Sync App from main repo

.gitattributes

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README.md

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----
-title: Hadt
-emoji: ⚡
-colorFrom: yellow
-colorTo: pink
-sdk: streamlit
-sdk_version: 1.41.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: Heart Arrhythmia Detection Tools
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import streamlit as st
+import requests
+import pandas as pd
+
+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"}
+
+# Model selection
+model_name = st.selectbox("Select a Model", list(models.keys()))
+
+# File uploader
+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)
+
+    if st.button("Predict"):
+        model = models[model_name]
+
+        # Call the API
+        response = requests.post(
+            "https://huggingface.co/spaces/fabriciojm/hadt-api/predict/",
+            json={"model_name": model, "input_data": df},
+        )
+
+        if response.status_code == 200:
+            prediction = response.json()["prediction"]
+            st.write(f"Prediction using {model_name}:", prediction)
+        else:
+            st.error(f"Error: {response.json().get('detail', 'Unknown error')}")