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from fastapi import FastAPI, Request
from fastapi.responses import HTMLResponse
from transformers import pipeline
import gradio as gr
# Load the model pipeline
pipe = pipeline("image-classification", "dima806/medicinal_plants_image_detection")
# Define the image classification function
def image_classifier(image):
# Perform image classification
outputs = pipe(image)
results = {}
for result in outputs:
results[result['label']] = result['score']
return results
# Define FastAPI app
app = FastAPI()
# Define Gradio Interface
gr_interface = gr.Interface(fn=image_classifier, inputs=gr.inputs.Image(), outputs="label")
# Define route for Gradio interface
@app.get("/")
async def gr_interface_route(request: Request):
return HTMLResponse(gr_interface.launch(request))
# Expose the FastAPI app using Uvicorn (for local testing)
# if __name__ == "__main__":
# import uvicorn
# uvicorn.run(app, host="0.0.0.0", port=8000)
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