Upload app.py

app.py

@@ -1,10 +1,17 @@
 import gradio as gr
 import tensorflow as tf
+import requests
 import numpy as np
 from PIL import Image
 from tensorflow.keras.utils import CustomObjectScope
 from tensorflow.keras.layers.experimental.preprocessing import RandomHeight
 
+import os
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+
+app = FastAPI()
+
 with CustomObjectScope({'RandomHeight': RandomHeight}):
     model_0 = tf.keras.models.load_model('bestmodel.h5')
 
@@ -27,6 +34,10 @@ def classify_image(inp):
     image = None
     if isinstance(inp, np.ndarray):
         image = Image.fromarray(inp)
+    if isinstance(inp, str) and (inp.startswith("http://") or inp.startswith("https://")):
+        response = requests.get(inp)
+        response.raise_for_status()
+        image = Image.open(BytesIO(response.content))
     else:
         image = inp
     
@@ -62,8 +73,12 @@ def classify_image(inp):
     return output
 
 
-image = gr.Image(height=224, width=224)
+@app.get("/predict2")
+async def predict_custom_controller(image_url: str):
+    return JSONResponse(content=classify_image(image_url))
+
+
 
 gr.Interface(
-    fn=classify_image, inputs=image, outputs="text",live=True,title="API de détection des images violentes",
+    fn=classify_image, inputs=gr.Image(height=224, width=224,type="filepath",label="Image ou Url"), outputs="text",live=True,title="API de détection des images violentes",
 ).launch()