set up project

app.py

@@ -0,0 +1,88 @@
+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
+
+# Load model
+model = SentenceTransformer('intfloat/multilingual-e5-small')
+
+qdrant_client = QdrantClient(
+    url=url,
+    api_key=api_key,
+)
+
+# Global cache to hold current query/result
+latest_query_result = {"query": "", "result": ""}
+
+# Feedback logger
+def log_feedback(feedback):
+    now = datetime.now().isoformat()
+    log_entry = {
+        "timestamp": now,
+        "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}"
+
+# Main search function
+def search_product(query):
+    if (query == ""):
+      return
+    start_search = time.time()
+    start_embed = time.time()
+    query_embed = model.encode("query: " + query)
+    embed_time = time.time() - start_embed
+
+    start_query = time.time()
+    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
+    query_time = time.time() - start_query
+    search_time = time.time() - start_search
+
+    output = f"🕐 Embedding Time: {embed_time:.4f} sec\n"
+    output += f"🔍 Query Time: {query_time:.4f} sec\n"
+    output += f"⏱ Total Search Time: {search_time:.4f} sec\n\n"
+    output += "📦 ผลลัพธ์:\n"
+
+    result_summary = ""
+    for res in result:
+        line = f"- {res.payload.get('name', '')} (score: {res.score:.4f})"
+        output += line + "\n"
+        result_summary += line + " | "
+
+    latest_query_result["query"] = query
+    latest_query_result["result"] = result_summary.strip()
+
+    return output
+
+# Gradio UI
+with gr.Blocks() as demo:
+    gr.Markdown("## 🔎 Product Semantic Search (E5 + Qdrant)")
+
+    query_input = gr.Textbox(label="ใส่คำค้นหาสินค้า")
+    result_output = gr.Textbox(label="📋 ผลลัพธ์")
+
+    with gr.Row():
+        match_btn = gr.Button("✅ ตรงกับที่ต้องการ")
+        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)
+
+# Launch
+demo.launch(share=True)

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+sentence-transformers
+qdrant-client