Create aap.py

aap.py

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+import os
+from huggingface_hub import login
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+import streamlit as st
+
+# Authenticate with Hugging Face using the API key from environment variables
+# If running on Hugging Face Spaces, make sure the API key is stored as a secret.
+login(os.environ["HF_API_KEY"])
+
+# Initialize a question-answering model
+question_answerer = pipeline("question-answering", model="distilbert-base-cased-distilled-squad")
+
+# Load or create data on Pakistan's economic and population growth trends
+documents = [
+    {"id": 1, "text": "Pakistan's population growth rate is approximately 2%, making it one of the fastest-growing populations in South Asia."},
+    {"id": 2, "text": "The youth population in Pakistan is significant, with over 60% of the population under the age of 30."},
+    {"id": 3, "text": "Pakistan's economy relies heavily on agriculture, with about 20% of GDP coming from this sector."},
+    {"id": 4, "text": "In recent years, Pakistan has been investing in infrastructure projects, such as the China-Pakistan Economic Corridor (CPEC), to boost economic growth."},
+    {"id": 5, "text": "Urbanization is rapidly increasing in Pakistan, with cities like Karachi and Lahore seeing substantial population inflows."},
+    {"id": 6, "text": "Remittances from overseas Pakistanis play a critical role in supporting the country's economy."},
+    {"id": 7, "text": "Pakistan's literacy rate has improved over the years but remains lower than the regional average."},
+    {"id": 8, "text": "The government is focusing on initiatives for digital economy growth, particularly in the technology and freelancing sectors."},
+    {"id": 9, "text": "Pakistan’s unemployment rate is a concern, especially among young people entering the job market."},
+    {"id": 10, "text": "The fertility rate in Pakistan has been declining but remains above the replacement rate."},
+]
+
+# Embed documents for retrieval using SentenceTransformer
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+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 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):
+    # Retrieve relevant documents
+    retrieved_docs = retrieve_documents(question)
+    # Combine retrieved documents into a single context
+    context = " ".join(retrieved_docs)
+    
+    # Generate an answer based on retrieved context
+    answer = question_answerer(question=question, context=context)
+    return answer['answer']
+
+# Streamlit Interface
+def streamlit_interface():
+    # Set title and description
+    st.title("Pakistan Economic and Population Growth Advisor")
+    st.write("Ask questions related to Pakistan's economic and population growth. This app uses retrieval-augmented generation to provide answers based on relevant documents about Pakistan.")
+
+    # Input: User enters a question
+    question = st.text_input("Ask a question:")
+
+    if question:
+        # Get the answer using the RAG system
+        answer = ask_question(question)
+        # Output the answer
+        st.write("Answer:", answer)
+
+if __name__ == "__main__":
+    # Run the Streamlit app
+    streamlit_interface()