Update app.py

app.py

@@ -4,6 +4,8 @@ from datetime import datetime
 import pandas as pd
 from dotenv import load_dotenv
 from simple_salesforce import Salesforce
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
 import gradio as gr
 from transformers import pipeline
 
@@ -21,6 +23,16 @@ SF_DOMAIN = os.getenv("SF_DOMAIN", "login")
 # Hugging Face sentiment analysis model
 sentiment_pipeline = pipeline("sentiment-analysis")
 
+# FastAPI app
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
 # Connect to Salesforce
 def connect_to_salesforce():
     try:
@@ -39,6 +51,7 @@ def connect_to_salesforce():
 # Lookup Vendor ID by Name (assuming Vendor_Name__c references Vendor__c)
 def get_vendor_id_by_name(sf, vendor_name):
     try:
+        # Query the Vendor__c object (adjust object name if different, e.g., Account)
         query = f"SELECT Id FROM Vendor__c WHERE Name = '{vendor_name}' LIMIT 1"
         result = sf.query(query)
         if result["totalSize"] > 0:
@@ -57,13 +70,14 @@ def save_to_salesforce(data):
         return "Salesforce connection failed"
     try:
         vendor_name = data["vendor_name_id"]
+        # Always look up the vendor ID by name
         vendor_id = get_vendor_id_by_name(sf, vendor_name)
         if not vendor_id:
             return f"Error: No vendor found with name '{vendor_name}'"
 
         result = sf.Vendor_Performance__c.create({
             "Vendor_ID__c": int(data["vendor_id"]),
-            "Vendor_Name__c": vendor_id,
+            "Vendor_Name__c": vendor_id,  # Use the resolved ID
             "Score__c": data["score"],
             "Timeliness_Score__c": data["timeliness_score"],
             "Issue_Count__c": int(data["issue_score"] / 3),
@@ -82,7 +96,7 @@ def score_vendor(vendor_id, vendor_name_id, delivery_records, issue_counts, nps_
     if not vendor_id or not vendor_name_id:
         return {"error": "Vendor ID and Vendor Name are required."}
     if not (0 <= delivery_records <= 100):
-        return {"error": "Delivery Records must be 0–100"}  # Updated error message
+        return {"error": "Delivery % must be 0–100"}
     if not (0 <= nps_values <= 100):
         return {"error": "NPS must be 0–100"}
 
@@ -119,29 +133,26 @@ def format_output(vendor_id, vendor_name_id, delivery_records, issue_counts, nps
         "issue_score", "feedback_score", "evaluation_date", "rationale"
     ]]
 
-# Batch CSV Upload and Return CSV File Path
+# Batch CSV Upload
 def process_uploaded_file(file):
     try:
-        df = pd.read_csv(file)
+        df = pd.read_csv(file.name)
         required = {"vendor_id", "vendor_name_id", "delivery_records", "issue_counts", "nps_values"}
         if not required.issubset(df.columns):
-            return f"Missing required columns: {', '.join(required)}", None, None
+            return f"Missing required columns: {', '.join(required)}", None
         results = []
         for _, row in df.iterrows():
             res = score_vendor(row["vendor_id"], row["vendor_name_id"], row["delivery_records"], row["issue_counts"], row["nps_values"])
             if "error" not in res:
                 results.append(res)
         if not results:
-            return "No valid records found", None, None
-        result_df = pd.DataFrame(results)[[
+            return "No valid records found", None
+        return "Batch processed", pd.DataFrame(results)[[
             "vendor_id", "vendor_name_id", "score", "timeliness_score",
             "issue_score", "feedback_score", "evaluation_date", "rationale"
         ]]
-        output_csv_path = "processed_vendors.csv"
-        result_df.to_csv(output_csv_path, index=False)
-        return "Batch processed", result_df, output_csv_path
     except Exception as e:
-        return f"Error processing file: {e}", None, None
+        return f"Error processing file: {e}", None
 
 # Gradio UI
 with gr.Blocks(title="Vendor Performance App") as demo:
@@ -150,8 +161,8 @@ with gr.Blocks(title="Vendor Performance App") as demo:
     with gr.Tabs():
         with gr.Tab("Single Vendor"):
             vendor_id = gr.Textbox(label="Vendor ID")
-            vendor_name_id = gr.Textbox(label="Vendor Name")
-            delivery = gr.Slider(0, 100, label="Delivery Records")  # Updated label
+            vendor_name_id = gr.Textbox(label="Vendor Name")  # Updated label to only expect name
+            delivery = gr.Slider(0, 100, label="Delivery %")
             issues = gr.Number(label="Issue Count", minimum=0)
             nps = gr.Slider(0, 100, label="NPS")
 
@@ -169,27 +180,20 @@ with gr.Blocks(title="Vendor Performance App") as demo:
             )
 
         with gr.Tab("Upload CSV"):
-            gr.Markdown("Upload a CSV with columns: `vendor_id`, `vendor_name_id` (vendor name, e.g., 'Shiva'), `delivery_records` (0–100), `issue_counts` (number), `nps_values` (0–100).")  # Updated description
-            file_upload = gr.File(label="Upload CSV", file_types=[".csv"])
+            file_upload = gr.File(label="Upload CSV (vendor_name_id should contain vendor names)", file_types=[".csv"])
             file_status = gr.Textbox(label="Upload Status")
             file_result = gr.Dataframe(headers=[
                 "Vendor ID", "Vendor Name", "Score", "Timeliness",
                 "Issues", "Feedback", "Date", "Rationale"
             ])
-            download_button = gr.File(label="Download Processed Results as CSV", visible=False)
-
-            def update_download_button(status, result_df, csv_path):
-                if result_df is not None and csv_path:
-                    return gr.update(visible=True, value=csv_path)
-                return gr.update(visible=False)
 
-            file_upload.upload(
+            file_upload.change(
                 fn=process_uploaded_file,
                 inputs=file_upload,
-                outputs=[file_status, file_result, download_button],
-                _js="() => {setTimeout(() => document.querySelector('input[type=\"file\"]').value = '', 0)}"
+                outputs=[file_status, file_result]
             )
 
-# Launch the Gradio app
 if __name__ == "__main__":
-    demo.launch()
+    import uvicorn
+    demo.launch(server_name="0.0.0.0", server_port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=8000)