Update app.py

app.py

@@ -11,43 +11,51 @@ 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 += "<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 = "<table style='border-collapse: collapse; width: 80%; margin: 20px;'>"
         
-    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 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 += "</table>"
-    return formatted_info
 
+        
+        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."
 
 
 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}'
+        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."
 
-    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)
@@ -56,19 +64,23 @@ 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 = [
@@ -78,7 +90,6 @@ example_images = [
     "images/alvaro_morte.png",
     "images/amber_heard.png"
 ]
-        
 # Gradio interface
 iface = gr.Interface(
     fn=predict_and_fetch_images,
@@ -98,3 +109,4 @@ iface = gr.Interface(
 
 
 iface.launch()
+