add application file

app.py

@@ -0,0 +1,59 @@
+import gradio as gr
+from fastai.vision.all import *
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load your trained FastAI model
+learn = load_learner('export.pkl') 
+
+def predict_and_plot(img):
+    # Get predictions
+    pred, pred_idx, probs = learn.predict(img)
+    # Get 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']
+    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 the Gradio interface
+interface = gr.Interface(
+    fn=predict_and_plot,
+    inputs=gr.Image(type='pil'),
+    outputs=gr.Plot(),
+    title="Dr. Macloomber",
+    description="Upload an image of a retina to predict the probabilities of various eye conditions."
+)
+
+# Launch the app
+interface.launch()
+
+