Upload 3 files

eda.py

@@ -9,10 +9,6 @@ import numpy as np
 
 @st.cache_data
 def load_data():
-    # Load your dataset here
-    # For example:
-    # data = pd.read_csv('your_dataset.csv')
-    # Return dummy data for now
     return pd.DataFrame({
         'Class': ['Plastic', 'Metal', 'Paper', 'Miscellaneous Trash', 'Cardboard', 'Vegetation', 'Glass', 'Food Organics', 'Textile Trash'],
         'Number of Images': [921, 790, 500, 495, 461, 436, 420, 411, 318]
@@ -38,21 +34,13 @@ def run():
     st.write("This chart shows the distribution of images across different waste categories. "
              "Plastic and Metal categories have significantly more images, which could lead to bias in the model.")
 
-    # Add more EDA visualizations and insights here
-    # For example:
-    # - Sample images from each category
-    # - Image size distribution
-    # - Color histograms
-    # - etc.
 
     st.subheader("Sample Images")
     st.write("Here are sample images from each waste category:")
-    # Add code to display sample images here
 
     st.subheader("Image Size Distribution")
     st.write("All images in the dataset have a resolution of 524x524 pixels.")
 
-    # Add more EDA components as needed
 
 if __name__ == "__main__":
     run()

prediction.py

@@ -1,134 +1,134 @@
-import streamlit as st
-import numpy as np
-from PIL import Image
-import plotly.graph_objects as go
-import tensorflow as tf
-import time
-import os
-
-# Load your trained model
-@st.cache_resource
-def load_model():
-    return tf.keras.models.load_model('transfer_learning_model.h5')
-
-def preprocess_image(image):
-    img = image.resize((299, 299))
-    img_array = np.array(img) / 255.0
-    img_array = np.expand_dims(img_array, axis=0)
-    return img, img_array
-
-def predict(image):
-    model = load_model()
-    _, processed_image = preprocess_image(image)
-    prediction = model.predict(processed_image)
-    class_names = ['Cardboard', 'Food Organics', 'Glass', 'Metal', 'Miscellaneous Trash', 'Paper', 'Plastic', 'Textile Trash', 'Vegetation']
-    return {class_names[i]: float(prediction[0][i]) for i in range(len(class_names))}
-
-def run():
-    st.title('🔍 Waste Classification Prediction')
-
-    # Example images
-    example_images = ['1.jpg', '2.jpg', '3.jpg', '4.jpg', '5.jpg', '6.jpg', '7.jpg']
-    example_path = '.'  # Set the path to the current directory
-
-    st.subheader("Choose an example image or upload your own:")
-
-    # Initialize session state for the selected image
-    if 'selected_image' not in st.session_state:
-        st.session_state.selected_image = None
-
-    # Create columns for example images
-    cols = st.columns(4)
-    for i, img_name in enumerate(example_images):
-        with cols[i % 4]:
-            img_path = os.path.join(example_path, img_name)
-            
-            # Display the preview image under the button
-            st.image(img_path, width=100, caption=f'Example {i+1}')
-            
-            # Create the button for each example
-            if st.button(f"Example {i+1}", key=f"example_{i}"):
-                st.session_state.selected_image = img_path
-
-    uploaded_file = st.file_uploader("Or upload your own image", type=["jpg", "jpeg", "png"])
-
-    # Use session state to store the selected or uploaded image
-    if uploaded_file is not None:
-        st.session_state.selected_image = uploaded_file
-
-    image = None
-    if st.session_state.selected_image is not None:
-        if isinstance(st.session_state.selected_image, str):  # Example image case
-            image = Image.open(st.session_state.selected_image).convert('RGB')
-        else:  # Uploaded image case
-            image = Image.open(st.session_state.selected_image).convert('RGB')
-
-    if image:
-        # Create two columns for images
-        col1, col2 = st.columns(2)
-
-        # Display original image in the left column
-        with col1:
-            st.subheader("Selected Image")
-            st.image(image, caption='Selected Image', use_column_width=True)
-
-        # Add a button to start prediction
-        if st.button("Start Prediction"):
-            # Progress and status indicators
-            progress_bar = st.progress(0)
-            status_text = st.empty()
-
-            # Preprocess the image
-            status_text.text('Preprocessing image...')
-            resized_image, _ = preprocess_image(image)
-            progress_bar.progress(33)
-            time.sleep(0.5)  # Simulate processing time
-
-            # Display resized image in the right column
-            with col2:
-                st.subheader("Resized Image (299x299 for Model)")
-                st.image(resized_image, caption='Resized Image for Prediction (299x299)', use_column_width=True)
-
-            # Make prediction
-            status_text.text('Making prediction...')
-            prediction = predict(image)
-            progress_bar.progress(66)
-            time.sleep(0.5)  # Simulate processing time
-
-            # Analyze results
-            status_text.text('Analyzing results...')
-            predicted_class = max(prediction, key=prediction.get)
-            confidence = prediction[predicted_class]
-            progress_bar.progress(100)
-            time.sleep(0.5)  # Simulate processing time
-
-            # Clear the status text and progress bar
-            status_text.empty()
-            progress_bar.empty()
-
-            # Display prediction results under the images
-            st.subheader("Prediction Results")
-            st.write(f"Predicted waste type: **{predicted_class}**")
-            st.write(f"Confidence: {confidence:.2%}")
-
-            # Display vertical bar chart of probabilities using Plotly
