Update app.py

app.py

@@ -3,6 +3,9 @@ from PIL import Image
 from ultralytics import YOLO
 import requests
 import json
+import logging
+
+logging.basicConfig(level=logging.INFO)
 
 model = YOLO("Bottles_Cans_Classify_v1.pt")
 
@@ -20,40 +23,50 @@ def create_solutions(image_urls, names):
     for image_url, prediction in zip(image_urls, names):       
         prediction_list=[]
         prediction_list.append(prediction)
-        obj = {"image": image_url, "answer": prediction_list}      
+        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 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", "")
+    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", "")
 
-    images = [Image.open(requests.get(url, stream=True).raw) for url in image_urls]  # images from URLs
+    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}, indent=4)
+    # 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'], 'api':'abc', 'job_id':'123'} ")
-outputs = gr.JSON(share=True)
+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()