requirements.txt

transformers
sentence-transformers
faiss-cpu
streamlit
numpy

app.py

@@ -1,5 +1,3 @@
-# Required Libraries Installation
-
 # Import necessary modules
 from transformers import pipeline
 from sentence_transformers import SentenceTransformer
@@ -33,19 +31,4 @@ def retrieve_documents(query, top_k=3):
     distances, indices = index.search(query_embedding, top_k)
     return [documents[i]['text'] for i in indices[0]]
 
-# Function to generate an answer using the retrieved documents
-def ask_question(question):
-    retrieved_docs = retrieve_documents(question)
-    context = " ".join(retrieved_docs)
-    answer = question_answerer(question=question, context=context)
-    return answer['answer']
-
-# Streamlit Interface for the RAG App
-st.title("Economic and Population Growth Advisor")
-st.write("Ask questions related to economic and population growth. This app uses retrieval-augmented generation to provide answers based on relevant documents.")
-
-# Input for the question
-question = st.text_input("Enter your question:")
-if question:
-    answer = ask_question(question)
-    st.write("Answer:", answer)
+# Function to generate an answer using the ret