Update app.py

app.py

@@ -1,7 +1,103 @@
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+import os
+import uuid
+import joblib
+import json
 import gradio as gr
+import pandas as pd
+from huggingface_hub import CommitScheduler
+from pathlib import Path
+
+# Configure the logging functionality
+log_file = Path("logs/") / f"data_{uuid.uuid4()}.json"
+log_folder = log_file.parent
+
+repo_id = "operand-logs"
+
+# Create a commit scheduler
+scheduler = CommitScheduler(
+    repo_id=repo_id,
+    repo_type="dataset",
+    folder_path=log_folder,
+    path_in_repo="data",
+    every=2
+)
+
+# # Load the saved model
+# #insurance_charge_predictor = joblib.load('model.joblib')
+
+# # Define the input features
+
+# #numeric_features = ['age', 'bmi', 'children']
+# #categorical_features = ['sex', 'smoker', 'region']
+
+# age_input = gr.Number(label="Age")
+# bmi_input = gr.Number(label="BMI")
+# children_input = gr.Number(label="Children")
+
+# # sex: ['female' 'male']
+# # smoker: ['yes' 'no']
+# # region: ['southwest' 'southeast' 'northwest' 'northeast']
+
+# sex_input = gr.Dropdown(['female','male'],label='Sex')
+# smoker_input = gr.Dropdown(['yes','no'],label='Smoker')
+# region_input = gr.Dropdown(['southwest', 'southeast', 'northwest', 'northeast'],label='Region')
+
+# model_output = gr.Label(label="charges")
+
+# Define the predict function which will take features, convert to dataframe and make predictions using the saved model
+# the functions runs when 'Submit' is clicked or when a API request is made
+
+def dprocess(age, bmi, children, sex, smoker, region):
+    
+    #Index(['age', 'sex', 'bmi', 'children', 'smoker', 'region'], dtype='object')
+
+    sample = {
+        'age': age,
+        'sex': sex,
+        'bmi': bmi,
+        'children': children,
+        'smoker': smoker,
+        'region': region
+    }
+
+    data_point = pd.DataFrame([sample])
+    
+    prediction = insurance_charge_predictor.predict(data_point).tolist()
+
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps(
+                {
+                    'age': age,
+                    'sex': sex,
+                    'bmi': bmi,
+                    'children': children,
+                    'smoker': smoker,
+                    'region': region,
+                    'prediction': prediction[0]
+                }
+            ))
+            f.write("\n")
+
+    return prediction[0]
+
+# Set-up the Gradio UI
+textbox = gr.Textbox(label='Command:')
+company = gr.Radio(label='Company:',
+                   choices=["aws", "google", "IBM", "Meta", "msft"],
+                   value="aws")
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Create Gradio interface
+# For the inputs parameter of Interface provide [textbox,company] with outputs parameter of Interface provide prediction
+demo = gr.Interface(fn=dprocess,
+                    inputs=[textbox, company],
+                    outputs="text",
+                    title="operand data automation CLI",
+                    description="",
+                    theme=gr.themes.Soft())
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.queue()
 demo.launch()