Update app.py

app.py

@@ -1,30 +1,48 @@
+from fastai.vision.all import *
+from io import BytesIO
+import requests
 import streamlit as st
 
-# 示例图片的路径
-example_images = {
-    "Wind Energy": "exwind.jpg"
-}
-
-# 用户选择的能源类型
-category = st.radio("Choose the energy type of the image to upload", ["Hydro Energy", "Wind Energy", "Solar Energy"])
-
-# 显示选定能源类型的示例图片
-if category:
-    st.write(f"Example of a {category} image:")
-    st.image(example_images[category], caption=f"Example {category}", use_column_width=True)
-
-    option = st.radio("", ["Upload Image", "Image URL"])
-    if option == "Upload Image":
-        uploaded_file = st.file_uploader("Please upload an image.")
-        if uploaded_file is not None:
-            img = PILImage.create(uploaded_file)
-            predict(img, category)
-    else:
-        url = st.text_input("Please input a url.")
-        if url:
-            try:
-                response = requests.get(url)
-                pil_img = PILImage.create(BytesIO(response.content))
-                predict(pil_img, category)
-            except requests.exceptions.RequestException as e:
-                st.error(f"Problem reading image from the URL: {url}\nError: {str(e)}")
+# 标题和应用描述
+st.title("HeartNet")
+st.write("This is a classifier for images of 12-lead EKGs. It will attempt to detect whether the EKG indicates an acute MI. It was trained on simulated images.")
+
+# 展示示例图片
+st.write("## Example of an EKG image to upload")
+st.image("exwind.jpg", caption="Example EKG Image", use_column_width=True)
+
+def predict(img):
+    st.image(img, caption="Your image", use_column_width=True)
+    pred, key, probs = learn_inf.predict(img)
+
+    result_message = f"""
+    ## This **{'is ' if pred == 'mi' else 'is not'}** an MI (heart attack).
+    ### Prediction result: {pred}
+    ### Probability of {pred}: {probs[key].item()*100: .2f}%
+    """
+    st.write(result_message)
+
+# 加载模型
+path = "./"
+learn_inf = load_learner(path + "resnet34_stage_4.pkl")
+
+# 用户上传图片或提供图片 URL
+option = st.radio("", ["Upload Image", "Image URL"])
+
+if option == "Upload Image":
+    uploaded_file = st.file_uploader("Please upload an image.")
+
+    if uploaded_file is not None:
+        img = PILImage.create(uploaded_file)
+        predict(img)
+
+else:
+    url = st.text_input("Please input a URL for an image.")
+
+    if url != "":
+        try:
+            response = requests.get(url)
+            pil_img = PILImage.create(BytesIO(response.content))
+            predict(pil_img)
+        except Exception as e:
+            st.error(f"Problem reading image from the URL. Please check the URL and try again. Error: {str(e)}")