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Car_Colours_Classify_v1.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:150f8d1b43a06bf22bf289dbae60660d0f91f44f3d42f7b4c15728d397a9968c
+size 31719065

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("Car_Colours_Classify_v1.pt")
+
+def detect_objects(images):
+    results = model(images)
+    classes = {0: "beige", 1: "black", 2: "blue", 3: "brown", 4: "gold", 5: "green", 6: "grey", 7: "orange", 8: "pink", 9: "purple", 10: "red", 11: "silver", 12: "tan", 13: "white", 14: "yellow"}
+    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):
+        obj = {"url": image_url, "answer": [prediction]}
+        solutions.append(obj)
+    return solutions
+
+# def send_results_to_api(solutions, url):
+#     headers = {"Content-Type": "application/json"}
+#     try:
+#         logging.info(f"Sending results to API at {url} with data: {solutions}")
+#         # response = requests.patch(url, json = {"solutions":solutions})  # Set a timeout   headers=headers,, timeout=60
+#         data = {"solutions":solutions}
+#         response = requests.patch(url, data=json.dumps(data), headers=headers)
+#         response.raise_for_status()
+#         logging.info(f"Response from API: {response.text}")
+#         return response.json()
+#     except requests.exceptions.RequestException as e:
+#         logging.error(f"Failed to send results to API: {e}")
+#         return {"error": f"Failed to send results to API: {str(e)}"}
+
+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]
+    except Exception as e:
+        logging.error(f"Error loading images: {e}")
+        return {"error": f"Error loading images: {str(e)}"}
+
+    names = detect_objects(images)
+    solutions = create_solutions(image_urls, names)
+
+    # result_url = f"{api}/{job_id}"
+    # response = send_results_to_api(solutions, result_url)
+
+    return json.dumps({"solutions": solutions})
+
+inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'urls':['a.jpg','b.jpg']}")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Car Colour Classification with API Integration")
+application.launch()

requirements.txt

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+ultralytics