Delete bad version app-hf.py

app-hf.py

@@ -1,53 +0,0 @@
-from fastapi import FastAPI, HTTPException, Response
-from fastapi.responses import HTMLResponse
-from transformers import pipeline, YolosForObjectDetection, YolosImageProcessor
-from PIL import Image, ImageDraw
-import torch
-import requests
-import io
-import base64
-
-
-app = FastAPI() # Create a new FastAPI app instance
-
-# Initialize the Yolos model and image processor
-yolos_model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
-yolos_image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
-
-# Define a route for the root "/"
-@app.get("/")
-def read_root():
-    return {"message": "Welcome to the YOLOS Object Detection API!"}
-
-# Define a route for detecting objects from an image URL
-@app.get("/", response_class=HTMLResponse)
-def detect_objects(url: str):
-    try:
-        # Download the image from the specified URL
-        image = Image.open(requests.get(url, stream=True).raw)
-
-        # Preprocess the image using the Yolos image processor
-        inputs = yolos_image_processor(images=image, return_tensors="pt")
-
-        # Run the Yolos model on the preprocessed image
-        outputs = yolos_model(**inputs)
-
-        # Post-process the object detection results
-        target_sizes = torch.tensor([image.size[::-1]])
-        results = yolos_image_processor.post_process_object_detection(outputs, threshold=0.9, target_sizes=target_sizes)[0]
-
-        # Draw bounding boxes on the image
-        for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
-            image_draw = ImageDraw.Draw(image)
-            image_draw.rectangle(box.tolist(), outline="red", width=2)
-            image_draw.text((box[0], box[1]), f"{yolos_model.config.id2label[label.item()]}: {round(score.item(), 3)}", fill="red")
-
-        # Save the modified image to a byte stream
-        image_byte_array = io.BytesIO()
-        image.save(image_byte_array, format="PNG")
-
-        # Return the image as a Response with content type "image/png"
-        return Response(content=image_byte_array.getvalue(), media_type="image/png")
-
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}")