Update app.py

app.py

@@ -1,8 +1,9 @@
-import gradio as gr
+import streamlit as st
 import pickle
 import cv2
 import numpy as np
 from skimage.transform import resize
+from PIL import Image
 
 # Load the trained KNN model and class names
 with open("knn_model.pkl", "rb") as f:
@@ -11,12 +12,13 @@ with open("knn_model.pkl", "rb") as f:
 with open("class_names.pkl", "rb") as f:
     class_names = pickle.load(f)
 
-# Image Preprocessing Function
+# Function to preprocess image
 def preprocess_image(image):
     """Resizes and flattens the image for model prediction."""
-    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert RGB to BGR for OpenCV
-    image_resized = resize(image, (224, 224))  # Resize to match training size
-    image_flattened = image_resized.flatten().reshape(1, -1)  # Flatten to 1D
+    image = np.array(image)  # Convert PIL image to numpy array
+    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)  # Convert RGB to BGR
+    image_resized = resize(image, (224, 224))  # Resize to 224x224
+    image_flattened = image_resized.flatten().reshape(1, -1)  # Flatten
     return image_flattened
 
 # Prediction Function
@@ -24,21 +26,19 @@ def predict_animal(image):
     """Predicts the class of the uploaded image."""
     processed_image = preprocess_image(image)
     prediction = knn_model.predict(processed_image)[0]
-    return f"Predicted Animal: {class_names[prediction]}"
-
-# Gradio UI
-title = "Animal Image Classifier 🐾"
-description = "Upload an image of an animal and click 'Identify' to predict the species."
-
-app = gr.Interface(
-    fn=predict_animal,
-    inputs=gr.Image(type="numpy"),
-    outputs="text",
-    title=title,
-    description=description,
-    theme="huggingface",
-)
-
-# Run the app
-if __name__ == "__main__":
-    app.launch()
+    return class_names[prediction]
+
+# Streamlit UI
+st.title("🐾 Animal Image Classifier")
+st.write("Upload an image of an animal and click **Identify** to predict the species.")
+
+# Upload Image
+uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+if uploaded_image is not None:
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    if st.button("Identify"):
+        prediction = predict_animal(image)
+        st.success(f"**Predicted Animal:** {prediction} 🐾")