Update app.py

app.py

@@ -1,20 +1,62 @@
+import numpy as np
+from keras.models import load_model
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+import matplotlib.pyplot as plt
+import os
 import streamlit as st
-from transformers import pipeline
-from PIL import Image
+from styling import footer
 
-pipeline = pipeline(task="image-classification", model="Owos/tb-classifier")
+st.cache(allow_output_mutation=True)
+st.title("TB Image Classifier")
 
-st.title("Hot Dog? Or Not?")
+# loading model
+model = load_model('tb_model.h5')
+# loading the imaage
 
-file_name = st.file_uploader("Upload a hot dog candidate image")
+file = st.file_uploader(
+    "Upload the image",
+    type=["png", "jpg"],
+    accept_multiple_files=False,
+    key=None,
+    help=None,
+    on_change=None,
+    args=None,
+    kwargs=None,
+)
 
-if file_name is not None:
-  col1, col2 = st.columns(2)
+run = st.button(
+    "Make Prediction", key=None, help=None, on_click=None, args=None, kwargs=None
+)
+st.subheader("This app classifies an x-ray image if it has TB or not")
+# image laoder
+def load_image(img_path, img_size, show=False):
+    img = image.load_img(img_path, target_size=img_size)
+    img_tensor = image.img_to_array(img)
+    img_tensor = np.expand_dims(img_tensor, axis=0)  # expanding image tensor
+    img_tensor = img_tensor / 255.0  # scaling the image_T
 
-  image = Image.open(file_name)
-  col1.image(image, use_column_width=True)
-  predictions = pipeline(image)
+    if show:
+        plt.imshow(img_tensor[0])
+        plt.axis("off")
+        plt.show()
+    return img_tensor
 
-  col2.header("Probabilities")
-  for p in predictions:
-    col2.subheader(f"{ p['label'] }: { round(p['score'] * 100, 1)}%")
+
+img_size = (300, 300)
+img_path = "inference image from medscape.jpg"
+
+classes = ["Negative", "Positive"]#["Normal", "Tuberculosis"]
+if __name__ == "__main__":
+    ## load img
+    footer()
+    if run == True:
+        if file is not None:
+            img = load_image(img_path, img_size)
+            pred = model.predict(img)
+            output = classes[round(pred[0][0])]
+            st.subheader(f"The image is {output}")
+        else:
+            st.write("Please upload an image first")
+
+            # st.image(file)