removed duplicate main.py

image.txt

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-xiit edictum a caesare augusto: ut de scribe
-retur uniuersus orbis. Haec descriptio pri
-ma. facta est apraeside syriae cyrino
-tibant omnes: ut profiterentur singuli
-in suam ciuitatem Ascendit autẽ et iosepb
-agalilea deciuitate natareth. in iudacam
-ciuitatem dauid quae uocatur bethleem:
-Oquod esset dedomo et familia dauid.
-ut proficeretuir cummarta de sponsata sibi
-uxorte praegnantes. Pactum est autemcũ
-essent ibi : impleti sunt dies ute pareret
-t peperit filium suum primogenitũ. et
-pannis eum in uoluit. et reclinacuit cum in
-presepio. quia non erat eis bocus in diuerso
-rio. Et pastores erant in negione eadem.
-tugilantes et custodientes uigilias noctis.
-supra gregem suum. Et eoce angelus dñi
-stetit. iuxta illos. et claritas dicincum ful
-sit illos: et timuerunt timore magno
-Etdixit illis angelus. Nolite timere. Ecce

main.py

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-from fastapi import FastAPI, UploadFile, File
-from fastapi.responses import JSONResponse
-import subprocess
-import json
-import os
-import tempfile
-import shutil
-from pydantic import BaseModel
-import torch
-
-app = FastAPI()
-
-class LineDetectionResponse(BaseModel):
-    lines: list
-
-@app.post("/detect_lines", response_model=LineDetectionResponse)
-async def detect_lines(file: UploadFile = File(...)):
-    # Check if CUDA is available
-    device = "cuda" if torch.cuda.is_available() else "cpu"
-    
-    # Create a temporary directory
-    with tempfile.TemporaryDirectory() as temp_dir:
-        # Save the uploaded file
-        temp_file_path = os.path.join(temp_dir, file.filename)
-        with open(temp_file_path, "wb") as buffer:
-            shutil.copyfileobj(file.file, buffer)
-        
-        # Set up the output JSON path
-        lines_json_path = os.path.join(temp_dir, "lines.json")
-        
-        # Run Kraken for line detection
-        kraken_command = f"kraken -i {temp_file_path} {lines_json_path} segment -bl --device {device}"
-        subprocess.run(kraken_command, shell=True, check=True)
-        
-        # Load the lines from the JSON file
-        with open(lines_json_path, 'r') as f:
-            lines_data = json.load(f)
-    
-    # Return the lines data
-    return LineDetectionResponse(lines=lines_data['lines'])
-
-# Optionally, you can add a root endpoint for basic information
-@app.get("/")
-async def root():
-    return {"message": "Welcome to the Kraken Line Detection API"}
-
-# To run the app with GPU support on Hugging Face Spaces, you need to use uvicorn with the following settings:
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860, workers=1)