Update app.py

app.py

@@ -11,51 +11,43 @@ dataset = load_dataset("SaladSlayer00/twin_matcher_data")
 image_classifier = pipeline("image-classification", model="SaladSlayer00/twin_matcher_beta")
 
 def format_info(info_json):
-    try:
+
         
-        info_data = json.loads(info_json)
+    info_data = json.loads(info_json)
 
         
-        formatted_info = "<table style='border-collapse: collapse; width: 80%; margin: 20px;'>"
+    formatted_info = "<table style='border-collapse: collapse; width: 80%; margin: 20px;'>"
         
-        formatted_info += "<tr style='background-color: #f2f2f2;'>"
-        for key in info_data[0].keys():
-            formatted_info += f"<th style='border: 1px solid #dddddd; text-align: left; padding: 8px;'><b>{key.capitalize()}</b></th>"
-        formatted_info += "</tr>"
+    formatted_info += "<tr style='background-color: #f2f2f2;'>"
+    for key in info_data[0].keys():
+        formatted_info += f"<th style='border: 1px solid #dddddd; text-align: left; padding: 8px;'><b>{key.capitalize()}</b></th>"
+    formatted_info += "</tr>"
 
         
-        for entry in info_data:
-            formatted_info += "<tr>"
-            for value in entry.values():
-                formatted_info += f"<td style='border: 1px solid #dddddd; text-align: left; padding: 8px;'>{value}</td>"
-            formatted_info += "</tr>"
-        formatted_info += "</table>"
-        return formatted_info
-    except Exception as e:
-        print(f"Error formatting info: {e}")
-        return "Info not available."
+    for entry in info_data:
+        formatted_info += "<tr>"
+        for value in entry.values():
+            formatted_info += f"<td style='border: 1px solid #dddddd; text-align: left; padding: 8px;'>{value}</td>"
+        formatted_info += "</tr>"
+    formatted_info += "</table>"
+    return formatted_info
+
 
 
 def fetch_info(celebrity_label):
-    try:
         
-        parts = celebrity_label.split("_")
-        formatted_label = " ".join([part.capitalize() for part in parts])
+    parts = celebrity_label.split("_")
+    formatted_label = " ".join([part.capitalize() for part in parts])
 
         
-        api_url = f'https://api.api-ninjas.com/v1/celebrity?name={formatted_label}'
-
-        token = os.getenv('TOKEN')
-        response = requests.get(api_url, headers={'X-Api-Key': token})
-        if response.status_code == 200:
-            return format_info(response.text)
-        else:
-            return "Description not available."
-    except Exception as e:
-        print(f"Error fetching information: {e}")
-        traceback.print_exc()
-        return "Description not available."
+    api_url = f'https://api.api-ninjas.com/v1/celebrity?name={formatted_label}'
 
+    token = os.getenv('TOKEN')
+    response = requests.get(api_url, headers={'X-Api-Key': token})
+    if response.status_code == 200:
+        return format_info(response.text)
+    else:
+        return "A shining star for sure."
 
 def fetch_images_for_label(label):
     label_data = dataset['train'].filter(lambda example: example['label'] == label)
@@ -64,23 +56,20 @@ def fetch_images_for_label(label):
 
 
 def predict_and_fetch_images(input_image):
-    try:
+
         # Use the image classifier pipeline
-        predictions = image_classifier(input_image)
-        top_prediction = max(predictions, key=lambda x: x['score'])
-        label, score = top_prediction['label'], top_prediction['score']
+    predictions = image_classifier(input_image)
+    top_prediction = max(predictions, key=lambda x: x['score'])
+    label, score = top_prediction['label'], top_prediction['score']
 
         # Fetch images for the predicted label
-        images = fetch_images_for_label(label)
+    images = fetch_images_for_label(label)
 
         # Fetch information for the predicted label
-        info = fetch_info(label)
+    info = fetch_info(label)
+
+    return label, score, images, info, "No Error"
 
-        return label, score, images, info, "No Error"
-    except Exception as e:
-        print(f"Error during prediction: {e}")
-        traceback.print_exc()
-        return "Error during prediction", 0, [], "N/A", str(e)
 
 example_images = [
     "images/megan_fox.png",