Update app.py

app.py

@@ -1,31 +1,3 @@
-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)
@@ -35,6 +7,10 @@ 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]
@@ -54,20 +30,3 @@ 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)
-