Update app.py

app.py

@@ -85,9 +85,10 @@ index_to_word = StringLookup(
 
 ## Probabilistic prediction using the trained model
 def predict_caption(file):
+    filename = Image.open(file)
     gru_state = tf.zeros((1, ATTENTION_DIM))
     
-    img = tf.image.decode_jpeg(tf.io.read_file(filename), channels=IMG_CHANNELS)
+    img = tf.image.decode_jpeg(filename, channels=IMG_CHANNELS)
     img = tf.image.resize(img, (IMG_HEIGHT, IMG_WIDTH))
     img = img / 255
     
@@ -117,38 +118,15 @@ def predict_caption(file):
 
     return img, result
 
-
-filename = "../sample_images/surf.jpeg"  # you can also try surf.jpeg
-
-for i in range(5):
-    image, caption = predict_caption(filename)
-    print(" ".join(caption[:-1]) + ".")
-
-img = tf.image.decode_jpeg(tf.io.read_file(filename), channels=IMG_CHANNELS)
-plt.imshow(img)
-plt.axis("off")
-
-
-filename = np.array(Image.open(file).convert('RGB'))
-
-def model_prediction(path):
-    resize = tf.image.resize(path, (256,256))
-    with st.spinner('Model is being loaded..'):
-        model=load_image_model()
-    yhat = model.predict(np.expand_dims(resize/255, 0))
-    return yhat
-
 def on_click():
     if file is None:
         st.text("Please upload an image file")
     else:
         image = Image.open(file)
         st.image(image, use_column_width=True)
-        image = image.convert('RGB')
-        predictions = model_prediction(np.array(image))
-        if (predictions>0.5):
-            st.write("""# Prediction : Implant is loose""")
-        else:
-            st.write("""# Prediction : Implant is in control""")
-
-st.button('Predict', on_click=on_click)
+        for i in range(5):
+            image, caption = predict_caption(file)
+            #print(" ".join(caption[:-1]) + ".")
+            st.write(" ".join(caption[:-1]) + ".")
+
+st.button('Generate', on_click=on_click)