added app.py file

app.py

@@ -0,0 +1,90 @@
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain_community.document_loaders import WebBaseLoader
+# from langchain_community.embeddings import OllamaEmbeddings
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+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.faiss import FAISS
+
+import time
+from PyPDF2 import PdfReader
+import tempfile
+from dotenv import load_dotenv
+load_dotenv()
+
+## Load the Groq API key
+groq_api_key = os.environ['GROQ_API_KEY']
+google_api_key = os.environ['GOOGLE_API_KEY']
+
+st.title("Ask your questions from pdf(s) or website")
+option = None
+
+# Prompt user to choose between PDFs or website
+option = st.radio("Choose input type:", ("PDF(s)", "Website"), index=None)
+
+
+def get_pdf_processed(pdf_docs):
+    text=""
+    for pdf in pdf_docs:
+        pdf_reader= PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+def llm_model():
+    llm = ChatGroq(groq_api_key=groq_api_key, model="mixtral-8x7b-32768")
+    prompt = ChatPromptTemplate.from_template(
+    """
+    Answer the question based on the provided context only.
+    Please provide the most accurate response based on the question
+    <context>
+    {context}
+    </context>
+    Questions:{input}
+    """
+    )
+    document_chain = create_stuff_documents_chain(llm,prompt)
+    retriever = st.session_state.vector.as_retriever() if st.session_state.vector else None
+    retrieval_chain = create_retrieval_chain(retriever,document_chain)
+
+    prompt = st.text_input("Input your question here")
+
+    if prompt:
+
+        start =time.process_time()
+        response = retrieval_chain.invoke({"input":prompt})
+        print("Response time :", time.process_time()-start)
+        st.write(response['answer'])
+
+st.session_state.embeddings =GoogleGenerativeAIEmbeddings(model = 'models/embedding-001',google_api_key=google_api_key)
+st.session_state.text_splitter = RecursiveCharacterTextSplitter(chunk_size =1000, chunk_overlap= 200)
+
+if option:
+    if option == "Website":
+        website_link = st.text_input("Enter the website link:")
+        if website_link:
+            st.session_state.loader = WebBaseLoader(website_link)
+            st.session_state.docs = st.session_state.loader.load()
+            st.session_state.final_documents = st.session_state.text_splitter.split_documents(st.session_state.docs)
+            st.session_state.vector = FAISS.from_documents(st.session_state.final_documents,st.session_state.embeddings)
+            llm_model()
+
+    elif option == "PDF(s)":
+        pdf_files = st.file_uploader("Upload your PDF files", type=["pdf"], accept_multiple_files=True)
+        if pdf_files:
+            st.session_state.docs = get_pdf_processed(pdf_files)
+            st.session_state.final_documents = st.session_state.text_splitter.split_text(st.session_state.docs)
+            st.session_state.vector = FAISS.from_texts(st.session_state.final_documents,st.session_state.embeddings)
+            llm_model()
+
+        # with st.expander("Document Similarity Search"):
+        #     for i, doc in enumerate(response['context']):
+        #         st.write(doc.page_content)
+        #         st.write("-----------------------------")
+
+        
+