Create app.py

app.py

@@ -0,0 +1,64 @@
+import os
+import streamlit as st
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.document_loaders import PyPDFLoader
+from langchain.chains import RetrievalQA
+from langchain.llms import OpenAI
+from dotenv import load_dotenv
+
+# Load API key from Hugging Face secrets
+load_dotenv()
+OPENAI_API_KEY = os.getenv("GROQ_API_KEY")
+if not OPENAI_API_KEY:
+    st.error("API key is not set. Please set GROQ_API_KEY in Hugging Face secrets.")
+
+# Configure OpenAI API key
+os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
+
+# Streamlit app UI
+st.title("RAG-based Application")
+st.write("Upload a PDF, ask questions, and get answers based on the document content.")
+
+# Upload PDF file
+uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
+
+if uploaded_file:
+    # Load PDF
+    loader = PyPDFLoader(uploaded_file)
+    documents = loader.load()
+
+    # Split the text into chunks
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    chunks = text_splitter.split_documents(documents)
+
+    # Tokenize and store data in FAISS vector database
+    st.write("Processing the document and creating vector database...")
+    embeddings = OpenAIEmbeddings()
+    vector_db = FAISS.from_documents(chunks, embeddings)
+
+    # Save vector database
+    faiss_file = "vector_store.faiss"
+    vector_db.save_local(faiss_file)
+    st.success(f"Vector database saved as {faiss_file}.")
+
+    # Question-Answer Retrieval
+    st.write("You can now ask questions about the document.")
+    query = st.text_input("Enter your question:")
+
+    if query:
+        # Initialize QA Chain
+        retriever = vector_db.as_retriever()
+        llm = OpenAI(model="text-davinci-003", temperature=0.7)
+        qa_chain = RetrievalQA(llm=llm, retriever=retriever)
+
+        # Get the answer
+        with st.spinner("Generating answer..."):
+            answer = qa_chain.run(query)
+        st.success("Answer:")
+        st.write(answer)
+
+# Deployment instructions
+st.write("To deploy this app on Hugging Face, use the following command:")
+st.code("huggingface-cli login && huggingface-cli deploy --app-dir <your_streamlit_directory>")