Upload app.py (#1)

- Upload app.py (12896f33a216402a1d302a6279a7123bbb1cc929)


Co-authored-by: Lontsi hermann <[email protected]>

app.py

@@ -1,10 +1,17 @@
+from io import BytesIO
 import gradio as gr
 import tensorflow as tf
+import logging
+import requests
 import numpy as np
 from PIL import Image
 from tensorflow.keras.utils import CustomObjectScope
 from tensorflow.keras.layers.experimental.preprocessing import RandomHeight
 
+logging.basicConfig(
+    level=logging.WARNING,  
+    format="%(asctime)s - %(levelname)s - %(message)s",  
+)
 with CustomObjectScope({'RandomHeight': RandomHeight}):
     model_0 = tf.keras.models.load_model('bestmodel.h5')
 
@@ -21,14 +28,37 @@ if gpus:
     except RuntimeError as e:
         print(e)
 
+def clean_url(input_string):
+    if "http" in input_string:
+        return input_string[input_string.find("http"):]
+    return input_string
+
 
 def classify_image(inp):
     # Convert to PIL Image if we have a numpy array
     image = None
+    logging.warning("entree dans ckassify_image")
     if isinstance(inp, np.ndarray):
         image = Image.fromarray(inp)
+        logging.warning("1")
+
+    if isinstance(inp, str) :
+        logging.warning("2")
+
+        inp = clean_url(inp)
+        response = requests.get(inp)
+        response.raise_for_status()
+        image = Image.open(BytesIO(response.content))
+    if isinstance(inp, str) and (inp.startswith("http://") or inp.startswith("https://")):
+        logging.warning("3")
+        response = requests.get(inp)
+        response.raise_for_status()
+        image = Image.open(BytesIO(response.content))
+       
     else:
-        image = inp
+              logging.warning("4")
+
+              image = inp
     
     # Resize image to 224x224
     image = image.resize((224, 224), Image.Resampling.LANCZOS)
@@ -62,8 +92,6 @@ def classify_image(inp):
     return output
 
 
-image = gr.Image(height=224, width=224)
-
 gr.Interface(
-    fn=classify_image, inputs=image, outputs="text",live=True,title="API de détection des images violentes",
-).launch()
+    fn=classify_image, inputs=gr.Text(label="Url de l image"), outputs="text",live=True,title="API de détection des images violentes",
+).launch(share=True)