Delete src/app.py

src/app.py

@@ -1,132 +0,0 @@
-import streamlit as st
-import os
-from langchain_groq import ChatGroq
-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 PyPDFDirectoryLoader
-from langchain_google_genai import GoogleGenerativeAIEmbeddings
-from dotenv import load_dotenv
-from  PDFprocess_sample import process_pdf
-
-# Loading GROQ and Google API
-load_dotenv()
-
-GROQ_API_KEY = os.getenv('GROQ_API_KEY')
-os.environ["GOOGLE_API_KEY"]= os.getenv('GOOGLE_API_KEY')
-
-#Loading CSS files
-
-def load_css(file_name):
-    with open(file_name) as f:
-        css = f.read()
-    st.markdown(f"<style>{css}</style>", unsafe_allow_html=True)
-
-load_css('CSS/style.css')
-
-#setting up LLM
-llm = ChatGroq(
-    api_key=GROQ_API_KEY,
-    model_name="Llama3-8b-8192"
-)
-
-
-prompt = ChatPromptTemplate.from_template(
-    """
-    Answer the questions based on the provided context only.
-    Please provide the most accurate response based on the question. Try to answer in detail in 1500 words
-    <context>
-    {context}
-    <context>
-    Questions: {input}
-    """
-)
-
-input_method = st.sidebar.selectbox("Choose a method" , ["Choose input method...","Interact with Doc", "Get Ques from Doc"])
-
-
-
-st.sidebar.title("Upload your pdf")
-
-main_placeholder = st.empty()
-#Document upload
-uploaded_file = st.sidebar.file_uploader("_____________________________________", type="pdf", accept_multiple_files=True)
-st.sidebar.write("Press Submit to process:")
-process = st.sidebar.button("Submit")
-
-#Document processing to convert it into vectors
-if process:
-    if uploaded_file:
-        # Process the uploaded PDF file
-        process_pdf(uploaded_file)
-    else:
-        st.warning("Please upload a PDF file.")
-        
-if input_method == "Choose input method...":
-    st.title(f"Welcome You all!")
-    st.title("Choose an option in the sidebar")
-    st.title("Now, let's get started!") 
-    
-        
-#If User wants to interact with the document
-elif input_method == "Interact with Doc":
-    st.title(f"let's Interact with pdf's")
-
-    prompt1 = st.text_input("______", placeholder="Enter your Question")
-
-
-    # Generate response if question is entered
-    if prompt1 and "vectors" in st.session_state:
-        document_chain = create_stuff_documents_chain(llm, prompt)
-        retriever = st.session_state.vectors.as_retriever()
-        retrieval_chain = create_retrieval_chain(retriever, document_chain)
-        
-        
-        response = retrieval_chain.invoke({'input': prompt1})
-
-        # st.write(response['answer'])
-        
-        #Get the respose in the card
-        st.markdown(
-        f"""
-        <div class="card">
-            <div class="response">{response['answer']}</div>
-        </div>
-        """,
-        unsafe_allow_html=True,
-    )
-        
-        
-        
-#When User wants to get questions from the doc based on certain topic
-elif input_method == "Get Ques from Doc":
-    st.title(f"Let's Get Ques from Document")
-    
-    prompt2 = """Based on the topic of {topic}, 
-                kindly provide a comprehensive list of all possible questions that could arise. 
-                For each question, provide detailed and explanatory answers in atleast 1000 words detail based on the context,
-                ensuring that the responses are as informative as possible.
-                make sure you strictly follow the {topic}"""
-    topic = st.text_input("Enter a topic", placeholder="What is your topic")
-    
-    # Generate response if question is entered
-    if topic and "vectors" in st.session_state:
-        document_chain = create_stuff_documents_chain(llm, prompt)
-        retriever = st.session_state.vectors.as_retriever()
-        retrieval_chain = create_retrieval_chain(retriever, document_chain)
-        
-        
-        response = retrieval_chain.invoke({'input': prompt2})
-        
-        #Get the respose in the card
-        st.markdown(
-        f"""
-        <div class="card">
-            <div class="response">{response['answer']}</div>
-        </div>
-        """,
-        unsafe_allow_html=True,
-    )
-