Update app.py

app.py

@@ -1,6 +1,35 @@
+import sklearn
 import gradio as gr
+import joblib
+import pandas as pd
+import datasets
 
-title = "Breast Cancer Prediction"
-description = "This app predicts breast cancer based on digitized images of a fine needle aspirate (FNA) of a breast mass."
+pipe = joblib.load("./model.pkl")
 
-gr.Interface.load("huggingface/scikit-learn/skops-blog-example", title=title, description=description).launch()
+title = "Depression Prediction"
+description = "This model predicts depression risk. Drag and drop any slice from dataset or edit values as you wish in below dataframe component."
+
+
+with open("./config.json") as f:
+    config_dict = eval(f.read())
+headers = config_dict["sklearn"]["columns"]
+
+df = datasets.load_dataset("silvaKenpachi/mental_health")
+df = df["train"].to_pandas()
+df.dropna(axis=0, inplace=True)
+
+feature_columns = [col for col in df.columns if col != 'Premium Amount']
+
+
+
+inputs = [gr.Dataframe(headers = headers, row_count = (2, "dynamic"), col_count=(len(headers), "dynamic"), label="Input Data", interactive=1)]
+outputs = [gr.Dataframe(row_count = (2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Premium Amount"])]
+
+
+def infer(inputs):
+    data = pd.DataFrame(inputs, columns=headers)
+    predictions = pipe.predict(inputs)
+    return pd.DataFrame(predictions, columns=["results"])
+
+gr.Interface(infer, inputs = inputs, outputs = outputs, title = title,
+            description = description, examples=[df.head(3)], cache_examples=False).launch(debug=True)