Create app.py

app.py

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+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.chains import create_retrieval_chain
+from langchain_community.vectorstores import FAISS
+from langchain_community.document_loaders import PyPDFLoader
+from dotenv import load_dotenv
+import tempfile
+
+# Show title and description.
+st.title("📄 Document question answering")
+st.write(
+    "Upload a document below and ask a question about it – Groq will answer! "
+    "To use this app, you need to provide an Groq API key, which you can get [here](https://console.groq.com/keys). "
+)
+
+# Ask user for their Groq API key via `st.text_input`.
+# Alternatively, you can store the API key in `./.streamlit/secrets.toml` and access it
+# via `st.secrets`, see https://docs.streamlit.io/develop/concepts/connections/secrets-management
+# Define model options
+model_options = [
+    "llama3-8b-8192",
+    "llama3-70b-8192",
+    "llama-3.1-8b-instant",
+    "llama-3.1-70b-versatile",
+    "llama-3.2-1b-preview",
+    "llama-3.2-3b-preview",
+    "llama-3.2-11b-text-preview",
+    "llama-3.2-90b-text-preview",
+    "mixtral-8x7b-32768",
+    "gemma-7b-it",
+    "gemma2-9b-it"
+]
+# Sidebar elements
+with st.sidebar:
+    selected_model = st.selectbox("Select any Groq Model", model_options)
+    groq_api_key = st.text_input("Groq API Key", type="password")
+    if not groq_api_key:
+        st.info("Please add your Groq API key to continue.", icon="🗝️")
+    else:
+    
+        # Create an Groq client.
+        llm = ChatGroq(groq_api_key=groq_api_key, model_name=selected_model)
+    
+        prompt = ChatPromptTemplate.from_template(
+        """
+        Answer the questions based on the provided context only.
+        Please provide the most accurate response based on the question.
+        <context>
+        {context}
+        <context>
+        Questions: {input}
+        """
+        )
+        
+        def create_vector_db_out_of_the_uploaded_pdf_file(pdf_file):
+        
+    
+            if "vector_store" not in st.session_state:
+        
+                with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        
+                    temp_file.write(pdf_file.read())
+        
+                    pdf_file_path = temp_file.name
+        
+                st.session_state.embeddings = HuggingFaceEmbeddings(model_name='BAAI/bge-small-en-v1.5', model_kwargs={'device': 'cpu'}, encode_kwargs={'normalize_embeddings': True})
+                
+                st.session_state.loader = PyPDFLoader(pdf_file_path)
+        
+                st.session_state.text_document_from_pdf = st.session_state.loader.load()
+        
+                st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+                
+                st.session_state.final_document_chunks = st.session_state.text_splitter.split_documents(st.session_state.text_document_from_pdf)
+        
+                st.session_state.vector_store = FAISS.from_documents(st.session_state.final_document_chunks, st.session_state.embeddings)
+        
+        
+        pdf_input_from_user = st.file_uploader("Upload the PDF file", type=['pdf'])
+        
+        
+        if pdf_input_from_user is not None:
+        
+            if st.button("Create the Vector DB from the uploaded PDF file"):
+                
+                if pdf_input_from_user is not None:
+                    
+                    create_vector_db_out_of_the_uploaded_pdf_file(pdf_input_from_user)
+                    
+                    st.success("Vector Store DB for this PDF file Is Ready")
+                
+                else:
+                    
+                    st.write("Please upload a PDF file first")
+        
+    
+# Main section for question input and results
+if "vector_store" in st.session_state:
+
+    user_prompt = st.text_input("Enter Your Question related to the uploaded PDF")
+
+    if st.button('Submit Prompt'):
+
+        if user_prompt:
+            
+            if "vector_store" in st.session_state:
+
+                document_chain = create_stuff_documents_chain(llm, prompt)
+
+                retriever = st.session_state.vector_store.as_retriever()
+
+                retrieval_chain = create_retrieval_chain(retriever, document_chain)
+
+                response = retrieval_chain.invoke({'input': user_prompt})
+
+                st.write(response['answer'])
+
+            else:
+
+                st.write("Please embed the document first by uploading a PDF file.")
+
+        else:
+
+            st.error('Please write your prompt')