app.py

import gradio as gr
import time
from datetime import datetime
import pandas as pd
from sentence_transformers import SentenceTransformer
from qdrant_client import QdrantClient
from qdrant_client.models import Filter, FieldCondition, MatchValue
import os
from rapidfuzz import process, fuzz
from pythainlp.tokenize import word_tokenize
from pyairtable import Table
from pyairtable import Api
import pickle
import re
import unicodedata

# Setup Qdrant Client
qdrant_client = QdrantClient(
    url=os.environ.get("Qdrant_url"),
    api_key=os.environ.get("Qdrant_api"),
)

# Airtable Config
AIRTABLE_API_KEY = os.environ.get("airtable_api")
BASE_ID = os.environ.get("airtable_baseid")
TABLE_NAME = "Feedback_search"
api = Api(AIRTABLE_API_KEY)
table = api.table(BASE_ID, TABLE_NAME)

# Load model
model = SentenceTransformer('intfloat/multilingual-e5-small')
collection_name = "product_E5"

# Load whitelist
with open("keyword_whitelist.pkl", "rb") as f:
    keyword_whitelist = pickle.load(f)

# Utils
def normalize(text: str) -> str:
    text = unicodedata.normalize("NFC", text)
    return text.replace("เแ", "แ").replace("เเ", "แ").strip().lower()

def smart_tokenize(text: str) -> list:
    tokens = word_tokenize(text.strip(), engine="newmm")
    return tokens if tokens and len("".join(tokens)) >= len(text.strip()) * 0.5 else [text.strip()]

def correct_query_merge_phrases(query: str, whitelist, threshold=80, max_ngram=3):
    query_norm = normalize(query)
    tokens = smart_tokenize(query_norm)
    corrected = []
    i = 0
    while i < len(tokens):
        matched = False
        for n in range(min(max_ngram, len(tokens) - i), 0, -1):
            phrase = "".join(tokens[i:i+n])
            match, score, _ = process.extractOne(phrase, whitelist, scorer=fuzz.token_sort_ratio)
            if score >= threshold:
                corrected.append(match)
                i += n
                matched = True
                break
        if not matched:
            corrected.append(tokens[i])
            i += 1
    return "".join([word for word in corrected if len(word) > 1 or word in whitelist])

# Global state
latest_query_result = {"query": "", "result": "", "raw_query": "", "time": ""}

# Main Search
def search_product(query):
    start_time = time.time()
    latest_query_result["raw_query"] = query
    corrected_query = correct_query_merge_phrases(query, keyword_whitelist)
    query_embed = model.encode("query: " + corrected_query)

    try:
        result = qdrant_client.query_points(
            collection_name=collection_name,
            query=query_embed.tolist(),
            with_payload=True,
            query_filter=Filter(must=[FieldCondition(key="type", match=MatchValue(value="product"))]),
            limit=50
        ).points
    except Exception as e:
        return f"<p>❌ Qdrant error: {str(e)}</p>"

    elapsed = time.time() - start_time
    html_output = f"<p>⏱ <strong>{elapsed:.2f} วินาที</strong></p>"
    if corrected_query != query:
        html_output += f"<p>🔧 แก้คำค้นจาก: <code>{query}</code> → <code>{corrected_query}</code></p>"

    html_output += '<div style="display: grid; grid-template-columns: repeat(auto-fill, minmax(220px, 1fr)); gap: 20px;">'

    result_summary, found = "", False
    for res in result:
        if res.score > 0.8:
            found = True
            name = res.payload.get("name", "ไม่ทราบชื่อสินค้า")
            score = f"{res.score:.4f}"
            img_url = res.payload.get("imageUrl", "")
            price = res.payload.get("price", "ไม่ระบุ")
            brand = res.payload.get("brand", "")

            html_output += f"""
            <div style="border: 1px solid #ddd; border-radius: 8px; padding: 10px; text-align: center; box-shadow: 1px 1px 5px rgba(0,0,0,0.1); background: #fff;">
                <img src="{img_url}" style="width: 100%; max-height: 150px; object-fit: contain; border-radius: 4px;">
                <div style="margin-top: 10px;">
                    <div style="font-weight: bold; font-size: 14px;">{name}</div>
                    <div style="color: gray; font-size: 12px;">{brand}</div>
                    <div style="color: green; margin: 4px 0;">฿{price}</div>
                    <div style="font-size: 12px; color: #555;">score: {score}</div>
                </div>
            </div>
            """
            result_summary += f"{name} (score: {score}) | "

    html_output += "</div>"

    if not found:
        html_output += '<div style="text-align: center; font-size: 18px; color: #a00; padding: 30px;">❌ ไม่พบสินค้าที่เกี่ยวข้องกับคำค้นนี้</div>'
        return html_output

    latest_query_result.update({
        "query": corrected_query,
        "result": result_summary.strip(),
        "time": elapsed,
    })

    return html_output

# Feedback logging
def log_feedback(feedback):
    try:
        now = datetime.now().strftime("%Y-%m-%d")
        table.create({
            "model": "E5 (intfloat/multilingual-e5-small)",
            "timestamp": now,
            "raw_query": latest_query_result["raw_query"],
            "query": latest_query_result["query"],
            "result": latest_query_result["result"],
            "time(second)": latest_query_result["time"],
            "feedback": feedback
        })
        return "✅ Feedback saved to Airtable!"
    except Exception as e:
        return f"❌ Failed to save feedback: {str(e)}"

# Gradio UI
with gr.Blocks() as demo:
    gr.Markdown("## 🔎 Product Semantic Search (Vector Search + Qdrant)")

    query_input = gr.Textbox(label="พิมพ์คำค้นหา")
    result_output = gr.HTML(label="📋 ผลลัพธ์")

    with gr.Row():
        match_btn = gr.Button("✅ ตรง")
        not_match_btn = gr.Button("❌ ไม่ตรง")

    feedback_status = gr.Textbox(label="📬 สถานะ Feedback")

    query_input.submit(search_product, inputs=[query_input], outputs=result_output)
    match_btn.click(lambda: log_feedback("match"), outputs=feedback_status)
    not_match_btn.click(lambda: log_feedback("not_match"), outputs=feedback_status)

# Run
demo.launch(share=True)