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import os
import torch
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
from PIL import Image
import io
import torchvision.transforms as transforms
import torch.nn.functional as F
import string
import tensorflow as tf
import keras
# Initialize FastAPI app
app = FastAPI()
# Load your custom model (replace this with your actual model loading code)
# Example: Loading a PyTorch model
model = tensorflow.keras.load_model.load("path_to_your_custom_model.pth")
model.eval() # Set the model to evaluation mode
# Preprocessing function (adjust according to your model's input requirements)
transform = transforms.Compose([
transforms.Resize((32, 128)), # Resize image to match your model input size
transforms.Grayscale(num_output_channels=1), # If your model takes grayscale images
transforms.ToTensor(),
transforms.Normalize(mean=[0.5], std=[0.5]) # Example normalization, adjust accordingly
])
# Endpoint to upload an image for OCR processing
@app.post("/uploadfile/")
async def upload_file(file: UploadFile = File(...)):
try:
# Read the image from the uploaded file
image_data = await file.read()
image = Image.open(io.BytesIO(image_data))
# Preprocess the image for the model
image = transform(image).unsqueeze(0) # Add batch dimension
# Perform OCR using the custom model
with torch.no_grad(): # Turn off gradients during inference
output = model(image) # Get model predictions
# Assuming your model outputs the predicted text as a tensor (you may need to decode this output)
# Example: decoding the predicted tensor into text
predicted_text = decode_output(output) # Replace this with your actual decoding function
# Return the OCR result as a JSON response
return JSONResponse(content={"extracted_text": predicted_text})
except Exception as e:
return JSONResponse(status_code=400, content={"error": str(e)})
def decode_output(output):
# Assuming the model outputs logits (unnormalized probabilities)
output = F.log_softmax(output, dim=2)
output = output.squeeze(0) # Remove batch dimension
# Get the predicted characters
_, predicted_indices = torch.max(output, dim=1)
# Convert indices to characters (assuming your model outputs indices for characters)
alphabet = string.ascii_lowercase + string.digits + " " # Modify this as per your model's character set
predicted_text = ''.join([alphabet[i] for i in predicted_indices])
return predicted_text
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