Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,87 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from utils import predict, get_class_probabilities
+from PIL import Image
+import os
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+st.set_page_config(page_title="Rose Disease Detection", layout="centered")
+st.title("🌹 Rose Disease Detection")
+st.write("Upload a rose leaf image to detect diseases")
+
+# Add description of possible classes
+st.markdown("""
+### Possible Classifications:
+- **Healthy Leaf Rose**: Healthy rose leaves
+- **Rose Rust**: Rose leaves affected by rust disease
+- **Rose Sawfly/Rose Slug**: Rose leaves affected by sawfly or slug damage
+""")
+
+uploaded_file = st.file_uploader("Upload a Rose Leaf Image", type=["jpg", "png", "jpeg"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+    if st.button("Detect Disease"):
+        model_path = "models/rose_model.h5"
+        try:
+            label, confidence = predict(model_path, uploaded_file)
+            
+            # Customize the output based on the prediction
+            if "Healthy" in label:
+                st.success(f"**Prediction**: {label} ({confidence*100:.2f}% confidence)")
+            else:
+                st.warning(f"**Prediction**: {label} ({confidence*100:.2f}% confidence)")
+                st.info("⚠️ This leaf appears to be affected by a disease. Please take appropriate measures.")
+
+            # Display probability distribution
+            st.subheader("Probability Distribution")
+            probabilities = get_class_probabilities(model_path, uploaded_file)
+            
+            # Create a bar chart of probabilities
+            plt.figure(figsize=(10, 6))
+            classes = list(probabilities.keys())
+            probs = list(probabilities.values())
+            plt.bar(classes, probs)
+            plt.xticks(rotation=45, ha='right')
+            plt.ylabel('Probability')
+            plt.title('Class Probabilities')
+            plt.tight_layout()
+            st.pyplot(plt)
+
+        except Exception as e:
+            st.error(f"Error during prediction: {str(e)}")
+            st.info("Please make sure the model is trained and available in the models directory.")
+
+        # Display metrics if available
+        metrics_path = f"models/{model_option.lower()}_metrics.json"
+        if os.path.exists(metrics_path):
+            with open(metrics_path, "r") as f:
+                metrics = json.load(f)
+                st.subheader("Model Performance Metrics")
+                col1, col2, col3 = st.columns(3)
+                with col1:
+                    st.metric("Accuracy", f"{metrics['accuracy']:.2%}")
+                with col2:
+                    st.metric("Precision", f"{metrics['precision']:.2%}")
+                with col3:
+                    st.metric("Recall", f"{metrics['recall']:.2%}")
+
+            # Display confusion matrix if available
+            cm_path = f"models/{model_option.lower()}_confusion_matrix.json"
+            if os.path.exists(cm_path):
+                with open(cm_path, "r") as f:
+                    cm = np.array(json.load(f))
+                    plt.figure(figsize=(10, 8))
+                    sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')
+                    plt.title('Confusion Matrix')
+                    plt.ylabel('True Label')
+                    plt.xlabel('Predicted Label')
+                    st.pyplot(plt)
+
+            with open("models/class_names.json", "r") as f:
+                class_names = json.load(f)
 
-"""
-# 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])),
-    ))