Initial

app.py

@@ -0,0 +1,26 @@
+import streamlit as st
+import pickle
+from sentence_transformers import SentenceTransformer
+import pandas as pd
+import numpy as np
+
+
+lr_model_loaded = pickle.load(open('./models/lr_model.pkl', 'rb'))
+scaler_loaded = pickle.load(open('./models/scaler.pkl', 'rb'))
+stmodel = SentenceTransformer("all-MiniLM-L6-v2")
+
+
+def predict(context):
+    r = stmodel.encode(context)
+    y_pred = scaler_loaded.transform([r])
+    #print(y_pred)
+    result = lr_model_loaded.predict(y_pred)
+    return 'บทวิเคราะห์' if result[0] == 1 else 'ไม่ใช่บทวิเคราะห์'
+
+context = st.text_area('ใส่ข่าววิเคราะห์ที่นี่',height=300)
+if context:
+  r = predict(context)
+  if r == "บทวิเคราะห์":
+    st.write("เนื้อหานี้เป็นบทวิเคราะห์")
+  else:
+    st.write("เนื้อหานี้ไม่ใช่บทวิเคราะห์")

models/lr_model.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:259eb170b49cb0f00c0b503d0a74f557b845897426eb16c42a6f8ab1bcc99722
+size 3791

models/scaler.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ecff43a03ad726b78582c965237a2bb8d9953565cb7169dc73646c28f97ea07
+size 9684