'''
import gradio as gr

def greet(name):
    return "Hello " + name + "!"

demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
    
demo.launch(share=True)
'''
import gradio as gr
from transformers import pipeline

# Load the plant identification model
classifier = pipeline("image-classification", model="umutbozdag/plant-identity")

# Define the prediction function
def identify_plant(image):
    results = classifier(image)
    # Format top result
    top_result = results[0]
    label = top_result['label']
    score = round(top_result['score'] * 100, 2)
    return f"Prediction: {label} ({score}%)"

# Create Gradio interface
gr.Interface(
    fn=identify_plant,
    inputs=gr.Image(type="filepath", label="Upload Plant Image"),
    outputs=gr.Text(label="Plant Identification Result"),
    title="Plant Identifier",
    description="Upload an image of a plant to identify its species using a Hugging Face model."
).launch()