Update app.py

app.py

@@ -3,47 +3,21 @@ from transformers import pipeline
 from datasets import load_dataset
 import requests
 import traceback
-import json
+import os
 
 dataset = load_dataset("SaladSlayer00/twin_matcher")
 
 image_classifier = pipeline("image-classification", model="SaladSlayer00/twin_matcher")
 
-def format_info(info_json):
-    try:
-        
-        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>"
-
-        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])
-
-        
         api_url = f'https://api.api-ninjas.com/v1/celebrity?name={formatted_label}'
-
-        token = os.getenv('AWS_SECRET_ACCESS_KEY')   
-        response = requests.get(api_url, headers={'X-Api-Key': 'token'})
+        token = os.getenv('TOKEN')
+        response = requests.get(api_url, headers={'X-Api-Key': token})
         if response.status_code == 200:
-            return format_info(response.text)
+            return response.text
         else:
             return "Description not available."
     except Exception as e:
@@ -51,23 +25,24 @@ def fetch_info(celebrity_label):
         traceback.print_exc()
         return "Description not available."
 
+# Function to fetch images for the predicted label from the Hugging Face dataset
 def fetch_images_for_label(label):
     label_data = dataset['train'].filter(lambda example: example['label'] == label)
     images = [example['image'] for example in label_data]
     return images
 
-
+# Function to process the image, predict, and fetch images and info
 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']
 
-        
+        # Fetch images for the predicted label
         images = fetch_images_for_label(label)
 
-        
+        # Fetch information for the predicted label
         info = fetch_info(label)
 
         return label, score, images, info, "No Error"
@@ -77,20 +52,22 @@ def predict_and_fetch_images(input_image):
         return "Error during prediction", 0, [], "N/A", str(e)
 
 
+
+# Gradio interface
 iface = gr.Interface(
     fn=predict_and_fetch_images,
     inputs=gr.Image(type="pil", label="Upload or Take a Snapshot"),
     outputs=[
-        "text",  
-        "number",  
-        gr.Gallery(label="Lookalike Images"),  
-        "html",  
+        "text",  # Predicted label
+        "number",  # Prediction score
+        gr.Gallery(label="Lookalike Images"),  # Slideshow component for images
+        "text",  # Info/Description
         gr.Textbox(type="text", label="Feedback", placeholder="Provide feedback here")  # Feedback textbox
     ],
     live=True,
-    title="Celebrity Lookalike Predictor",
+    title="Celebrity Lookalike Predictor with Description and Feedback",
     description="Take a snapshot or upload an image to see which celebrity you look like!"
 )
 
-
+# Launch the Gradio interface
 iface.launch()