Create app.py

app.py

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+# Required Libraries Installation
+!pip install transformers sentence-transformers faiss-cpu streamlit
+
+# Import necessary modules
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import streamlit as st
+
+# Initialize a Question-Answering model from Hugging Face
+question_answerer = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+
+# Example dataset on economic and population growth trends
+documents = [
+    {"id": 1, "text": "Global economic growth is projected to slow down due to inflation."},
+    {"id": 2, "text": "Population growth in developing countries continues to increase."},
+    {"id": 3, "text": "Economic growth in advanced economies is experiencing fluctuations due to market changes."},
+    # Additional documents can be added here
+]
+
+# Embed documents using SentenceTransformer for retrieval
+embedder = SentenceTransformer('all-MiniLM-L6-v2')  # Lightweight model for embeddings
+document_embeddings = [embedder.encode(doc['text']) for doc in documents]
+
+# Convert embeddings to a FAISS index for similarity search
+dimension = 384  # Make sure this matches the SentenceTransformer embedding dimension
+index = faiss.IndexFlatL2(dimension)
+index.add(np.array(document_embeddings))
+
+# Function for retrieving relevant documents based on query
+def retrieve_documents(query, top_k=3):
+    query_embedding = embedder.encode(query).reshape(1, -1)
+    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)