# 1. INSTALL NECESSARY LIBRARIES
#!pip install -q transformers torch gradio pandas

import gradio as gr
import pandas as pd
from transformers import pipeline
import datetime

# 2. LOAD THE INTELLIGENCE ENGINE (Hugging Face)
# We use a Zero-Shot Classifier: It categorizes text WITHOUT needing prior training data.
print("Loading AI Model... Please wait.")
classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

# 3. DEFINE THE DATA ENGINEERING LOGIC
def process_sales_lead(customer_name, company, interaction_text):
    # Define our business categories (Targeting the Lead Scoring & Automation)
    labels = ["Sales Opportunity", "Technical Support", "General Inquiry", "Urgent RFQ"]

    # Run the AI Inference
    result = classifier(interaction_text, labels)
    top_intent = result['labels'][0]
    confidence_score = round(result['scores'][0] * 100, 2)

    # Logic for Lead Prioritization (The "Project Engineer" value-add)
    priority = "LOW"
    if top_intent in ["Sales Opportunity", "Urgent RFQ"] and confidence_score > 70:
        priority = "HIGH"
    elif confidence_score > 50:
        priority = "MEDIUM"

    # Create a fabricated record for the "Dashboard"
    output_data = {
        "Timestamp": datetime.datetime.now().strftime("%Y-%m-%d %H:%M"),
        "Customer": customer_name,
        "Company": company,
        "Detected Intent": top_intent,
        "AI Confidence (%)": confidence_score,
        "Priority Level": priority
    }

    # Return formatted results for the UI
    return f"Intent: {top_intent} ({confidence_score}%)", f"Priority: {priority}", output_data

# 4. BUILD THE GRADIO INTERFACE (The Proof of Concept)
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 🚀 Sales Ops: AI Lead Scoring Hub")
    gr.Markdown("### Proof of Concept: Automating Repetitive Manual Lead Triaging")

    with gr.Row():
        with gr.Column():
            name = gr.Textbox(label="Customer Name", placeholder="e.g., John Doe")
            company = gr.Textbox(label="Company", placeholder="e.g., TechCorp")
            text = gr.TextArea(label="Interaction Text (Email/Log)",
                               placeholder="e.g., I need a quote for 500 units of the 5G connector...")
            submit_btn = gr.Button("Analyze Lead", variant="primary")

        with gr.Column():
            intent_out = gr.Label(label="AI Analysis")
            priority_out = gr.Textbox(label="System Priority Action")
            json_out = gr.JSON(label="Cleaned Data for SAP/Salesforce Ingestion")

    submit_btn.click(
        fn=process_sales_lead,
        inputs=[name, company, text],
        outputs=[intent_out, priority_out, json_out]
    )

# 5. LAUNCH THE POC
demo.launch(debug=True)