trying something simple

app.py

@@ -1,74 +1,72 @@
 import gradio as gr
 from fastai.vision.all import *
-import altair as alt
-import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from transformers import pipeline 
 
-# Load your trained FastAI model
-learn = load_learner('export.pkl')
+pipe = pipeline(
+    task="image-classification",
+    model ="export.pkl",
+    description="Retinal Condition Classifier",
+    examples=['sick-eye.jpeg', 'healthy.jpg']
+    ).launch()
 
-def predict_and_plot(img):
-    # Get predictions
-    pred, pred_idx, probs = learn.predict(img)
-    
-    # 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
-    
-    # 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(
-    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
-if __name__ == "__main__":
-    interface.launch()
+
+
+
+
+# # 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()