plant detection model test

app.py

@@ -1,3 +1,4 @@
+'''
 import gradio as gr
 
 def greet(name):
@@ -5,4 +6,28 @@ def greet(name):
 
 demo = gr.Interface(fn=greet, inputs="textbox", outputs="textbox")
     
-demo.launch(share=True)
+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()

requirements.txt

@@ -1 +1,3 @@
+transformers
+torch
 gradio