  # Use float32 on CPU
model = model.eval()

# OCR function
def extract_text_got(uploaded_file):
    """Use GOT-OCR2.0 model to extract text from the uploaded image."""
    temp_file_path = 'temp_image.jpg'
    
    try:
        # Save the uploaded file temporarily
        with open(temp_file_path, 'wb') as temp_file:
            temp_file.write(uploaded_file.read())

        print(f"Processing image from path: {temp_file_path}")  # Debug info

        ocr_types = ['ocr', 'format']
        fine_grained_options = ['ocr', 'format']
        color_options = ['red', 'green', 'blue']
        box = [10, 10, 100, 100]  # Example box for demonstration
        multi_crop_types = ['ocr', 'format']

        results = []

        # Run basic OCR types
        for ocr_type in ocr_types:
            with torch.no_grad():
                print(f"Running basic OCR with type: {ocr_type}")  # Debug info
                outputs = model.chat(tokenizer, temp_file_path, ocr_type=ocr_type)
                
                # Debug outputs
                print(f"Outputs for {ocr_type}: {outputs}")
                
                if isinstance(outputs, list) and outputs[0].strip():
                    return outputs[0].strip()  # Return if successful
                results.append(outputs[0].strip() if outputs else "No result")

        # Try FINE-GRAINED OCR with box options
        for ocr_type in fine_grained_options:
            with torch.no_grad():
                print(f"Running fine-grained OCR with box, type: {ocr_type}")  # Debug info
                outputs = model.chat(tokenizer, temp_file_path, ocr_type=ocr_type, ocr_box=box)
                
                print(f"Outputs for {ocr_type} with box: {outputs}")
                
                if isinstance(outputs, list) and outputs[0].strip():
                    return outputs[0].strip()  # Return if successful
                results.append(outputs[0].strip() if outputs else "No result")

        # Try FINE-GRAINED OCR with color options
        for ocr_type in fine_grained_options:
            for color in color_options:
                with torch.no_grad():
                    print(f"Running fine-grained OCR with color {color}, type: {ocr_type}")  # Debug info
                    outputs = model.chat(tokenizer, temp_file_path, ocr_type=ocr_type, ocr_color=color)
                    
                    print(f"Outputs for {ocr_type} with color {color}: {outputs}")
                    
                    if isinstance(outputs, list) and outputs[0].strip():
                        return outputs[0].strip()  # Return if successful
                    results.append(outputs[0].strip() if outputs else "No result")

        # Try MULTI-CROP OCR
        for ocr_type in multi_crop_types:
            with torch.no_grad():
                print(f"Running multi-crop OCR with type: {ocr_type}")  # Debug info
                outputs = model.chat_crop(tokenizer, temp_file_path, ocr_type=ocr_type)
                
                print(f"Outputs for multi-crop {ocr_type}: {outputs}")
                
                if isinstance(outputs, list) and outputs[0].strip():
                    return outputs[0].strip()  # Return if successful
                results.append(outputs[0].strip() if outputs else "No result")

        # Return combined results or no text found message
        if all(not text for text in results):
            return "No text extracted."
        else:
            return "\n".join(results)

    except Exception as e:
        return f"Error during text extraction: {str(e)}"

    finally:
        # Clean up temporary file
        if os.path.exists(temp_file_path):
            os.remove(temp_file_path)
            print(f"Temporary file {temp_file_path} removed.")  # Debug info