Update app.py

app.py

@@ -1,59 +1,135 @@
+# import gradio as gr
+# from PIL import Image
+# from ultralytics import YOLO
+# import requests
+# import json
+
+# 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):       
+#         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.patch(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="Car Colour Classification with API Integration")
+# application.launch(share=True)
+
+
+#modified code for patch solution into api
 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     
+    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):       
-        prediction_list=[]
+    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)   
+        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.patch(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}"}
+    try:
+        response = requests.patch(result_url, json=data, headers=headers)
+        response.raise_for_status()
+        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):
-    # Parse the JSON string into a dictionary
-    params = json.loads(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("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
+    if not image_urls or not api or not job_id:
+        logging.error("Missing required parameters: 'image_urls', 'api', or 'job_id'")
+        return {"error": "Missing required parameters: 'image_urls', 'api', or 'job_id'"}
 
-    names = detect_objects(images)  # Perform object detection
-    solutions = create_solutions(image_urls, names)  # Create solutions with image URLs and bounding boxes
+    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)}"}
 
-    result_url = f"{api}/{job_id}"
-    send_results_to_api(solutions, result_url)
+    names = detect_objects(images)
+    solutions = create_solutions(image_urls, names)
 
-    return json.dumps({"solutions": solutions}, indent=4)
+    result_url = f"{api}/{job_id}"
+    response = send_results_to_api(solutions, result_url)
 
+    return json.dumps({"solutions": solutions, "api_response": response}, indent=4)
 
-inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'img_url':['a.jpg','b.jpg'], 'api':'abc', 'job_id':'123'} ")
+inputt = gr.Textbox(label="Parameters (JSON format) Eg. {'img_url':['a.jpg','b.jpg'], 'api':'http://localhost:9000/api/v1/normalUpdateHandler', 'job_id':'123'}")
 outputs = gr.JSON()
 
 application = gr.Interface(fn=process_images, inputs=inputt, outputs=outputs, title="Car Colour Classification with API Integration")
-application.launch(share=True)
+application.launch(share=True)