app.py

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

# Load env variables
load_dotenv()

# Salesforce credentials
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

# 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)
        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)

        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,
            "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)}"

# 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)
            stage = gr.Dropdown(
                ["Prospecting", "Proposal/Price Quote", "Negotiation", "Closed Won", "Closed Lost"],
                label="📊 Stage"
            )
            industry = gr.Textbox(label="🏭 Industry", value="Software")

        with gr.Row():
            emails = gr.Number(label="✉️ Emails (Last 7 Days)", value=3)
            meetings = gr.Number(label="📅 Meetings (Last 30 Days)", value=2)
            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"):
        with gr.Accordion("AI Scoring Output", open=True):
            lead_score_out = gr.Number(label="🔢 Lead Score", interactive=False)
            ai_score_out = gr.Number(label="🌟 AI Score (0–100)", interactive=False)
            confidence_out = gr.Number(label="📐 Confidence", interactive=False)

            risk_out = gr.Textbox(label="⚠️ Risk Level", lines=1, interactive=False)
            reco_out = gr.Textbox(label="💡 AI Recommendation", lines=2, interactive=False)
            explain_out = gr.Textbox(label="🧠 Explanation", lines=5, interactive=False)

        status = gr.Markdown("")

    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=share_app)