feat: model, app, req

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.pkl filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,39 @@
+import gradio as gr
+import torch
+from torch import tensor
+from torch.nn import functional as F
+from sklearn.preprocessing import LabelEncoder
+import pandas as pd
+
+label_encoder = LabelEncoder()
+
+model = torch.load('model.pkl')
+
+def calc(X_test):
+    # return model.predict(X_test)
+    return X_test
+
+def main(inputs):
+    df = pd.DataFrame(columns=inputs[1:])
+    df.loc[0] = inputs[1:]
+
+    for column in df.columns:
+        df[column] = label_encoder.fit_transform(df[column])
+
+    t_indep = tensor(df[indep_cols].values, dtype=torch.float)
+    vals,indices = t_indep.max(dim=0)
+    t_indep = t_indep / vals
+    return calc_preds(coeffs, t_indep)
+
+iface = gr.Interface(
+    fn=calc,
+    inputs=[gr.DropDown('Feature 1', choices=['Option 1', 'Option 2', 'Option 3']),
+            gr.DropDown('Feature 2', choices=['Option A', 'Option B', 'Option C']),
+            # Add more DropDown or other input components as needed
+            ],
+    outputs="text",
+    title="Heart Disease identifier",
+    description="Identifies if a person has/will have a heart disease"
+)
+
+iface.launch()

model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a72df222ca0d5752241edf45b1670fde6d6391e047d0095ad24d5485bde9f4eb
+size 193440658

requirements.txt

@@ -0,0 +1 @@
+fastai