Update src/app.py

src/app.py

@@ -1,3 +1,144 @@
+# 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")
+# 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 process:
+#     if uploaded_file is not None:
+#         file_bytes = uploaded_file.read()
+#         process_pdf(file_bytes)
+#     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,
+#     )
+
+
 import streamlit as st
 import os
 from langchain_groq import ChatGroq
@@ -52,26 +193,18 @@ st.sidebar.title("Upload your pdf")
 
 main_placeholder = st.empty()
 #Document upload
-uploaded_file = st.sidebar.file_uploader("_____________________________________", type="pdf")
+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.")
-
+#Document processing to convert it into vectors
 if process:
-    if uploaded_file is not None:
-        file_bytes = uploaded_file.read()
-        process_pdf(file_bytes)
+    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")