Update app.py

app.py

@@ -1,27 +1,33 @@
 import gradio as gr
 import tensorflow as tf
-model_0 = tf.keras.models.load_model('bestmodel_porno_final_meilleure100%2.0.h5')
-def classify_image(inp):
-    inp = inp.reshape((-1, 224, 224, 3))
-    prediction = model_0.predict(inp)
-    if prediction.argmax() == 0:
-      output = "Rifle violence"
-    elif prediction.argmax() == 1:
-      output = "guns violence"
-    elif prediction.argmax() == 2:
-      output = "knife violence"
-    elif prediction.argmax() == 3:
-      output = "image porno"
-    elif prediction.argmax() == 4:
-      output = "personne habillée" 
+import numpy as np 
+import cv2
+
+new_model = tf.keras.models.load_model('best_model_mammography.h5')
+
+def classify_image(file_name):
+    img1 = cv2.imread(file_name.name.replace("\\",'/'),0)
+    img = cv2.resize(img1, (224,224))
+    img = img.reshape(img.shape[0],img.shape[1],1)
+    pred = new_model.predict(np.array([img]))
+    pred = np.round(pred,1)
+    if pred == 0:
+        pred = "Vôtre cancer est Begnine"
     else:
-      output = "tank violence" 
-    return output
+        pred= "Vôtre cancer est maline"
+    return pred
 
 
-image = gr.inputs.Image(shape=(224, 224))
-label = gr.outputs.Label(num_top_classes=3)
+image = gr.inputs.File( file_count="single",type="file", label="Fichier à Traiter (sous fichier .pgm)")
+# label = gr.outputs.Label(num_top_classes=3)
 
 gr.Interface(
-    fn=classify_image, inputs=image, outputs=label, interpretation="default"
+    fn=classify_image, 
+    inputs=image, 
+    outputs="text", 
+    interpretation="default",
+    live=True,
+    theme="dark-peach",
+    title="API BREASTNET de Test de diagnostique du Cancer de Sein",
+    description="Cette API est utilisé pour dire si le Cancer de sein est Maline ou Pas"
 ).launch()