app.py

import gradio as gr
import joblib
import numpy as np
import json
import math
import re
from fpdf import FPDF
import tempfile
from deep_translator import GoogleTranslator
from xgboost import Booster

# Load models
xgb = Booster()
xgb.load_model("xgb_model.json")
rf = joblib.load("rf_model.pkl")

# Load tile catalog and size mappings
with open("tile_catalog.json", "r", encoding="utf-8") as f:
    tile_catalog = json.load(f)

with open("tile_sizes.json", "r", encoding="utf-8") as f:
    tile_sizes = json.load(f)

tile_size_map = {s["label"].lower().replace(" ", ""): s["area_sqft"] for s in tile_sizes}

def translate(text, lang="en"):
    try:
        return GoogleTranslator(source="auto", target=lang).translate(text)
    except:
        return text

def remove_emojis(text):
    return re.sub(r'[^\x00-\x7F]+', '', text)

def create_pdf(text):
    pdf = FPDF()
    pdf.add_page()
    pdf.add_font("FreeSans", "", "FreeSans.ttf", uni=True)
    pdf.set_font("FreeSans", size=12)
    pdf.cell(0, 10, "Tile Estimate Report", ln=True, align="C")
    pdf.ln(5)
    for line in text.strip().split("\n"):
        pdf.multi_cell(0, 10, remove_emojis(line))
    tmp = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf")
    pdf.output(tmp.name)
    return tmp.name

def extract_tile_area(msg):
    msg = msg.lower().replace("×", "x").replace("into", "x").replace("*", "x")
    msg = msg.replace("ft", "").replace("feet", "").replace("mm", "").strip()
    
    if "x" in msg:
        parts = msg.split("x")
        if len(parts) == 2:
            try:
                val1 = float(re.sub(r"[^\d.]", "", parts[0]))
                val2 = float(re.sub(r"[^\d.]", "", parts[1]))
                if val1 > 20:  # treat as mm
                    sqft = (val1 * val2) / 92903.04  # mm² to ft²
                else:  # treat as feet
                    sqft = val1 * val2
                return round(sqft, 2)
            except:
                return None
    return None

def chat_fn(message, history, user_state={}):
    if "lang" not in user_state:
        try:
            user_state["lang"] = GoogleTranslator(source="auto", target="en").detect(message)
        except:
            user_state["lang"] = "en"
    lang = user_state["lang"]
    def reply(text): return translate(text, lang)

    if "step" not in user_state:
        if message.lower() in ["floor", "wall"]:
            user_state["tile_type"] = message.capitalize()
            user_state["step"] = "get_area"
            return reply(f"✅ Great! You selected {user_state['tile_type']} tiles.\nPlease enter the total area in sq.ft:"), None, user_state
        return reply("👋 Hello! Are you looking for Floor or Wall tiles?"), None, user_state

    if user_state["step"] == "get_area":
        try:
            user_state["area"] = float(message)
            user_state["step"] = "get_tile_size"
            return reply("📏 Now enter the tile size like 2 x 2 ft or 600x600 MM or 200 * 200:"), None, user_state
        except:
            return reply("❗ Please enter a number for area (e.g. 120)."), None, user_state

    if user_state["step"] == "get_tile_size":
        area = extract_tile_area(message)
        if area is None:
            examples = "2 x 2 ft, 600x600 MM, 200*200, 1.5x1.5"
            return reply(f"❗ Oops, I didn't catch that size. Try formats like: {examples}"), None, user_state

        user_state["tile_area"] = area
        user_state["step"] = "done"

        area_needed = user_state["area"]
        tile_type = user_state["tile_type"]
        tile_needed = math.ceil((area_needed / area) * 1.1)

        best = []
        for tile in tile_catalog:
            if tile["type"].lower() == tile_type.lower():
                per_box = tile["coverage"] / area
                if per_box > 0:
                    boxes = math.ceil(tile_needed / per_box)
                    total = boxes * tile["price"]
                    best.append({
                        "name": tile["name"],
                        "size": tile["size"],
                        "price": tile["price"],
                        "boxes": boxes,
                        "total": total,
                        "url": tile["url"]
                    })

        best.sort(key=lambda x: x["total"])
        top3 = best[:3]

        summary = f"""
🧱 Tile Type: {tile_type}
📐 Area: {area_needed} sq.ft
🧮 Tile Size: {round(area, 2)} sq.ft
🔢 Total Tiles Needed (10% buffer): {tile_needed}

🎯 Top Recommendations:
"""
        for i, t in enumerate(top3, 1):
            summary += f"\n{i}. {t['name']} ({t['size']})\n   ₹{t['price']}/box → ~{t['boxes']} boxes\n   {t['url']}\n"

        summary += "\nSay 'Floor' or 'Wall' to start a new estimate."

        pdf_path = create_pdf(summary)
        return reply(summary), [pdf_path], {}

    return reply("Say 'Floor' or 'Wall' to begin again."), None, {}

# Launch the bot
with gr.Blocks() as demo:
    chatbot = gr.ChatInterface(
        fn=chat_fn,
        title="🧱 TileBot – ChatGPT-Style Tile Estimator",
        description="Chat like a human. Enter sizes naturally like 200x200, 2 x 2 ft, or 600x600 MM.\nSay 'Floor' or 'Wall' to begin.",
        type="messages",
        additional_outputs=[gr.File(label="📄 Download Report")]
    )

demo.launch()