Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from transformers import pipeline
 from huggingface_hub import hf_hub_download
 import json
+import numpy as np
 
 # Define model repository details
 model_repo = "KhadijaAsehnoune12/LeafDiseaseDetector"
@@ -18,30 +19,9 @@ with open(config_path, "r", encoding="utf-8") as f:
 pipe = pipeline(task="image-classification", model=model_repo)
 
 # Define a custom prediction function
-import numpy as np
-
-from PIL import Image
-from io import BytesIO
-import numpy as np
-import base64
-
 def predict(image):
-    # Check if the input image is a base64 encoded string
-    if isinstance(image, str):
-        # Decode the base64 encoded image string and convert it to a PIL image object
-        image_data = BytesIO(base64.b64decode(image))
-        pil_image = Image.open(image_data)
-        # Convert the PIL image to a numpy array
-        image_np = np.array(pil_image)
-    elif isinstance(image, np.ndarray):
-        # If the input image is already a numpy array, use it directly
-        image_np = image
-    else:
-        # If the input is neither a base64 encoded string nor a numpy array, raise an error
-        raise ValueError("Input image must be either a base64 encoded string or a numpy array.")
-
     # Get the predictions from the pipeline
-    predictions = pipe(image_np)
+    predictions = pipe(image)
     # Get the predicted label index
     predicted_index = predictions[0]['label']
     # Map the index to the corresponding disease name using id2label
@@ -50,15 +30,13 @@ def predict(image):
     confidence_score = predictions[0]['score']
     return f"{label_name} ({confidence_score:.2f})"
 
-
-
 # Create Gradio interface
 iface = gr.Interface(fn=predict,
-                     inputs=gr.Image(type="numpy"),
-                     outputs=gr.Textbox(),
+                     inputs=gr.inputs.Image(type="numpy"),
+                     outputs=gr.outputs.Textbox(),
                      title="Orange Disease Image Classification",
                      description="Detect diseases in orange leaves and fruits.",
-                     examples=['MoucheB.jpg', 'verdissement.jpg'])
+                     examples=[np.random.rand(224, 224, 3)])
 
 # Launch the app
 iface.launch()