add classifier

best_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3870cefaa28a3803fddac25f8316880e0fadba580910acded5185df3eebba82e
+size 78129506

src/streamlit_app.py

@@ -1,40 +1,178 @@
-import altair as alt
+import matplotlib.pyplot as plt
 import numpy as np
-import pandas as pd
 import streamlit as st
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torchvision.transforms as T
+from PIL import Image
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+st.set_page_config(page_title="Garbage Classification")
+
+
+# CNN Model Definition
+class SimpleCNN(nn.Module):
+    def __init__(self, num_classes, input_channels=3):
+        super().__init__()
+
+        # Convolutional layers
+        self.conv1 = nn.Conv2d(input_channels, 32, kernel_size=3, padding=0)
+        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=0)
+        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=0)
+        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.conv4 = nn.Conv2d(128, 256, kernel_size=3, padding=0)
+        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
+
+        self.flatten = nn.Flatten()
+
+        # Dense layers
+        self.fc1 = nn.Linear(256 * 12 * 12, 512)
+        self.dropout1 = nn.Dropout(0.5)
+
+        self.fc2 = nn.Linear(512, 512)
+        self.dropout2 = nn.Dropout(0.5)
+
+        self.fc3 = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        # Conv blocks
+        x = F.relu(self.conv1(x))
+        x = self.pool1(x)
+
+        x = F.relu(self.conv2(x))
+        x = self.pool2(x)
+
+        x = F.relu(self.conv3(x))
+        x = self.pool3(x)
+
+        x = F.relu(self.conv4(x))
+        x = self.pool4(x)
+
+        # Dense layers
+        x = self.flatten(x)
+        x = F.relu(self.fc1(x))
+        x = self.dropout1(x)
+
+        x = F.relu(self.fc2(x))
+        x = self.dropout2(x)
+
+        x = self.fc3(x)
+        return x
+
+
+# Class names
+CLASS_NAMES = [
+    "battery",
+    "biological",
+    "cardboard",
+    "clothes",
+    "glass",
+    "metal",
+    "paper",
+    "plastic",
+    "shoes",
+    "trash",
+]
+
+
+# Cache the model loading
+@st.cache_resource
+def load_model():
+    """Load the trained model"""
+    device = torch.device("cpu")
+    model = SimpleCNN(num_classes=10)
+    model = nn.DataParallel(model)
+
+    try:
+        model.load_state_dict(torch.load("best_model.pth", map_location=device))
+        model.eval()
+        return model, device
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None, device
+
+
+def preprocess_image(image):
+    """Preprocess uploaded image"""
+    transform = T.Compose(
+        [
+            T.Resize(224),
+            T.CenterCrop(224),
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+        ]
+    )
+
+    image_tensor = transform(image).unsqueeze(0)
+    return image_tensor
+
+
+def predict_image(image, model, device):
+    """Make prediction on image"""
+    # Preprocess image
+    input_tensor = preprocess_image(image).to(device)
+
+    # Make prediction
+    with torch.no_grad():
+        outputs = model(input_tensor)
+        probabilities = F.softmax(outputs, dim=1)
+        confidence, predicted_idx = torch.max(probabilities, 1)
+
+    predicted_class = CLASS_NAMES[predicted_idx.item()]
+    confidence_score = confidence.item()
+    all_probabilities = probabilities.cpu().numpy().flatten()
+
+    return predicted_class, confidence_score, all_probabilities
+
+
+def get_confidence_color(confidence):
+    """Get color class based on confidence score"""
+    if confidence >= 0.7:
+        return "confidence-high"
+    elif confidence >= 0.4:
+        return "confidence-medium"
+    else:
+        return "confidence-low"
+
+
+def main():
+    # Load model
+    model, device = load_model()
+
+    # File uploader
+    st.header("Garbage Classification")
+    uploaded_file = st.file_uploader(
+        "Choose an image file",
+        type=["jpg", "jpeg", "png"],
+    )
+
+    if uploaded_file is not None:
+        # Display uploaded image
+        image = Image.open(uploaded_file).convert("RGB")
+
+        col1, col2 = st.columns([1, 1])
+        with col1:
+            st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        # Make prediction
+        with st.spinner("🔍 Analyzing image..."):
+            predicted_class, confidence, probabilities = predict_image(
+                image, model, device
+            )
+
+        sorted_indices = np.argsort(probabilities)[::-1]
+
+        container = col2.container(border=True)
+        for i, idx in enumerate(sorted_indices):
+            class_name = CLASS_NAMES[idx]
+            prob = probabilities[idx]
+            container.write(f"{class_name.title()}: {prob:.1%}")
+
+
+if __name__ == "__main__":
+    main()