Upload 3 files

app.py

@@ -1,67 +1,3 @@
-<<<<<<< HEAD
-import streamlit as st
-import tensorflow as tf
-from tensorflow.keras.preprocessing.image import load_img, img_to_array
-import numpy as np
-from PIL import Image
-import io
-
-st.set_page_config(
-    page_title="Waste Classifier",
-    layout="centered"
-)
-
-@st.cache_resource
-def load_model():
-    return tf.keras.models.load_model('CNN_Prak4_ML.h5')
-
-def preprocess_image(img):
-    img = img.resize((244, 244))
-    img = img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img = img / 255.0
-    return img
-
-LABEL_CLASS = {
-    0: "Cardboard",
-    1: "Glass",
-    2: "Metal",
-    3: "Paper",
-    4: "Textile Trash",
-    5: "Vegetation"
-}
-
-
-def main():
-    st.title("Waste Classifier")
-    st.write("Upload an image and the model will predict whether it's a chihuahua or a muffin!")
-    
-    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
-    
-    if uploaded_file is not None:
-        image = Image.open(uploaded_file)
-        st.image(image, caption='Uploaded Image', use_column_width=True)
-        
-        if st.button('Predict'):
-            model = load_model()
-            
-            processed_image = preprocess_image(image)
-            
-            with st.spinner('Predicting...'):
-                prediction = model.predict(processed_image)
-                pred_class = LABEL_CLASS[np.argmax(prediction)]
-                confidence = float(prediction.max()) * 100
-            
-            st.success(f'Prediction: {pred_class.upper()}')
-            st.info(f'Confidence: {confidence:.2f}%')
-            
-            st.write("Class Probabilities:")
-            for i, prob in enumerate(prediction[0]):
-                st.progress(float(prob))
-                st.write(f"{LABEL_CLASS[i]}: {float(prob)*100:.2f}%")
-
-if __name__ == "__main__":
-=======
 import streamlit as st
 import tensorflow as tf
 from tensorflow.keras.preprocessing.image import load_img, img_to_array
@@ -97,7 +33,7 @@ LABEL_CLASS = {
 
 def main():
     st.title("Waste Classifier")
-    st.write("Upload an image and the model will predict whether it's a chihuahua or a muffin!")
+    st.write("Upload an image and the model will predict waste image")
     
     uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
     
@@ -124,5 +60,4 @@ def main():
                 st.write(f"{LABEL_CLASS[i]}: {float(prob)*100:.2f}%")
 
 if __name__ == "__main__":
->>>>>>> 9014ce89a3d5b013b1a3e93a65c21b8e932a0117
     main()

requirement.txt

@@ -1,8 +1,3 @@
-<<<<<<< HEAD
 pillow==10.4.0
 streamlit==1.39.0
-=======
-pillow==10.4.0
-streamlit==1.39.0
->>>>>>> 9014ce89a3d5b013b1a3e93a65c21b8e932a0117
 tensorflow==2.18.0