Update app.py

app.py

@@ -6,10 +6,10 @@ import numpy as np
 from sklearn.ensemble import RandomForestClassifier
 from sklearn.preprocessing import LabelEncoder
 
-# ✅ Use relative path for Hugging Face compatibility
+
 DATASET_PATH = "PlantVillage"
 
-# ✅ Load image data from subfolders
+#  Loading image data from dataset
 @st.cache_resource
 def load_data(data_dir, img_size=(64, 64)):
     X, y = [], []
@@ -27,7 +27,7 @@ def load_data(data_dir, img_size=(64, 64)):
                         print(f"Error loading image {img_path}: {e}")
     return np.array(X) / 255.0, np.array(y)
 
-# ✅ Train the model
+# ✅ Training model using RandomForest
 @st.cache_resource
 def train_model():
     X, y = load_data(DATASET_PATH)
@@ -37,11 +37,10 @@ def train_model():
     model.fit(X, y_encoded)
     return model, le
 
-# ✅ Get image paths and their classes
+# ✅ Getting all images from dataset folders
 def get_all_images(data_dir):
     image_paths = []
     valid_extensions = (".jpg", ".jpeg", ".png")
-    
     for class_name in os.listdir(data_dir):
         class_path = Path(data_dir) / class_name
         if class_path.is_dir():
@@ -57,16 +56,23 @@ st.title("🥔 Potato Leaf Image Viewer + Classifier")
 all_images = get_all_images(DATASET_PATH)
 
 if all_images:
-    st.write(f"Found {len(all_images)} images.")
+    st.success(f"✅ Found {len(all_images)} images in dataset.")
+
+    # Show filenames for selection
     image_display_names = [f"{cls} - {img.name}" for img, cls in all_images]
     selected_display_name = st.selectbox("Select an image:", image_display_names)
 
+    # Find selected image
     selected_index = image_display_names.index(selected_display_name)
     selected_image_path, actual_class = all_images[selected_index]
 
+    # Load and show image (small size)
     image = Image.open(selected_image_path).convert("RGB").resize((64, 64))
-    st.image(image, caption=f"Original Class: {actual_class}", use_container_width=True)
 
+    # Show image in smaller size
+    st.image(image, caption=f"📂 Original Class: {actual_class}", width=200)
+
+    # Predict
     model, le = train_model()
     img_array = np.array(image).flatten().reshape(1, -1) / 255.0
     prediction = model.predict(img_array)
@@ -75,4 +81,4 @@ if all_images:
     st.markdown(f"### 🧠 Predicted Class: **{predicted_class}**")
 
 else:
-    st.warning("No images found in the dataset.")
+    st.warning("⚠️ No images found in the dataset. Please check the folder structure.")