Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,28 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import cv2
+
+
+model=load_model('skin_cancer_model.h5')
+def process_image(img):
+    img=img.resize((170,170))
+    img=np.array(img)
+    img=img/255.0 # normaliseren
+    img=np.expand_dims(img,axis=0)
+    return img
+
+st.title('Deri kanser resmi siniflandirma :cancer:')
+st.write('Resim sec, model kanser olup olmadigini tahmin etsin!')
+
+file=st.file_uploader('Bir resim yukle',type=['jpg','jpeg','png'])
+
+if file is not None: #Resim yuklenmisse burasi calisacak
+    img=Image.open(file)
+    st.image(img, caption='Yuklenen Resim')
+    image=process_image(img)
+    prediction=model.predict(image)
+    predicted_class= 1 if prediction > 0.5 else 0
+    class_names=['Kanser degil','Kanser']
+    st.write(class_names[predicted_class])