Create app.py

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from PIL import Image
+from ultralytics import YOLO
+import requests
+import json
+
+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 = {"qcUserId": "", "image": 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):
+    # Parse the JSON string into a dictionary
+    params = json.loads(params)
+    
+    image_urls = params.get("image_urls", [])
+    api = params.get("api", "")
+    job_id = params.get("job_id", "")
+
+    images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]  # images from URLs
+
+    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}, indent=4)
+
+
+inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'img_url':['a.jpg','b.jpg'], 'api':'abc', 'job_id':'123'} ")
+outputs = gr.JSON()
+
+application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Bottles Cans Classification with API Integration")
+application.launch()