Update app.py

app.py

@@ -1,18 +1,27 @@
 import gradio as gr
-from fastai.vision.all import *
-import skimage
+from huggingface_hub import InferenceAPI
 
-learn = load_learner('model.pkl')
-#examples = [['Images/PKG - Breast-Metastases-MSKCC/Breast-Metastases-MSKCC/HobI16-053768896760.jpeg'], ['Images/PKG - CPTAC-BRCA/BRCA/01BR001-4ffefc66-d0ba-4a36-b4fa-35bd91.jpeg']]
-labels = learn.dls.vocab
+# Specify the model name
+model_repo = "jdelgado2002/diabetic_retinopathy_detection"
+
+# Initialize the InferenceAPI client
+api = InferenceAPI(model_repo)
+
+labels = ["No DR", "Mild", "Moderate", "Severe", "Proliferative DR"]
 
 def predict(img):
-    img = PILImage.create(img)
-    pred,pred_idx,probs = learn.predict(img)
-    return {labels[i]: float(probs[i]) for i in range(len(labels))}
+    # Convert the image to a base64 string
+    img_str = gr.inputs.Image.to_base64(img, ext="jpeg")
+    
+    # Perform inference
+    inputs = {"inputs": img_str}
+    result = api(inputs)
+    
+    # Convert the result to the expected format
+    return {labels[i]: float(result[i]) for i in range(len(labels))}
 
 title = "Proliferative Retinopathy Detection"
-description = """Detects severity of diabetic retinopathy - 
+description = """Detects severity of diabetic retinopathy -
 
     0 - No DR
 
@@ -28,4 +37,4 @@ article="<p style='text-align: center'><a href='https://www.kaggle.com/code/jose
 interpretation='default'
 enable_queue=True
 
-gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=6),title=title,description=description,article=article,examples=examples , interpretation=interpretation,enable_queue=enable_queue).launch()
+gr.Interface(fn=predict,inputs=gr.inputs.Image(shape=(512, 512)),outputs=gr.outputs.Label(num_top_classes=6),title=title,description=description,article=article,interpretation=interpretation,enable_queue=enable_queue).launch()