Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import pandas as pd
 import joblib
 import numpy as np
-from typing import Dict, Any
+from typing import Dict, Any, List
 
 # 1. Load the Model and Artifacts
 # Model and artifact paths - adjust as necessary
@@ -87,7 +87,7 @@ def preprocess_data(data: Dict[str, Any]) -> pd.DataFrame:
         return pd.DataFrame()
 
 # 3. Prediction Function
-def predict_vendor_score(*args: Any) -> Dict[str, Any]:
+def predict_vendor_score(*args: Any) -> List[Any]: # Change return type
     """
     Predicts the vendor performance score based on the input data from Vendor_Log__c.
     Returns a dictionary containing the Subcontractor_Performance_Score__c fields.
@@ -97,11 +97,16 @@ def predict_vendor_score(*args: Any) -> Dict[str, Any]:
                 the Vendor_Log__c fields.
 
     Returns:
-        dict: A dictionary containing the Subcontractor_Performance_Score__c fields
-              and a final score percentage.
+        List[Any]:  A list of output values.
     """
     if model is None:
-        return {'Final_Score_c': 'Model not loaded.  Check server logs.', 'error': True}
+        return [
+            'Model not loaded.  Check server logs.',  # Quality Score
+            'Model not loaded.  Check server logs.',  # Timeliness Score
+            'Model not loaded.  Check server logs.',  # Safety Score
+            'Model not loaded.  Check server logs.',  # Communication Score
+            'Model not loaded.  Check server logs.',  # Final Score
+        ]
 
     # 1. Prepare input data as a dictionary, mapping to Vendor_Log__c fields
     input_data = {
@@ -117,10 +122,13 @@ def predict_vendor_score(*args: Any) -> Dict[str, Any]:
     # 2. Preprocess the data
     processed_df = preprocess_data(input_data)
     if processed_df.empty:
-        return {
-            'Final_Score_c': 'Error in input data. Check logs.',
-            'error': True
-        }
+        return [
+            'Error in input data. Check logs.', # Quality Score
+            'Error in input data. Check logs.', # Timeliness Score
+            'Error in input data. Check logs.', # Safety Score
+            'Error in input data. Check logs.', # Communication Score
+            'Error in input data. Check logs.'  # Final Score
+        ]
 
     # 3. Make Prediction
     try:
@@ -130,34 +138,36 @@ def predict_vendor_score(*args: Any) -> Dict[str, Any]:
         #   IMPORTANT:  Adjust this mapping based on your model's output.
         #   This is a *crucial* step where you tell Gradio how to interpret
         #   the numbers coming from your model.
-        output_data = {
-            'Quality_Score__c': predictions[0] * 100,  # Convert to percentage (0-100 range)
-            'Timeliness_Score__c': predictions[1] * 100,
-            'Safety_Score__c': predictions[2] * 100,
-            'Communication_Score__c': predictions[3] * 100,
-            'Final_Score_c': (predictions[0] + predictions[1] + predictions[2] + predictions[3]) / 4 * 100, #example
-            'error': False,
-        }
+        output_data = [
+            predictions[0] * 100,  # Quality Score (%)
+            predictions[1] * 100,  # Timeliness Score (%)
+            predictions[2] * 100,  # Safety Score (%)
+            predictions[3] * 100,  # Communication Score (%)
+            (predictions[0] + predictions[1] + predictions[2] + predictions[3]) / 4 * 100, # Final Score (%)
+        ]
         return output_data
 
     except Exception as e:
         print(f"Error during prediction: {e}")
-        return {
-            'Final_Score_c': f'Error during prediction: {e}',
-            'error': True
-        }
+        return [
+            f'Error during prediction: {e}', # Quality Score
+            f'Error during prediction: {e}', # Timeliness Score
+            f'Error during prediction: {e}', # Safety Score
+            f'Error during prediction: {e}', # Communication Score
+            f'Error during prediction: {e}'  # Final Score
+        ]
 
 # 4. Gradio Interface
 iface = gr.Interface(
     fn=predict_vendor_score,
     inputs=[
-        gr.Textbox(label="Quality Report (Long Text)", type="text"),  #  Consistent
+        gr.Textbox(label="Quality Report (Long Text)", type="text"),
         gr.Number(label="Delay Days (Number)"),
         gr.Textbox(label="Incident Log (Long Text)", type="text"),
         gr.Textbox(label="Vendor ID (Text)"),  # Send ID
         gr.Textbox(label="Work Details (Long Text)", type="text"),
-        gr.DateTime(label="Work Completion Date"), # Changed to gr.DateTime
-        gr.DateTime(label="Actual Completion Date"), # Changed to gr.DateTime
+        gr.DateTime(label="Work Completion Date", type="datetime"),  # Corrected to "datetime"
+        gr.DateTime(label="Actual Completion Date", type="datetime"), # Corrected to "datetime"
     ],
     outputs=[ # Changed to a list
         gr.HighlightedText(label="Quality Score (%)"),
@@ -174,3 +184,4 @@ iface = gr.Interface(
 # 5. Launch the Interface
 if __name__ == "__main__":
     iface.launch()
+