Update app.py

app.py

@@ -1,3 +1,31 @@
+import streamlit as st
+from PIL import Image, ImageOps
+import numpy as np
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+import tensorflow as tf  # Use TensorFlow's Keras API
+
+# Load the TensorFlow Keras model
+model = tf.keras.models.load_model('gastrointestinal_model.h5', compile=False)
+
+# Load class names
+with open('labels.txt', 'r') as f:
+    class_names = f.readlines()
+
+# Function to create plot
+def create_plot(data):
+    sns.set_theme(style="whitegrid")
+    f, ax = plt.subplots(figsize=(5, 5))
+    sns.set_color_codes("pastel")
+    sns.barplot(x="Total", y="Labels", data=data, label="Total", color="b")
+    sns.set_color_codes("muted")
+    sns.barplot(x="Confidence Score", y="Labels", data=data, label="Confidence Score", color="b")
+    ax.legend(ncol=2, loc="lower right", frameon=True)
+    sns.despine(left=True, bottom=True)
+    return f
+
+# Function to predict gastrointestinal conditions
 def predict_gastrointestinal(img):
     np.set_printoptions(suppress=True)
     data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
@@ -7,10 +35,6 @@ def predict_gastrointestinal(img):
     normalized_image_array = (image_array.astype(np.float32) / 127.0) - 1
     data[0] = normalized_image_array
     prediction = model.predict(data)
-    
-    # Debugging: Print raw prediction values
-    st.write("Raw prediction values:", prediction)
-    
     index = np.argmax(prediction)
     class_name = class_names[index].strip()
     confidence_score = prediction[0][index]
@@ -30,3 +54,20 @@ def predict_gastrointestinal(img):
         prediction_text = f"The image shows signs of {class_name}."
     
     return prediction_text, plot
+
+# Streamlit app
+#st.title("Gastrointestinal Classification Web App")
+######
+st.write("Loaded class names:", class_names)
+
+uploaded_file = st.file_uploader("Upload a gastrointestinal image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+    st.write("Classifying...")
+
+    prediction, plot = predict_gastrointestinal(image)
+    st.write(prediction)
+    st.pyplot(plot)
+