Update app.py

app.py

@@ -1,33 +1,39 @@
-import sklearn
 import gradio as gr
 import joblib
 import pandas as pd
 import datasets
+import json
 
+# Load the model
 pipe = joblib.load("./model.pkl")
 
-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."
-
+title = "Premium Amount Prediction"
+description = "This model predicts the Premium Amount. Drag and drop any slice from the dataset or edit values as you wish in the dataframe component below."
 
+# Load configuration
 with open("./config.json") as f:
-    config_dict = eval(f.read())
+    config_dict = json.load(f)
 headers = config_dict["sklearn"]["columns"]
 
-df = datasets.load_dataset("silvaKenpachi/mental_health")
-df = df["train"].to_pandas()
+# Load and prepare dataset
+df = datasets.load_dataset("silvaKenpachi/mental_health")["train"].to_pandas()
 df.dropna(axis=0, inplace=True)
 
-
-
-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=["Depression"])]
-
+# Define input and output interfaces
+inputs = [gr.Dataframe(headers=headers, row_count=(2, "dynamic"), col_count=(len(headers), "fixed"), label="Input Data", interactive=True)]
+outputs = [gr.Dataframe(row_count=(2, "dynamic"), col_count=(1, "fixed"), label="Predictions", headers=["Depression"])]
 
 def infer(inputs):
     data = pd.DataFrame(inputs, columns=headers)
-    predictions = pipe.predict(inputs)
+    predictions = pipe.predict(data)
     return pd.DataFrame(predictions, columns=["Depression"])
 
-gr.Interface(infer, inputs = inputs, outputs = outputs, title = title,
-            description = description, examples=[df.head(3)], cache_examples=False).launch(debug=True)
+gr.Interface(
+    fn=infer,
+    inputs=inputs,
+    outputs=outputs,
+    title=title,
+    description=description,
+    examples=[df[headers].head(3).values.tolist()],
+    cache_examples=False
+).launch(debug=True)