Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,53 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from tensorflow.keras.models import load_model
+from tensorflow.keras.layers import DepthwiseConv2D
+from PIL import Image, ImageOps
+import numpy as np
+
+# Optional: Patch DepthwiseConv2D if needed
+class PatchedDepthwiseConv2D(DepthwiseConv2D):
+    def __init__(self, *args, groups=1, **kwargs):
+        super().__init__(*args, **kwargs)
+
+# Load model
+model = load_model(r"keras_model.h5", compile=False, custom_objects={"DepthwiseConv2D": PatchedDepthwiseConv2D})
+
+# Load class labels
+with open(r"labels.txt", "r") as f:
+    class_names = f.readlines()
+
+st.title("♻️ Garbage Classification Predictor")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload a waste image (jpg, png)", type=["jpg", "jpeg", "png"])
+
+if st.button("🧪 Predict Waste Type"):
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, use_container_width=True)
+
+
+        # Preprocess image
+        image = image.convert("RGB")
+        image = ImageOps.fit(image, (224, 224), Image.Resampling.LANCZOS)
+        image_array = np.asarray(image)
+        normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+        data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+        data[0] = normalized_image_array
+
+        # Make prediction
+        prediction = model.predict(data)
+        index = np.argmax(prediction)
+        predicted_label = class_names[index].strip()
+        confidence = prediction[0][index]
+
+        # Display result
+        st.success(f"Predicted Waste Type: **{predicted_label.upper()}**")
+        st.write(f"Confidence Score: **{confidence:.2f}**")
+        st.write("♻️ Dispose responsibly!")
+    else:
+        st.warning("⚠️ Please upload an image before predicting.")
+# 🔚 Footer
+st.markdown("---")
+st.markdown("<p style='text-align: center; font-size: 18px;'>Developed with ❤️ By Twinkle Ghangare for EDUNET FOUNDATION </p>", unsafe_allow_html=True)
 
-"""
-# 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])),
-    ))