Update app.py

app.py

@@ -1,43 +1,28 @@
-# Continue with the rest of the code
-from langchain.chains import RetrievalQA
-from langchain.document_loaders import TextLoader
+import streamlit as st
 from langchain.embeddings import SentenceTransformerEmbeddings
 from langchain.vectorstores import FAISS
 from transformers import pipeline
 
+# Initialize embedding model
+embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
+qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
 
-
-# Paste your data here
-data = """
-Enter your text data here. For example:
-"""
-
-# Split data into chunks for embedding
 def chunk_text(text, chunk_size=500):
     words = text.split()
     chunks = [" ".join(words[i:i + chunk_size]) for i in range(0, len(words), chunk_size)]
     return chunks
 
-# Prepare the text chunks
-text_chunks = chunk_text(data)
-
-# Generate embeddings and index the data
-embeddings = SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")
-vectorstore = FAISS.from_texts(text_chunks, embeddings)
-
-# Load a simple LLM (Hugging Face model)
-from transformers import pipeline
-qa_pipeline = pipeline("question-answering", model="distilbert-base-uncased-distilled-squad")
-
-# Define a function to perform QA
-def answer_question(question):
+# Streamlit app
+st.title("Simple RAG Application")
+data = st.text_area("Paste your text here:")
+if data:
+    text_chunks = chunk_text(data)
+    vectorstore = FAISS.from_texts(text_chunks, embeddings)
     retriever = vectorstore.as_retriever(search_kwargs={"k": 3})
-    relevant_docs = retriever.get_relevant_documents(question)
-    context = " ".join([doc.page_content for doc in relevant_docs])
-    answer = qa_pipeline(question=question, context=context)
-    return answer["answer"]
-
-# Ask a question
-import sentence_transformers
-print("sentence-transformers is installed successfully!")
-
+    
+    question = st.text_input("Ask a question:")
+    if question:
+        relevant_docs = retriever.get_relevant_documents(question)
+        context = " ".join([doc.page_content for doc in relevant_docs])
+        answer = qa_pipeline(question=question, context=context)
+        st.write("Answer:", answer["answer"])