Update app.py

app.py

@@ -3,21 +3,52 @@ 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('TOKEN')
         response = requests.get(api_url, headers={'X-Api-Key': token})
         if response.status_code == 200:
-            return response.text
+            return format_info(response.text)
         else:
             return "Description not available."
     except Exception as e:
@@ -25,13 +56,13 @@ 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
@@ -51,8 +82,6 @@ def predict_and_fetch_images(input_image):
         traceback.print_exc()
         return "Error during prediction", 0, [], "N/A", str(e)
 
-
-
 # Gradio interface
 iface = gr.Interface(
     fn=predict_and_fetch_images,
@@ -61,13 +90,13 @@ iface = gr.Interface(
         "text",  # Predicted label
         "number",  # Prediction score
         gr.Gallery(label="Lookalike Images"),  # Slideshow component for images
-        "text",  # Info/Description
+        "html",  # Info/Description as HTML
         gr.Textbox(type="text", label="Feedback", placeholder="Provide feedback here")  # Feedback textbox
     ],
     live=True,
-    title="Celebrity Lookalike Predictor with Description and Feedback",
+    title="Celebrity Lookalike Predictor",
     description="Take a snapshot or upload an image to see which celebrity you look like!"
 )
 
-# Launch the Gradio interface
+
 iface.launch()