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.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
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+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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+*.tflite filter=lfs diff=lfs merge=lfs -text
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+src/intel_classifier_improved.keras filter=lfs diff=lfs merge=lfs -text

Dockerfile

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-FROM python:3.13.5-slim
-
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y \
-    build-essential \
-    curl \
-    git \
-    && rm -rf /var/lib/apt/lists/*
-
-COPY requirements.txt ./
-COPY src/ ./src/
-
-RUN pip3 install -r requirements.txt
-
-EXPOSE 8501
-
-HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
-
-ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0"]
+FROM python:3.13.5-slim
+
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    build-essential \
+    curl \
+    git \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+COPY src/ ./src/
+
+RUN pip3 install -r requirements.txt
+
+EXPOSE 8501
+
+HEALTHCHECK CMD curl --fail http://localhost:8501/_stcore/health
+
+ENTRYPOINT ["streamlit", "run", "src/streamlit_app.py", "--server.port=8501", "--server.address=0.0.0.0", "--server.enableXsrfProtection=false"]

README.md

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----
-title: GC7
-emoji: 🚀
-colorFrom: red
-colorTo: red
-sdk: docker
-app_port: 8501
-tags:
-- streamlit
-pinned: false
-short_description: Streamlit template space
----
-
-# Welcome to Streamlit!
-
-Edit `/src/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).
+---
+title: GC7
+emoji: 🚀
+colorFrom: red
+colorTo: red
+sdk: docker
+app_port: 8501
+tags:
+- streamlit
+pinned: false
+short_description: Streamlit template space
+---
+
+# Welcome to Streamlit!
+
+Edit `/src/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).

requirements.txt

@@ -1,3 +1,5 @@
-altair
-pandas
-streamlit
+streamlit
+tensorflow
+numpy
+PIL
+json

src/class_indices.json

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+{
+  "buildings": 0,
+  "forest": 1,
+  "glacier": 2,
+  "mountain": 3,
+  "sea": 4,
+  "street": 5
+}

src/intel_classifier_improved.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:81f17c0449962a514dd16139340add7afb6b7d72c3cc10bf227a4a6dcc5ced2d
+size 132196258

src/streamlit_app.py

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-import altair as alt
-import numpy as np
-import pandas as pd
-import streamlit as st
-
-"""
-# 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])),
-    ))
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import json
+
+# Load model
+
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model("src/intel_classifier_improved.keras")
+    return model
+
+model = load_model()
+
+# Load class indices
+
+with open("src/class_indices.json", "r") as f:
+    class_indices = json.load(f)
+class_names = list(class_indices.keys())
+
+# Main app
+
+def run():
+    st.title("Environment Image Classifier")
+
+    with st.form(key='form_image_classifier'):
+        uploaded_file = st.file_uploader("Upload an image",
+                                         type=["jpg", "jpeg", "png"],
+                                         help="Upload an environment image to classify")
+        submitted = st.form_submit_button('Predict')
+    
+    if submitted:
+        if uploaded_file is None:
+            st.warning("Please upload an image first.")
+            return
+        image = Image.open(uploaded_file).convert('RGB')
+        st.image(image, caption="Uploaded Image", use_container_width=True)
+
+        # Preprocess
+        img = image.resize((150, 150))
+        img_array = np.array(img) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        # Predict
+        prediction = model.predict(img_array)[0]
+        predicted_index = np.argmax(prediction)
+        predicted_class = class_names[predicted_index]
+        confidence = np.max(prediction)
+
+        # Output
+        st.subheader("Prediction Result")
+
+        st.success(f"Predicted Class: {predicted_class}")
+        st.write(f"Confidence: {confidence:.2%}")
+
+        st.write("### Class Probabilities")
+        for i, prob in enumerate(prediction):
+            st.write(f"{class_names[i]}: {prob:.2%}")
+
+if __name__ == '__main__':
+    run()