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# app.py
from transformers import pipeline
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
import gradio as gr
# Initialize a free question-answering model from Hugging Face
question_answerer = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
# Load or create data 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."},
# Add more documents as needed
]
# Embed documents for retrieval using SentenceTransformer
embedder = SentenceTransformer('all-MiniLM-L6-v2') # A lightweight embedding model
document_embeddings = [embedder.encode(doc['text']) for doc in documents]
# Convert embeddings to a FAISS index for similarity search
index = faiss.IndexFlatL2(384) # Dimension of the embeddings
index.add(np.array(document_embeddings))
# Define the RAG retrieval function
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]]
# Implement the question-answering function with retrieval
def ask_question(question):
retrieved_docs = retrieve_documents(question)
context = " ".join(retrieved_docs)
answer = question_answerer(question=question, context=context)
return answer['answer']
# Create Gradio Interface for the RAG app
def rag_interface(question):
answer = ask_question(question)
return answer
interface = gr.Interface(
fn=rag_interface,
inputs="text",
outputs="text",
title="Economic and Population Growth Advisor",
description="Ask questions related to economic and population growth. This app uses retrieval-augmented generation to provide answers based on relevant documents."
)
interface.launch(debug=True)
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