Update app.py

app.py

@@ -1,32 +1,27 @@
-from fastapi import FastAPI, Request
-from fastapi.responses import HTMLResponse
-from transformers import pipeline
-import gradio as gr
+# app.py
+from fastapi import FastAPI, File, UploadFile
+from fastapi.middleware.cors import CORSMiddleware
+from predict import read_image, transformacao
 
-# 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")
+# Add CORS middleware to allow requests from any origin (for development)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
 
-# Define route for Gradio interface
 @app.get("/")
-async def gr_interface_route(request: Request):
-    return HTMLResponse(gr_interface.launch(request))
+async def root():
+    return {"message": "Welcome to the medicinal plants image detection API!"}
 
-# Expose the FastAPI app using Uvicorn (for local testing)
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
+@app.post("/uploadfile/")
+async def create_upload_file(file: UploadFile = File(...)):
+    contents = await file.read()
+    # read image
+    imagem = read_image(contents)
+    # transform and prediction 
+    prediction = transformacao(imagem)
+    return prediction