Sync App from main repo

app.py

@@ -1,15 +1,17 @@
 import streamlit as st
 import requests
 import pandas as pd
-
+import matplotlib.pyplot as plt
 st.title("Arrhythmia Detection")
 
-models = {"CNN Binary": "cnn_binary_model.h5",
-          "LSTM Binary": "lstm_binary_model.h5",
-          "PCA XGBoost Binary": "pca_xgboost_binary_model.pkl",
-          "CNN Multi": "cnn_multi_model.h5",
-          "LSTM Multi": "lstm_multi_model.h5",
-          "PCA XGBoost Multi": "pca_xgboost_multi_model.pkl"}
+models = {
+    "LSTM Multi": "lstm_multi_model.h5",
+    "CNN Multi": "cnn_multi_model.h5",
+    "PCA XGBoost Multi": "pca_xgboost_multi_model.pkl",
+    "LSTM Binary": "lstm_binary_model.h5",
+    "CNN Binary": "cnn_binary_model.h5",
+    "PCA XGBoost Binary": "pca_xgboost_binary_model.pkl",
+    }
 
 # Model selection
 model_name = st.selectbox("Select a Model", list(models.keys()))
@@ -18,15 +20,23 @@ model_name = st.selectbox("Select a Model", list(models.keys()))
 uploaded_file = st.file_uploader("Upload a CSV file", type="csv")
 if uploaded_file is not None:
     df = pd.read_csv(uploaded_file)
-    st.write("Uploaded Data:", df)
+    # st.write("Uploaded Data:", df)
+
+    st.write("Visualized Data:")
+    fig, ax = plt.subplots(figsize=(10, 6))
+    df.plot(ax=ax)
+    st.pyplot(fig)
 
     if st.button("Predict"):
         model = models[model_name]
 
-        # Call the API
+        # Reset the file pointer to the beginning
+        uploaded_file.seek(0)
+
+        # Call the API with the file directly
         response = requests.post(
-            "https://fabriciojm-hadt-api.hf.space/predict/",
-            json={"model_name": model, "input_data": df},
+            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: