Update app.py

app.py

@@ -1,6 +1,5 @@
 import streamlit as st
 import tensorflow as tf
-import random
 from PIL import Image
 from tensorflow import keras
 import numpy as np
@@ -18,19 +17,14 @@ st.set_page_config(
 )
 
 hide_streamlit_style = """
-	<style>
-  #MainMenu {visibility: hidden;}
-	footer {visibility: hidden;}
-  </style>
+    <style>
+    #MainMenu {visibility: hidden;}
+    footer {visibility: hidden;}
+    </style>
 """
 st.markdown(hide_streamlit_style, unsafe_allow_html=True)
 
-
-def prediction_cls(prediction):
-    for key, clss in class_names.items():
-        if np.argmax(prediction) == clss:
-            return key
-
+class_names = ["Normal", "PNEUMONIA"]
 
 with st.sidebar:
     st.title("ChestAI")
@@ -51,33 +45,32 @@ st.set_option("deprecation.showfileUploaderEncoding", False)
 @st.cache_resource(show_spinner=False)
 def load_model():
     try:
+        # Attempt to load the model from Hugging Face
         from huggingface_hub import from_pretrained_keras
         keras.utils.set_random_seed(42)
         model = from_pretrained_keras("ryefoxlime/PneumoniaDetection")
         return model
     except Exception as e:
-        st.error(f"Error loading model: {str(e)}")
+        st.error(f"Error loading model from Hugging Face: {str(e)}")
         return None
 
 with st.spinner("Model is being loaded..."):
     model = load_model()
 
 if model is None:
-    st.error("Failed to load model. Please try again.")
+    st.error("Failed to load model from Hugging Face. Please check the model name or path.")
     st.stop()
 
 file = st.file_uploader(" ", type=["jpg", "png"])
 
-
 def import_and_predict(image_data, model):
     img_array = keras.preprocessing.image.img_to_array(image_data)
     img_array = np.expand_dims(img_array, axis=0)
-    img_array = img_array/255
+    img_array = img_array / 255
 
     predictions = model.predict(img_array)
     return predictions
 
-
 if file is None:
     st.text("Please upload an image file")
 else:
@@ -85,22 +78,17 @@ else:
         image = keras.preprocessing.image.load_img(file, target_size=(224, 224), color_mode='rgb')
         st.image(image, caption="Uploaded Image.", use_column_width=True)
         predictions = import_and_predict(image, model)
-        
-        class_names = [
-            "Normal",
-            "PNEUMONIA",
-        ]
 
         confidence = float(max(predictions[0]) * 100)
         prediction_label = class_names[np.argmax(predictions)]
-        
+
         st.info(f"Confidence: {confidence:.2f}%")
-        
+
         if prediction_label == "Normal":
             st.balloons()
             st.success(f"Result: {prediction_label}")
         else:
             st.warning(f"Result: {prediction_label}")
-            
+
     except Exception as e:
-        st.error(f"Error processing image: {str(e)}")
+        st.error(f"Error processing image: {str(e)}")