Create app.py

app.py

@@ -1,53 +1 @@
-# 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)
+streamlit run app.py