app.py

import gradio as gr
import logging
import os
import numpy as np
from simple_salesforce import Salesforce
from dotenv import load_dotenv

# Setup logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

# Load environment variables from .env file
load_dotenv()  # Load the .env file

# 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_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):
    # Ensure confidence is between 0 and 100
    confidence = ai_score * 100
    return min(max(confidence, 0), 100)  # Clamp the value between 0 and 100

def calculate_risk(ai_score, confidence, emails, meetings):
    # Make sure to map the value to the Salesforce picklist options
    if ai_score > 0.75 and confidence > 75:
        return "Low"  # Update to match Salesforce picklist value
    elif ai_score > 0.5:
        return "Medium"  # Assuming "Medium" is another valid value
    else:
        return "High"  # Update to match Salesforce picklist value

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}%. Risk: {risk}."


# --- Push to Salesforce ---
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)}"


# --- 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)
        ai_score = calculate_score(lead_score, emails, meetings, close_gap, amount)
        confidence = calculate_confidence(ai_score)
        risk = calculate_risk(ai_score, confidence, emails, meetings)
        recommendation = generate_recommendation(stage, emails, meetings, risk)
        explanation = explain_score(lead_score, ai_score, confidence, risk, stage, close_gap, emails, meetings)

        sf_status = push_to_salesforce({
            "amount": amount, "stage": stage, "industry": industry,
            "emails": emails, "meetings": meetings, "gap": close_gap,
            "feedback": feedback, "lead_score": lead_score, "score": ai_score,
            "confidence": confidence, "risk": risk,
            "recommendation": recommendation, "explanation": explanation
        })

        return lead_score, ai_score, confidence, risk, recommendation, explanation, sf_status

    except Exception as e:
        return 0, 0, 0.0, "Error", "N/A", f"Error occurred: {str(e)}", f"❌ Error: {str(e)}"


# Gradio UI
with gr.Blocks(title="AI Deal Qualification Engine") as app:
    gr.Markdown("""
    <h1 style="text-align:center;">🤖 AI-Powered Deal Qualification Engine</h1>
    <p style="text-align:center;">Intelligently qualify sales deals using engagement and pipeline signals.</p>
    """, elem_id="header")

    with gr.Tab("📥 Input", elem_id="input-tab"):
        with gr.Row():
            amount = gr.Number(label="💰 Deal Amount (USD)", value=50000, elem_id="deal-amount")
            stage = gr.Dropdown(
                ["Prospecting", "Proposal/Price Quote", "Negotiation", "Closed Won", "Closed Lost"],
                label="📊 Stage",
                elem_id="deal-stage"
            )
            industry = gr.Textbox(label="🏭 Industry", value="Software", elem_id="industry")

        with gr.Row():
            emails = gr.Number(label="✉️ Emails (Last 7 Days)", value=3, elem_id="emails")
            meetings = gr.Number(label="📅 Meetings (Last 30 Days)", value=2, elem_id="meetings")
            close_gap = gr.Number(label="📆 Days Until Close", value=14, elem_id="days-until-close")

        feedback = gr.Textbox(label="💬 Optional: Rep Feedback", placeholder="Add any qualitative insights...", elem_id="feedback")
        submit = gr.Button("🚀 Run AI Scoring", elem_id="submit-btn")

    with gr.Tab("📈 Results", elem_id="result-tab"):
        with gr.Accordion("AI Scoring Output", open=True):
            lead_score_out = gr.Number(label="🔢 Lead Score", interactive=False, elem_id="lead-score")
            ai_score_out = gr.Number(label="🌟 AI Score (0–100)", interactive=False, elem_id="ai-score")
            confidence_out = gr.Number(label="📐 Confidence", interactive=False, elem_id="confidence")

            risk_out = gr.Textbox(label="⚠️ Risk Level", lines=1, interactive=False, elem_id="risk-level")
            reco_out = gr.Textbox(label="💡 AI Recommendation", lines=2, interactive=False, elem_id="ai-recommendation")
            explain_out = gr.Textbox(label="🧠 Explanation", lines=5, interactive=False, elem_id="explanation")

        status = gr.Markdown("", elem_id="status")

    submit.click(
        fn=run_engine,
        inputs=[amount, stage, industry, emails, meetings, close_gap, feedback],
        outputs=[lead_score_out, ai_score_out, confidence_out, risk_out, reco_out, explain_out, status]
    )

app.launch(share=True)