Upload app.py

app.py

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+import streamlit as st
+import pickle
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+from sentence_transformers import models
+import numpy as np
+
+res = pd.read_csv('qa2.csv')
+
+# Load pre-computed embeddings
+with open("embeddings_words.pkl", "rb") as f:
+    embedded_texts = pickle.load(f)
+
+# Define model
+model_name = 'kornwtp/simcse-model-phayathaibert'
+word_embedding_model = models.Transformer(model_name)
+pooling_model = models.Pooling(word_embedding_model.get_word_embedding_dimension(), pooling_mode='cls')  # Use CLS token for representation
+model = SentenceTransformer(modules=[word_embedding_model, pooling_model])
+
+# Streamlit UI setup with custom CSS for styling
+st.title("Thai Chat Bot", anchor="top")
+st.markdown("""
+    <style>
+        .css-1kyxreq { display: none; }  # Hide the Streamlit default hamburger menu
+        .stApp { background-color: #F4F8FC; }
+        .stChatMessage-User { background-color: #4CAF50; color: white; padding: 15px; border-radius: 12px; margin-bottom: 10px; }
+        .stChatMessage-Assistant { background-color: #2196F3; color: white; padding: 15px; border-radius: 12px; margin-bottom: 10px; }
+        .stButton { background-color: #4CAF50; color: white; padding: 12px 25px; font-size: 18px; border-radius: 12px; }
+        .stTextInput { border-radius: 12px; padding: 10px; font-size: 16px; }
+        .stTextInput input { background-color: #f7f7f7; border: none; color: #333; }
+        .stMarkdown { font-size: 18px; font-family: 'Arial', sans-serif; line-height: 1.5; }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Initialize session state for messages
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display existing chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Display a greeting message
+with st.chat_message("ai"):
+    st.write("สวัสดี! 😊")
+
+# Get user input
+if prompt := st.chat_input("พิมพ์ข้อความที่นี่ ..."):
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Show a loading spinner while processing
+    with st.spinner("กำลังค้นหาคำตอบ..."):
+        # Encode the user's prompt and calculate similarities
+        b = model.encode([prompt], normalize_embeddings=True)
+        inner_products = np.inner(b, embedded_texts)  # Calculate inner products
+        
+        # Get the index of the highest value
+        top_index = np.argmax(inner_products)
+        inner_products = inner_products.flatten()
+        similarity_percent = str(round(inner_products[top_index],2))
+        answer = f"{similarity_percent}% : {res['A'][top_index]}"
+
+        with st.chat_message("assistant"):
+            st.write(answer)
+
+        # Save the assistant's answer in session state
+        st.session_state.messages.append({"role": "assistant", "content": answer})
+
+    st.success("คำตอบเสร็จสิ้นแล้ว! 😊", icon="✅")