Update streamlit_app.py

streamlit_app.py

@@ -1,12 +1,48 @@
-import sys
-from pathlib import Path
-
-ROOT_DIR = Path(__file__).resolve().parent
-sys.path.append(str(ROOT_DIR))
-
 import streamlit as st
 import pandas as pd
 import json
 
 from utils.preprocess import preprocess_text
 from utils.predict import load_model, predict_label
+
+st.set_page_config(
+    page_title="BRD Clarity Detection",
+    layout="wide"
+)
+
+st.title("📄 BRD Clarity Detection Tool")
+
+@st.cache_resource
+def init_model():
+    clf, s2v = load_model()
+    return clf, s2v
+
+clf, s2v_model = init_model()
+
+uploaded_file = st.file_uploader(
+    "Upload requirement file (JSON or TXT)",
+    type=["json", "txt"]
+)
+
+text_input = st.text_area("Or paste requirement text here", height=200)
+
+if st.button("Analyze"):
+    if uploaded_file:
+        if uploaded_file.name.endswith(".json"):
+            data = json.load(uploaded_file)
+            texts = [d["text"] for d in data]
+        else:
+            texts = uploaded_file.read().decode("utf-8").splitlines()
+    elif text_input.strip():
+        texts = text_input.splitlines()
+    else:
+        st.warning("Please upload a file or paste text")
+        st.stop()
+
+    df = pd.DataFrame({"text": texts})
+    df["clean_text"] = df["text"].apply(preprocess_text)
+
+    preds = predict_label(df["clean_text"], clf, s2v_model)
+    df["Prediction"] = preds
+
+    st.dataframe(df, use_container_width=True)