-            fig = go.Figure(data=[go.Bar(
-                x=list(prediction.keys()),
-                y=list(prediction.values()),
-                marker=dict(
-                    color=list(prediction.values()),
-                    colorscale='Viridis',
-                    colorbar=dict(title='Probability')
-                )
-            )])
-            fig.update_layout(
-                title='Prediction Probabilities',
-                xaxis_title='Waste Type',
-                yaxis_title='Probability',
-                height=500,
-                width=700
-            )
-            st.plotly_chart(fig)
-
-if __name__ == "__main__":
-    run()
+import streamlit as st
+import numpy as np
+from PIL import Image
+import plotly.graph_objects as go
+import tensorflow as tf
+import time
+import os
+
+# Load your trained model
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model('transfer_learning_model.h5')
+
+def preprocess_image(image):
+    img = image.resize((299, 299))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img, img_array
+
+def predict(image):
+    model = load_model()
+    _, processed_image = preprocess_image(image)
+    prediction = model.predict(processed_image)
+    class_names = ['Cardboard', 'Food Organics', 'Glass', 'Metal', 'Miscellaneous Trash', 'Paper', 'Plastic', 'Textile Trash', 'Vegetation']
+    return {class_names[i]: float(prediction[0][i]) for i in range(len(class_names))}
+
+def run():
+    st.title('🔍 Waste Classification Prediction')
+
+    # Example images
+    example_images = ['cig_package.jpg', 'stella.jpg', 'water_bottle.jpg', 'textile_shoes.jpg', 'organic_eggs.jpg', 'men_metalic_pose.jpg', 'normal_men.jpg','uno.jpg']
+    example_path = '.\\visualization'  # Set the path 
+
+    st.subheader("Choose an example image or upload your own:")
+
+    # Initialize session state for the selected image
+    if 'selected_image' not in st.session_state:
+        st.session_state.selected_image = None
+
+    # Create columns for example images
+    cols = st.columns(4)
+    for i, img_name in enumerate(example_images):
+        with cols[i % 4]:
+            img_path = os.path.join(example_path, img_name)
+            
+            # Display the preview image under the button
+            st.image(img_path, width=100, caption=f'Example {i+1}')
+            
+            # Create the button for each example
+            if st.button(f"Example {i+1}", key=f"example_{i}"):
+                st.session_state.selected_image = img_path
+
+    uploaded_file = st.file_uploader("Or upload your own image", type=["jpg", "jpeg", "png"])
+
+    # Use session state to store the selected or uploaded image
+    if uploaded_file is not None:
+        st.session_state.selected_image = uploaded_file
+
+    image = None
+    if st.session_state.selected_image is not None:
+        if isinstance(st.session_state.selected_image, str):  # Example image case
+            image = Image.open(st.session_state.selected_image).convert('RGB')
+        else:  # Uploaded image case
+            image = Image.open(st.session_state.selected_image).convert('RGB')
+
+    if image:
+        # Create two columns for images
+        col1, col2 = st.columns(2)
+
+        # Display original image in the left column
+        with col1:
+            st.subheader("Selected Image")
+            st.image(image, caption='Selected Image', use_column_width=True)
+
+        # Add a button to start prediction
+        if st.button("Start Prediction"):
+            # Progress and status indicators
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+
+            # Preprocess the image
+            status_text.text('Preprocessing image...')
+            resized_image, _ = preprocess_image(image)
+            progress_bar.progress(33)
+            time.sleep(0.5)  # Simulate processing time
+
+            # Display resized image in the right column
+            with col2:
+                st.subheader("Resized Image (299x299 for Model)")
+                st.image(resized_image, caption='Resized Image for Prediction (299x299)', use_column_width=True)
+
+            # Make prediction
+            status_text.text('Making prediction...')
+            prediction = predict(image)
+            progress_bar.progress(66)
+            time.sleep(0.5)  # Simulate processing time
+
+            # Analyze results
+            status_text.text('Analyzing results...')
+            predicted_class = max(prediction, key=prediction.get)
+            confidence = prediction[predicted_class]
+            progress_bar.progress(100)
+            time.sleep(0.5)  # Simulate processing time
+
+            # Clear the status text and progress bar
+            status_text.empty()
+            progress_bar.empty()
+
+            # Display prediction results under the images
+            st.subheader("Prediction Results")
+            st.write(f"Predicted waste type: **{predicted_class}**")
+            st.write(f"Confidence: {confidence:.2%}")
+
+            # Display vertical bar chart of probabilities using Plotly
+            fig = go.Figure(data=[go.Bar(
+                x=list(prediction.keys()),
+                y=list(prediction.values()),
+                marker=dict(
+                    color=list(prediction.values()),
+                    colorscale='Viridis',
+                    colorbar=dict(title='Probability')
+                )
+            )])
+            fig.update_layout(
+                title='Prediction Probabilities',
+                xaxis_title='Waste Type',
+                yaxis_title='Probability',
+                height=500,
+                width=700
+            )
+            st.plotly_chart(fig)
+
+if __name__ == "__main__":
+    run()