Create app.py

app.py

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+from fastapi import FastAPI
+from transformers import pipeline, YolosForObjectDetection, YolosImageProcessor
+from PIL import Image
+import torch
+import requests
+
+# Create a new FastAPI app instance
+app = FastAPI()
+
+# Initialize the Yolos model and image processor
+yolos_model = YolosForObjectDetection.from_pretrained('hustvl/yolos-tiny')
+yolos_image_processor = YolosImageProcessor.from_pretrained("hustvl/yolos-tiny")
+
+# Route for object detection using Yolos model
+@app.get("/detect-objects")
+def detect_objects(url: str):
+    # 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)
+
+    # model predicts bounding boxes and corresponding COCO classes
+    logits = outputs.logits
+    pred_boxes = outputs.pred_boxes
+
+    # 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]
+
+    # Format and return the results
+    detected_objects = []
+    for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
+        box = [round(i, 2) for i in box.tolist()]
+        detected_objects.append({
+            "label": yolos_model.config.id2label[label.item()],
+            "confidence": round(score.item(), 3),
+            "location": box
+        })
+
+    return {"detected_objects": detected_objects}