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Vehicles_Classify_v1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8764da9da87603c7dc2038295569a8a64504fc063a2e840bab121525e9d18a85
+size 31705561

app.py

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+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+import requests
+import json
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+model = YOLO("Vehicles_Classify_v1.pt")
+
+def detect_objects(images):
+    results = model(images)
+    classes={ 0:"bus", 1:"family sedan", 2:"fire engine", 3:"heavy truck", 4:"jeep", 5:"mini bus", 6:"racing car", 7:"SUV", 8:"taxi", 9:"truck" }
+    names=[]  
+    for result in results:          
+        probs = result.probs.top1  
+        names.append(classes[probs])
+    return names
+
+def create_solutions(image_urls, names):
+    solutions = []   
+    for image_url, prediction in zip(image_urls, names):       
+        prediction_list=[]
+        prediction_list.append(prediction)
+        obj = {"url": image_url, "answer": prediction_list}      
+        solutions.append(obj)   
+    return solutions
+
+# def send_results_to_api(data, result_url):
+#     # Example function to send results to an API
+#     headers = {"Content-Type": "application/json"}
+#     response = requests.post(result_url, json=data, headers=headers)
+#     if response.status_code == 200:
+#         return response.json()  # Return any response from the API if needed
+#     else:
+#         return {"error": f"Failed to send results to API: {response.status_code}"}
+
+def process_images(params):
+    try:
+        params = json.loads(params)
+    except json.JSONDecodeError as e:
+        logging.error(f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}")
+        return {"error": f"Invalid JSON input: {e.msg} at line {e.lineno} column {e.colno}"}
+
+    image_urls = params.get("urls", [])
+    # api = params.get("api", "")
+    # job_id = params.get("job_id", "")
+
+    if not image_urls:
+        logging.error("Missing required parameters: 'urls'")
+        return {"error": "Missing required parameters: 'urls'"}
+    try:
+        images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]  # images from URLs
+    except Exception as e:
+        logging.error(f"Error loading images: {e}")
+        return {"error": f"Error loading images: {str(e)}"}
+        
+    names = detect_objects(images)  # Perform object detection
+    solutions = create_solutions(image_urls, names)  # Create solutions with image URLs and bounding boxes
+
+    # result_url = f"{api}/{job_id}"
+    # send_results_to_api(solutions, result_url)
+
+    return json.dumps({"solutions": solutions})
+
+inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'img_url':['a.jpg','b.jpg']}")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Bottles Cans Classification with API Integration")
+application.launch()

requirements.txt

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+ultralytics