Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,66 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+IMG_SIZE = 128 
+
+# Class Labels
+# Based on: {'ALB':0, 'YFT':1, 'SHARK':2, 'NoF':3, 'LAG':4, 'BET':5, 'OTHER':6, 'DOL':7}
+LABELS = ['ALB', 'YFT', 'SHARK', 'NoF', 'LAG', 'BET', 'OTHER', 'DOL']
+
+# Page Config
+st.set_page_config(page_title="🐟 Fish Species Detection", page_icon="🐟")
+
+# --- LOAD MODEL ---
+@st.cache_resource
+def load_model():
+    try:
+        model = tf.keras.models.load_model('src/fish.h5')
+        return model
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+model = load_model()
+
+# --- IMAGE PREPROCESSING ---
+def process_image(image):
+    image = image.resize((IMG_SIZE, IMG_SIZE))
+    img_array = np.array(image)
+    img_array = img_array / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+# --- USER INTERFACE ---
+st.title("🐟 Fish Species Classification")
+st.write("The Nature Conservancy Fisheries Monitoring Model")
+
+uploaded_file = st.file_uploader("Upload a fish image.", type=["jpg", "png", "jpeg"])
 
-"""
-# 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])),
-    ))
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert('RGB')
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    
+    if st.button('Predict'):
+        if model is None:
+            st.error("Model could not be loaded, prediction impossible.")
+        else:
+            with st.spinner('Analyzing...'):
+                # Process image
+                processed_img = process_image(image)
+                # Make prediction
+                predictions = model.predict(processed_img)                
+                # Get the highest probability class
+                predicted_class_idx = np.argmax(predictions[0])
+                predicted_class = LABELS[predicted_class_idx]
+                confidence = np.max(predictions[0])
+                # Display Results
+                st.success(f"Prediction: **{predicted_class}**")
+                st.info(f"Confidence: **{confidence * 100:.2f}%**")
+                # Probability Distribution Chart
+                st.write("---")
+                st.write("Probability Distribution:")
+                # Create a dictionary for the chart
+                chart_data = {label: float(predictions[0][i]) for i, label in enumerate(LABELS)}
+                st.bar_chart(chart_data)