Update app.py

app.py

@@ -1,32 +1,32 @@
-import streamlit as st
-from model import load_model, generate_answer,find_context
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-from dataset import dataset_a
-from sentence_transformers import SentenceTransformer
-
-# Load model automatically
-MODEL_PATH = "D:/Pycharm/Project/Project2/Model/vit5/vit5_base.zip/checkpoint-900"
-model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
-tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
-pos_sentences = dataset_a["context"]
-model2=SentenceTransformer('D:/Pycharm/Project/Project2/Model/model_retrival/tmp/checkpoint-586')
-
-# Streamlit UI customization
-st.set_page_config(page_title="Chat Bot Công dân số", page_icon="🤖", layout="wide")
-st.title("🤖 Chat Bot Công dân số")
-st.markdown("---")
-st.success("✅ Model Loaded Successfully")
-
-st.sidebar.header("⚙️ Settings")
-max_length = st.sidebar.slider("Max Answer Length", min_value=50, max_value=500, value=256, step=10)
-
-st.subheader("📌 Ask a Question")
-# context = st.text_area("📝 Context:", height=150)
-question = st.text_input("❓ Question:")
-context=find_context(pos_sentences,question,model2)
-
-
-if st.button("🚀 Generate Answer"):
-    answer = generate_answer(model, tokenizer, context, question, max_length=max_length)
-    st.markdown("### 💡 Answer:")
-    st.info(answer)
+import streamlit as st
+from model import load_model, generate_answer,find_context
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+from dataset import dataset_a
+from sentence_transformers import SentenceTransformer
+
+# Load model automatically
+MODEL_PATH = "namngo/CDS_vit5"
+model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_PATH)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)
+pos_sentences = dataset_a["context"]
+model2=SentenceTransformer('namngo/CDS_retrival')
+
+# Streamlit UI customization
+st.set_page_config(page_title="Chat Bot Công dân số", page_icon="🤖", layout="wide")
+st.title("🤖 Chat Bot Công dân số")
+st.markdown("---")
+st.success("✅ Model Loaded Successfully")
+
+st.sidebar.header("⚙️ Settings")
+max_length = st.sidebar.slider("Max Answer Length", min_value=50, max_value=500, value=256, step=10)
+
+st.subheader("📌 Ask a Question")
+# context = st.text_area("📝 Context:", height=150)
+question = st.text_input("❓ Question:")
+context=find_context(pos_sentences,question,model2)
+
+
+if st.button("🚀 Generate Answer"):
+    answer = generate_answer(model, tokenizer, context, question, max_length=max_length)
+    st.markdown("### 💡 Answer:")
+    st.info(answer)