trying altair

app.py

@@ -1,47 +1,63 @@
 import gradio as gr
 from fastai.vision.all import *
-import matplotlib.pyplot as plt
-import numpy as np
+import altair as alt
+import pandas as pd
 
-learn = load_learner('export.pkl') 
+# Load your trained FastAI model
+learn = load_learner('export.pkl')
 
 def predict_and_plot(img):
-    
+    # Get predictions
     pred, pred_idx, probs = learn.predict(img)
-
-    class_names = ['Normal',
-                   'Hollenhorst Emboli',
-                   'Hypertensive Retinopathy',
-                   'Coat\'s',
-                   'Macroaneurism',
-                   'Choroidal Neovascularization',
-                   'Other',
-                   'Branch Retinal Artery Occlusion',
-                   'Cilio-Retinal Artery Occlusion',
-                   'Branch Retinal Vein Occlusion',
-                   'Central Retinal Vein Occlusion',
-                   'Hemi-Central Retinal Vein Occlusion',
-                   'Background Diabetic Retinopathy',
-                   'Proliferative Diabetic Retinopathy',
-                   'Arteriosclerotic Retinopathy']
+    
+    # Define class names
+    class_names = [
+        'Normal',
+        'Hollenhorst Emboli',
+        'Hypertensive Retinopathy',
+        "Coat's",
+        'Macroaneurism',
+        'Choroidal Neovascularization',
+        'Other',
+        'Branch Retinal Artery Occlusion',
+        'Cilio-Retinal Artery Occlusion',
+        'Branch Retinal Vein Occlusion',
+        'Central Retinal Vein Occlusion',
+        'Hemi-Central Retinal Vein Occlusion',
+        'Background Diabetic Retinopathy',
+        'Proliferative Diabetic Retinopathy',
+        'Arteriosclerotic Retinopathy'
+    ]
+    
+    # Convert probabilities to numpy array
     probs_np = probs.numpy()
-
     threshold = 0.5
-
     present = probs_np > threshold
-    fig, ax = plt.subplots(figsize=(10, 6))
-    y_pos = np.arange(len(class_names))
-
-    colors = ['green' if is_present else 'red' for is_present in present]
-    ax.barh(y_pos, probs_np, color=colors)
-    ax.set_yticks(y_pos)
-    ax.set_yticklabels(class_names)
-    ax.invert_yaxis()
-    ax.set_xlabel('Probability')
-    ax.set_xlim(0, 1)
-    ax.set_title('Predicted Probabilities for Each Condition')
-    plt.tight_layout()
-    return fig
+    
+    # Create a DataFrame with the results
+    df = pd.DataFrame({
+        'Condition': class_names,
+        'Probability': probs_np,
+        'Present': present
+    })
+    
+    # Create an Altair bar chart
+    chart = alt.Chart(df).mark_bar().encode(
+        x=alt.X('Probability:Q', scale=alt.Scale(domain=(0, 1))),
+        y=alt.Y('Condition:N', sort='-x'),
+        color=alt.condition(
+            alt.datum.Present,
+            alt.value('green'),  # Color for conditions present
+            alt.value('red')     # Color for conditions not present
+        )
+    ).properties(
+        title='Predicted Probabilities for Each Condition',
+        width=600,
+        height=400
+    )
+    
+    # Return the chart
+    return chart
 
 # Create the Gradio interface
 interface = gr.Interface(
@@ -53,6 +69,6 @@ interface = gr.Interface(
 )
 
 # Launch the app
-interface.launch()
-
+if __name__ == "__main__":
+    interface.launch()