Update app.py

app.py

@@ -5,73 +5,87 @@ import os
 import random
 from tensorflow.keras.preprocessing import image
 
-# Root dataset folder
+# -----------------------------
+# Safe Model Loading
+# -----------------------------
+try:
+    model = tf.keras.models.load_model("Fruit_Classification_Model.h5")
+    print("Model loaded successfully")
+except Exception as e:
+    print("Model loading failed:", e)
+    model = None
+
+# -----------------------------
+# Safe Dataset Loading
+# -----------------------------
 DATASET_PATH = "Fruit_Classification"
 
-# Load class names from dataset folders
-class_names = sorted([
-    folder for folder in os.listdir(DATASET_PATH)
-    if os.path.isdir(os.path.join(DATASET_PATH, folder))
-])
+if os.path.exists(DATASET_PATH):
+    class_names = sorted([
+        folder for folder in os.listdir(DATASET_PATH)
+        if os.path.isdir(os.path.join(DATASET_PATH, folder))
+    ])
+else:
+    class_names = []
 
 print("Classes:", class_names)
 
-# Load trained model (your uploaded model)
-model = tf.keras.models.load_model("Fruit_Classification_Model.h5")
 
-print("Model loaded successfully")
-
-
-# Prediction function
+# -----------------------------
+# Prediction Function
+# -----------------------------
 def classify_from_text(text_input):
+    try:
+        if model is None:
+            return None, "Model not loaded properly."
 
-    if text_input is None or text_input.strip() == "":
-        return None, "Please enter a fruit name"
-
-    text_input = text_input.strip().capitalize()
-
-    # Check valid class
-    if text_input not in class_names:
-        return None, f"Invalid fruit name. Valid: {class_names}"
-
-    # Select random image from dataset
-    folder = os.path.join(DATASET_PATH, text_input)
-
-    images = os.listdir(folder)
+        if not class_names:
+            return None, "Dataset folder not found."
 
-    if len(images) == 0:
-        return None, "No images found in folder"
+        if text_input is None or text_input.strip() == "":
+            return None, "Please enter a fruit name."
 
-    img_name = random.choice(images)
+        text_input = text_input.strip().capitalize()
 
-    img_path = os.path.join(folder, img_name)
+        if text_input not in class_names:
+            return None, f"Invalid fruit name.\nValid: {class_names}"
 
-    # Load image
-    img = image.load_img(img_path, target_size=(224,224))
+        folder = os.path.join(DATASET_PATH, text_input)
 
-    img_array = image.img_to_array(img)
+        images = [
+            img for img in os.listdir(folder)
+            if img.lower().endswith((".jpg", ".jpeg", ".png"))
+        ]
 
-    img_array = np.expand_dims(img_array, axis=0)
+        if len(images) == 0:
+            return None, "No images found in folder."
 
-    img_array = img_array / 255.0
+        img_name = random.choice(images)
+        img_path = os.path.join(folder, img_name)
 
+        # Load and preprocess image
+        img = image.load_img(img_path, target_size=(224, 224))
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array = img_array / 255.0
 
-    # Predict
-    prediction = model.predict(img_array, verbose=0)
+        # Predict
+        prediction = model.predict(img_array, verbose=0)
+        predicted_index = np.argmax(prediction)
+        predicted_class = class_names[predicted_index]
+        confidence = float(np.max(prediction)) * 100
 
-    predicted_index = np.argmax(prediction)
+        result = f"Predicted: {predicted_class}\nConfidence: {confidence:.2f}%"
 
-    predicted_class = class_names[predicted_index]
+        return img, result
 
-    confidence = float(np.max(prediction)) * 100
-
-
-    result = f"Predicted: {predicted_class}\nConfidence: {confidence:.2f}%"
-
-
-    return img, result
+    except Exception as e:
+        return None, f"Error occurred: {str(e)}"
 
 
+# -----------------------------
+# Gradio Interface
+# -----------------------------
 interface = gr.Interface(
     fn=classify_from_text,
     inputs=gr.Textbox(
@@ -80,13 +94,12 @@ interface = gr.Interface(
     ),
     outputs=[
         gr.Image(label="Sample Image"),
-        gr.Textbox(label="Prediction Result")  # ✅ UNCOMMENT THIS
+        gr.Textbox(label="Prediction Result")
     ],
     title="CNN Fruit Classification System",
-    description="Enter fruit name → CNN predicts fruit"
+    description="Enter fruit name → CNN selects sample image → CNN predicts fruit"
 )
 
-# FIX for Hugging Face Spaces reload bug
-interface.launch(ssr_mode=False)
-
-
+# Launch normally (DO NOT use ssr_mode=False)
+if __name__ == "__main__":
+    interface.launch()