Sync App from main repo

app.py

@@ -38,11 +38,32 @@ classification_selected = st.selectbox("Classification type", classification)
 
 model_name = f"{model_selected} {classification_selected}"
 
+def visualize_single(df, st):
+    st.write("Visualized Data:")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    df.iloc[0].plot(ax=ax)
+    st.pyplot(fig)
+
+# This function will be used when the API is capable of returning extracted beats
+# def visualize_multiple(beats, st):
+#     st.write("Visualized Data:")
+#     if len(beats) % 4 != 0:
+#         nrows = len(beats) // 4 + 1
+#     else:
+#         nrows = len(beats) // 4
+#     fig, axs = plt.subplots(nrows, 4, figsize=(10, nrows*2.5))
+#     for i, beat in enumerate(beats):
+#         axs.flatten()[i].plot(beat)
+#     # delete last plots if not used
+#     for j in range(len(beats)%4):
+#         fig.delaxes(axs.flatten()[-j-1])
+#     st.pyplot(fig)
+
 st.markdown("""Upload a CSV file with single heartbeat (csv with 180 points) or load from available examples 
 """)
 
 # Option to upload or load a file
-option = st.radio("Choose input method", ("Load example file", "Upload CSV file", "Upload Apple Watch ECG CSV file"))
+option = st.radio("Choose input method", ("Load example file", "Upload CSV file", "Upload Apple Watch ECG CSV file (EXPERIMENTAL)"))
 
 if option == "Load example file":
     # Load example files from Hugging Face dataset
@@ -57,6 +78,8 @@ if option == "Load example file":
     uploaded_file.name = example_selected  # Set a name attribute to mimic the uploaded file
     df = pd.read_csv(uploaded_file)
     # st.write("Loaded Data:", df)
+    if 'df' in locals():
+        visualize_single(df, st)
 
 elif option == "Upload CSV file":
     # File uploader
@@ -65,8 +88,10 @@ elif option == "Upload CSV file":
     if uploaded_file is not None:
         df = pd.read_csv(uploaded_file)
         # st.write("Uploaded Data:", df)
+    if 'df' in locals():
+        visualize_single(df, st)
 
-elif option == "Upload Apple Watch ECG CSV file":
+elif option == "Upload Apple Watch ECG CSV file (EXPERIMENTAL)":
     # File uploader
     st.write("DISCLAIMER: this is an experimental feature, and the results may not be accurate. This should not be used as professional medical advice.")
     uploaded_file = st.file_uploader("Upload a CSV file", type="csv")
@@ -76,29 +101,28 @@ elif option == "Upload Apple Watch ECG CSV file":
         df = pd.read_csv(uploaded_file)
         # st.write("Uploaded Data:", df)
 
-# Visualize data
-if 'df' in locals():
-    st.write("Visualized Data:")
-    fig, ax = plt.subplots(figsize=(10, 6))
-    df.iloc[0].plot(ax=ax)
-    st.pyplot(fig)
 
-    if st.button("Predict"):
-        model = models[model_name]
-
-        # Reset the file pointer to the beginning
-        uploaded_file.seek(0)
-
-        # Call the API with the file directly
-        response = requests.post(
-            f"https://fabriciojm-hadt-api.hf.space/predict?model_name={model}",
-            files={"filepath_csv": (uploaded_file.name, uploaded_file, "text/csv")}
-        )
-
-        if response.status_code == 200:
-            prediction = response.json()["prediction"]
-            st.write(f"Prediction using {model_name}:")
-            for i, p in enumerate(prediction):
-                st.write(f"Beat {i+1}: {beat_labels[p]} (class {p})") #  {beat_labels[prediction]} (class {prediction}) heartbeat
-        else:
-            st.error(f"Error: {response.json().get('detail', 'Unknown error')}")
+if st.button("Predict"):
+    model = models[model_name]
+
+    # Reset the file pointer to the beginning
+    uploaded_file.seek(0)
+
+    # Call the API with the file directly
+    base_url = "https://fabriciojm-hadt-api.hf.space/predict"
+    if option == "Upload Apple Watch ECG CSV file (EXPERIMENTAL)":
+        base_url += "_multibeats"
+    print(f"Request url: {base_url}?model_name={model}")
+    response = requests.post(
+        f"{base_url}?model_name={model}",
+        files={"filepath_csv": (uploaded_file.name, uploaded_file, "text/csv")}
+    )
+
+    if response.status_code == 200:
+        prediction = response.json()["prediction"]
+        st.write(f"Prediction using {model_name}:")
+        for i, p in enumerate(prediction):
+            st.write(f"Beat {i+1}: {beat_labels[p]} (class {p})") #  {beat_labels[prediction]} (class {prediction}) heartbeat
+    else:
+        st.error(f"Error: {response.json().get('detail', 'Unknown error')}")
+