| ''' | |
| 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() | |