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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"
config_filename = "config.json"
# Download the config.json file from the model repository
config_path = hf_hub_download(repo_id=model_repo, filename=config_filename)
# Load the config.json file
with open(config_path, "r", encoding="utf-8") as f:
config = json.load(f)
# Initialize the pipeline
pipe = pipeline(task="image-classification", model=model_repo)
# 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=gr.outputs.Textbox(),
title="Orange Disease Image Classification",
description="Detect diseases in orange leaves and fruits.",
examples=[np.random.rand(224, 224, 3)])
# Launch the app
iface.launch()
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