Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,50 @@
-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 numpy as np
+from PIL import Image
+from tensorflow.keras.models import load_model
+from tensorflow.keras.applications.resnet50 import preprocess_input
+
+class_names = [
+    'Asian Green Bee-Eater',
+    'Brown-Headed Barbet',
+    'Cattle Egret',
+    'Common Kingfisher',
+    'Common Myna',
+    'Common Rosefinch',
+    'Common Tailorbird',
+    'Coppersmith Barbet',
+    'Forest Wagtail',
+    'Gray Wagtail',
+    'Hoopoe',
+    'House Crow',
+    'Indian Grey Hornbill',
+    'Indian Peacock',
+    'Indian Pitta',
+    'Indian Roller',
+    'Jungle Babbler',
+    'Northern Lapwing',
+    'Red-Wattled Lapwing',
+    'Ruddy Shelduck',
+    'Rufous Treepie',
+    'Sarus Crane',
+    'White Wagtail',
+    'White-Breasted Kingfisher',
+    'White-Breasted Waterhen'
+]
+
+model = load_model("src/indianBirds_InceptionV3Model.keras")
+
+st.title("Indian Bird Species Classifier")
+
+uploaded_file = st.file_uploader("Upload a bird image", type=["jpg", "jpeg", "png"])
+if uploaded_file:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, use_container_width=True)
+
+    img = image.resize((224, 224))
+    x = np.expand_dims(np.array(img), axis=0)
+    x = preprocess_input(x)
+
+    preds = model.predict(x)
+    idx = np.argmax(preds[0])
+    st.markdown(f"### Predicted Species: **{class_names[idx]}**")