Update app.py

app.py

@@ -1,30 +1,63 @@
 import gradio as gr
+import logging
 import os
-from dotenv import load_dotenv
+import numpy as np
 from simple_salesforce import Salesforce
+from dotenv import load_dotenv
 
-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
-
-# Load environment variables
-load_dotenv()
+# Load environment variables from .env file
+load_dotenv()  # Load the .env file
 
-# Salesforce credentials
+# Get the Salesforce credentials from environment variables
 sf_username = os.getenv("SF_USERNAME")
 sf_password = os.getenv("SF_PASSWORD")
 sf_security_token = os.getenv("SF_SECURITY_TOKEN")
-sf_domain = os.getenv("SF_DOMAIN", "login")  # default to production
+sf_instance_url = os.getenv("SF_INSTANCE_URL")
+
+# Check if the environment variables are correctly set
+if not sf_username or not sf_password or not sf_security_token or not sf_instance_url:
+    logger.error("❌ Salesforce credentials are missing from environment variables!")
+    raise ValueError("Salesforce credentials are not properly set.")
+
+# Salesforce connection
+try:
+    sf = Salesforce(
+        username=sf_username,
+        password=sf_password,
+        security_token=sf_security_token,
+        instance_url=sf_instance_url
+    )
+    logger.info("✅ Connected to Salesforce")
+except Exception as e:
+    logger.error(f"❌ Salesforce connection failed: {str(e)}")
+    raise
+
+# AI functions (These should be implemented in your project)
+def get_lead_score(stage, emails, meetings, close_gap, amount):
+    # Example logic for lead score calculation
+    return 0.8 * amount + 0.1 * emails + 0.1 * meetings
+
+def calculate_score(lead_score, emails, meetings, close_gap, amount):
+    # Example AI score calculation
+    return lead_score * 0.5 + 0.3 * emails + 0.2 * meetings
+
+def calculate_confidence(ai_score):
+    # Example confidence calculation
+    return ai_score * 100
+
+def calculate_risk(ai_score, confidence, emails, meetings):
+    # Example risk calculation
+    return "Low" if confidence > 75 else "High"
 
-# Connect to Salesforce
-sf = Salesforce(
-    username=sf_username,
-    password=sf_password,
-    security_token=sf_security_token,
-    domain=sf_domain
-)
+def generate_recommendation(stage, emails, meetings, risk):
+    # Example recommendation generation
+    return "Proceed with caution" if risk == "High" else "Proceed"
 
+def explain_score(lead_score, ai_score, confidence, risk, stage, close_gap, emails, meetings):
+    # Example explanation generation
+    return f"Lead score based on {emails} emails and {meetings} meetings is {lead_score}. AI score is {ai_score}, confidence is {confidence}%."
+
+# --- Push to Salesforce ---
 def push_to_salesforce(data: dict) -> str:
     try:
         response = sf.qualification_engine__c.create({
@@ -46,6 +79,7 @@ def push_to_salesforce(data: dict) -> str:
     except Exception as e:
         return f"❌ Salesforce Error: {str(e)}"
 
+# --- Run Engine (Calculate and Push to Salesforce) ---
 def run_engine(amount, stage, industry, emails, meetings, close_gap, feedback=""):
     try:
         lead_score = get_lead_score(stage, emails, meetings, close_gap, amount)
@@ -55,13 +89,6 @@ def run_engine(amount, stage, industry, emails, meetings, close_gap, feedback=""
         recommendation = generate_recommendation(stage, emails, meetings, risk)
         explanation = explain_score(lead_score, ai_score, confidence, risk, stage, close_gap, emails, meetings)
 
-        log_submission({
-            "amount": amount, "stage": stage, "industry": industry,
-            "emails": emails, "meetings": meetings, "gap": close_gap,
-            "lead_score": lead_score, "score": ai_score, "confidence": confidence,
-            "risk": risk, "feedback": feedback
-        })
-
         sf_status = push_to_salesforce({
             "amount": amount, "stage": stage, "industry": industry,
             "emails": emails, "meetings": meetings, "gap": close_gap,
@@ -75,14 +102,11 @@ 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.")
-
+    
     with gr.Tab("📥 Input"):
         with gr.Row():
             amount = gr.Number(label="💰 Deal Amount (USD)", value=50000)
@@ -98,7 +122,6 @@ with gr.Blocks(title="AI Deal Qualification Engine") as app:
             close_gap = gr.Number(label="📆 Days Until Close", value=14)
 
         feedback = gr.Textbox(label="💬 Optional: Rep Feedback", placeholder="Add any qualitative insights...")
-
         submit = gr.Button("🚀 Run AI Scoring")
 
     with gr.Tab("📈 Results"):
@@ -119,4 +142,4 @@ 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=share_app)
+app.launch(share=True)