Update app.py

app.py

@@ -1,39 +1,51 @@
 import gradio as gr
+import os
+from dotenv import load_dotenv
+from simple_salesforce import Salesforce
+
 from scorer import get_lead_score, calculate_score, calculate_confidence, calculate_risk
 from recommender import generate_recommendation
 from insights import explain_score
 from logger import log_submission
 
-from simple_salesforce import Salesforce
-from dotenv import load_dotenv
-import os
+# Load env variables
+load_dotenv()
 
-# Get the Salesforce credentials from environment variables
+# Salesforce credentials
 sf_username = os.getenv("SF_USERNAME")
 sf_password = os.getenv("SF_PASSWORD")
 sf_security_token = os.getenv("SF_SECURITY_TOKEN")
-sf_instance_url = os.getenv("SF_INSTANCE_URL")
-
-# Data mapped to your Qualification_Engine__c fields
-data = {
-    "Deal_Amount__c": amount,  # 💰 Deal Amount
-    "Stage__c": stage,  # 📊 Deal Stage
-    "Industry__c": industry,  # 🏭 Industry
-    "Emails_7_Days__c": emails,  # ✉️ Emails in last 7 days
-    "Meetings_30_Days__c": meetings,  # 📅 Meetings in last 30 days
-    "Days_Until_Close__c": close_gap,  # 📆 Days Until Close
-    "Rep_Feedback__c": feedback,  # 💬 Rep Feedback
-    "Lead_Score__c": lead_score,  # 🔢 Lead Score
-    "AI_Score__c": ai_score,  # 🌟 AI Score
-    "Confidence__c": confidence,  # 📐 Confidence %
-    "Risk_Level__c": risk,  # ⚠️ Risk Level
-    "AI_Recommendation__c": recommendation,  # 💡 AI Recommendation
-    "Explanation__c": explanation  # 🧠 Explanation
-}
-
- # Creating the record in Salesforce
-        response = sf.qualification_engine__c.create(data)
-        
+sf_domain = os.getenv("SF_DOMAIN", "login")  # default to production
+
+# Connect to Salesforce
+sf = Salesforce(
+    username=sf_username,
+    password=sf_password,
+    security_token=sf_security_token,
+    domain=sf_domain
+)
+
+def push_to_salesforce(data: dict) -> str:
+    try:
+        response = sf.Qualification_Engine__c.create({
+            "Deal_Amount__c": data.get("amount"),
+            "Stage__c": data.get("stage"),
+            "Industry__c": data.get("industry"),
+            "Emails_7_Days__c": data.get("emails"),
+            "Meetings_30_Days__c": data.get("meetings"),
+            "Days_Until_Close__c": data.get("gap"),
+            "Rep_Feedback__c": data.get("feedback"),
+            "Lead_Score__c": data.get("lead_score"),
+            "AI_Score__c": data.get("score"),
+            "Confidence__c": data.get("confidence"),
+            "Risk_Level__c": data.get("risk"),
+            "AI_Recommendation__c": data.get("recommendation"),
+            "Explanation__c": data.get("explanation")
+        })
+        return f"✅ Pushed to Salesforce with ID: {response['id']}"
+    except Exception as e:
+        return f"❌ Salesforce Error: {str(e)}"
+
 def run_engine(amount, stage, industry, emails, meetings, close_gap, feedback=""):
     try:
         lead_score = get_lead_score(stage, emails, meetings, close_gap, amount)
@@ -63,6 +75,10 @@ def run_engine(amount, stage, industry, emails, meetings, close_gap, feedback=""
     except Exception as e:
         return 0, 0, 0.0, "Error", "N/A", f"Error occurred: {str(e)}", f"❌ Error: {str(e)}"
 
+# Read share flag from env
+share_app = os.getenv("GRADIO_SHARE", "false").lower() == "true"
+
+# Gradio UI
 with gr.Blocks(title="AI Deal Qualification Engine") as app:
     gr.Markdown("## 🤖 AI-Powered Deal Qualification Engine")
     gr.Markdown("Intelligently qualify sales deals using engagement and pipeline signals.")
@@ -103,4 +119,4 @@ with gr.Blocks(title="AI Deal Qualification Engine") as app:
         outputs=[lead_score_out, ai_score_out, confidence_out, risk_out, reco_out, explain_out, status]
     )
 
-app.launch(share=False)
+app.launch(share=share_app)