Update app.py

app.py

@@ -56,62 +56,5 @@ if uploaded_file:
         # Display prediction (assuming result is a probability or class index)
         st.write(f"Predicted Axle Configuration: {result}")
     except Exception as e:
-        st.error(f"An error occurred during prediction: {e}")import streamlit as st
-import requests
-import numpy as np
-from PIL import Image
-from tensorflow import keras
-
-# Cache the model download and loading to avoid reloading on every interaction
-@st.cache_resource
-def load_model():
-    # URL of the model file on Hugging Face
-    model_url = "https://huggingface.co/spaces/langatnaomi10/CNN/resolve/main/trained_model.h5"
-    
-    # Download the file
-    model_path = "trained_model.h5"
-    response = requests.get(model_url, stream=True)
-    with open(model_path, "wb") as file:
-        for chunk in response.iter_content(chunk_size=1024):
-            if chunk:
-                file.write(chunk)
-    
-    # Load the saved model
-    model = keras.models.load_model(model_path)
-    return model
-
-# Load the model
-try:
-    model = load_model()
-except Exception as e:
-    st.error(f"Failed to load the model: {e}")
-    st.stop()
+        st.error(f"An error occurred during prediction: {e}")
 
-# Define a prediction function
-def predict_axle_configuration(image):
-    # Resize and preprocess the image
-    image = image.resize((128, 128))  # Resize to match model input size
-    image_array = np.array(image) / 255.0  # Normalize pixel values to [0, 1]
-    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
-    
-    # Make prediction
-    prediction = model.predict(image_array)
-    return prediction
-
-# Streamlit UI
-st.title("Vehicle Axle Configuration Prediction")
-uploaded_file = st.file_uploader("Upload a vehicle image", type=['jpg', 'jpeg', 'png'])
-
-if uploaded_file:
-    try:
-        img = Image.open(uploaded_file)
-        st.image(img, caption='Uploaded Image', use_column_width=True)
-        st.write("Classifying...")
-        
-        # Get prediction
-        result = predict_axle_configuration(img)
-        
-        # Display prediction (assuming result is a probability or class index)
-        st.write(f"Predicted Axle Configuration: {result}")
-    except Exception as e:
-        st.error(f"An error occurred during prediction: {e}")