Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import numpy as np
 import cv2
-import os
 
 # Load model data
 mean = np.load("mean.npy")
@@ -9,38 +8,50 @@ eigenfaces = np.load("eigenfaces.npy")
 projected_faces = np.load("projected_faces.npy")
 labels = np.load("labels.npy")
 
-# Preprocessing
+# Resize and flatten the image
 def preprocess(img):
-    img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-    img = cv2.resize(img, (100, 100))  # or your dataset size
-    img_flat = img.flatten()
-    return img_flat
+    try:
+        gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
+    except Exception as e:
+        raise ValueError(f"Failed to convert to grayscale: {e}")
 
-# Prediction function
+    resized = cv2.resize(gray, (100, 100))
+    return resized.flatten()
+
+# Predict function with debug
 def predict(img):
+    print("📸 Raw input image:", type(img))
+
     if img is None:
-        return "No image provided"
-    
+        return "❌ No image provided"
+
     try:
+        # Ensure the input is a NumPy array
+        if not isinstance(img, np.ndarray):
+            return f"❌ Unexpected input type: {type(img)}"
+
+        # Preprocess image
         img_vector = preprocess(img)
         img_centered = img_vector - mean
         img_projected = np.dot(eigenfaces.T, img_centered)
 
+        # Compare with dataset
         distances = np.linalg.norm(projected_faces - img_projected, axis=1)
         min_index = np.argmin(distances)
         predicted_label = labels[min_index]
-        
-        return f"Match found: Person {predicted_label} (distance={distances[min_index]:.2f})"
+
+        return f"✅ Match found: Person {predicted_label} (distance={distances[min_index]:.2f})"
+
     except Exception as e:
-        return f"Error processing image: {str(e)}"
+        return f"⚠️ Error during prediction: {str(e)}"
 
-# Gradio UI
+# Gradio interface
 iface = gr.Interface(
     fn=predict,
     inputs=gr.Image(type="numpy", label="Upload Face Image"),
     outputs=gr.Textbox(label="Prediction Result"),
     title="Eigenfaces Face Recognition",
-    description="Upload a face image to identify the most similar person from the dataset."
+    description="Upload a face image (JPG/PNG) to identify the most similar person from the dataset."
 )
 
 iface.launch()