Hugging Face's logo

Spaces:
sibthinon
/
environment
Running

App Files Community
environment
File size: 4,146 Bytes 
28b8e02
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
 
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 qdrant_client import QdrantClient

qdrant_client = QdrantClient(
    url=os.environ.get("Qdrant_url"),
    api_key=os.environ.get("Qdrant_api")
)

# โมเดลที่โหลดล่วงหน้า
models = {
    "E5 (intfloat/multilingual-e5-small)": SentenceTransformer('intfloat/multilingual-e5-small'),
    "MiniLM (paraphrase-multilingual-MiniLM-L12-v2)": SentenceTransformer('paraphrase-multilingual-MiniLM-L12-v2'),
    "DistilUSE (distiluse-base-multilingual-cased-v1)": SentenceTransformer('sentence-transformers/distiluse-base-multilingual-cased-v1')
}

# Global memory to hold feedback state
latest_query_result = {"query": "", "result": "", "model": ""}


# 🔍 Search Functions
def search_with_e5(query):
    embed = models["E5 (intfloat/multilingual-e5-small)"].encode("query: " + query)
    return embed

def search_with_minilm(query):
    embed = models["MiniLM (paraphrase-multilingual-MiniLM-L12-v2)"].encode(query)
    return embed

def search_with_distiluse(query):
    embed = models["DistilUSE (distiluse-base-multilingual-cased-v1)"].encode(query)
    return embed


# 🌟 Main search function
def search_product(query, model_name):
    start_time = time.time()

    # Choose encoder function
    if "E5" in model_name:
        query_embed = search_with_e5(query)
    elif "MiniLM" in model_name:
        query_embed = search_with_minilm(query)
    elif "DistilUSE" in model_name:
        query_embed = search_with_distiluse(query)
    else:
        return "❌ ไม่พบโมเดล"

    # Query Qdrant
    result = qdrant_client.query_points(
        collection_name="product_E5",
        query=query_embed.tolist(),
        with_payload=True,
        query_filter=Filter(
            must=[FieldCondition(key="type", match=MatchValue(value="product"))]
        )
    ).points

    elapsed = time.time() - start_time

    # Format result
    output = f"⏱ Time: {elapsed:.2f}s\n\n📦 ผลลัพธ์:\n"
    result_summary = ""
    for res in result:
        line = f"- {res.payload.get('name', '')} (score: {res.score:.4f})"
        output += line + "\n"
        result_summary += line + " | "

    # Save latest query
    latest_query_result["query"] = query
    latest_query_result["result"] = result_summary.strip()
    latest_query_result["model"] = model_name

    return output


# 📝 Logging feedback
def log_feedback(feedback):
    now = datetime.now().isoformat()
    log_entry = {
        "timestamp": now,
        "model": latest_query_result["model"],
        "query": latest_query_result["query"],
        "result": latest_query_result["result"],
        "feedback": feedback
    }
    df = pd.DataFrame([log_entry])
    df.to_csv("feedback_log.csv", mode='a', header=not pd.io.common.file_exists("feedback_log.csv"), index=False)
    return f"✅ Feedback saved: {feedback}"


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

    with gr.Row():
        model_selector = gr.Dropdown(
            choices=list(models.keys()),
            label="เลือกโมเดล",
            value="E5 (intfloat/multilingual-e5-small)"
        )
        query_input = gr.Textbox(label="พิมพ์คำค้นหา")

    result_output = gr.Textbox(label="📋 ผลลัพธ์")

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

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

    # Events
    submit_fn = lambda q, m: search_product(q, m)
    query_input.submit(submit_fn, inputs=[query_input, model_selector], 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 app
demo.launch(share=True)