Create app.py

app.py

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+import streamlit as st
+import pandas as pd
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from transformers import T5ForConditionalGeneration, T5Tokenizer
+
+# Load the Sentence Transformer and T5 model
+@st.cache(allow_output_mutation=True)
+def load_models():
+    embedding_model = SentenceTransformer('all-MiniLM-L6-v2')
+    qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
+    tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small")
+    return embedding_model, qa_model, tokenizer
+
+embedding_model, qa_model, tokenizer = load_models()
+
+# Upload and load the CSV file
+st.title("Economics & Population Advisor")
+uploaded_file = st.file_uploader("Upload your CSV file with economic documents", type=["csv"])
+
+if uploaded_file is not None:
+    # Load CSV
+    df = pd.read_csv(uploaded_file, error_bad_lines=False, engine='python')
+    st.write("Dataset Preview:", df.head())
+
+    # Assume 'text' column contains the document text; replace with actual column name
+    documents = df['text'].tolist() if 'text' in df.columns else st.text_input("Specify the text column name:")
+
+    # Create embeddings for FAISS indexing
+    st.write("Indexing documents...")
+    embeddings = embedding_model.encode(documents)
+    dimension = embeddings.shape[1]
+    index = faiss.IndexFlatL2(dimension)
+    index.add(np.array(embeddings))
+    st.write("Indexing complete.")
+
+    # Function to generate response
+    def generate_summary(context):
+        inputs = tokenizer("summarize: " + context, return_tensors="pt", max_length=512, truncation=True)
+        outputs = qa_model.generate(inputs["input_ids"], max_length=150, min_length=50, length_penalty=2.0)
+        return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    # RAG functionality: Ask a question, retrieve documents, and generate an answer
+    st.subheader("Ask a Question about Economic Data")
+    question = st.text_input("Enter your question:")
+
+    if st.button("Get Answer") and question:
+        question_embedding = embedding_model.encode([question])
+        D, I = index.search(np.array(question_embedding), k=3)
+        retrieved_docs = [documents[i] for i in I[0]]
+        context = " ".join(retrieved_docs)
+        answer = generate_summary(context)
+        
+        st.write("Answer:", answer)