Update app.py

app.py

@@ -1,10 +1,34 @@
 import gradio as gr
 from transformers import pipeline
+import json
 
-pipe = pipeline(task="image-classification", 
-                model="KhadijaAsehnoune12/LeafDiseaseDetector")
-gr.Interface.from_pipeline(pipe, 
-                           title="Orange Disease Image Classification",
-                           description="Detect diseases in orange leaves and fruits.",
-                           examples = ['MoucheB.jpg', 'verdissement.jpg',],
-                           ).launch()
+# Load the config.json file
+config_path = "KhadijaAsehnoune12/LeafDiseaseDetector/config.json"  # Update this path
+with open(config_path, "r", encoding="utf-8") as f:
+    config = json.load(f)
+
+# Initialize the pipeline
+pipe = pipeline(task="image-classification", model="KhadijaAsehnoune12/LeafDiseaseDetector")
+
+# Define a custom prediction function
+def predict(image):
+    # Get the predictions from the pipeline
+    predictions = pipe(image)
+    # Get the predicted label index
+    predicted_index = predictions[0]['label']
+    # Map the index to the corresponding disease name using id2label
+    label_name = config["id2label"][str(predicted_index)]
+    # Optionally, you can add the confidence score as well
+    confidence_score = predictions[0]['score']
+    return f"{label_name} ({confidence_score:.2f})"
+
+# Create Gradio interface
+iface = gr.Interface(fn=predict,
+                     inputs=gr.inputs.Image(type="numpy"),
+                     outputs="text",
+                     title="Orange Disease Image Classification",
+                     description="Detect diseases in orange leaves and fruits.",
+                     examples=['MoucheB.jpg', 'verdissement.jpg'])
+
+# Launch the app
+iface.launch()