Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# Load dataset
+@st.cache_data
+def load_data():
+    df = pd.read_csv("finance_data.csv")
+    return df
+
+# Embed questions
+@st.cache_resource
+def load_embeddings(df):
+    model = SentenceTransformer("all-MiniLM-L6-v2")
+    embeddings = model.encode(df["question"].tolist(), show_progress_bar=True)
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(np.array(embeddings))
+    return model, index, embeddings
+
+# Get answer
+def get_answer(query, model, index, df, embeddings, k=1):
+    query_emb = model.encode([query])
+    D, I = index.search(query_emb, k)
+    return df["answer"].iloc[I[0][0]]
+
+# Streamlit UI
+st.title("💸 Financial Q&A Chatbot")
+st.markdown("Ask me anything about finance!")
+
+df = load_data()
+model, index, embeddings = load_embeddings(df)
+
+query = st.text_input("Enter your question")
+
+if query:
+    answer = get_answer(query, model, index, df, embeddings)
+    st.success(answer)