test 2

app.py

@@ -2,12 +2,7 @@ import streamlit as st
 import os
 from PyPDF2 import PdfReader
 import openpyxl
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.embeddings import GooglePalmEmbeddings
-from langchain.llms import HuggingFaceTransformers  # Updated import
-from langchain.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-from langchain.memory import ConversationBufferMemory
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, pipeline
 
 os.environ['GOOGLE_API_KEY'] = 'AIzaSyD8uzXToT4I2ABs7qo_XiuKh8-L2nuWCEM'
 
@@ -20,88 +15,59 @@ def get_pdf_text(pdf_docs):
     return text
 
 def get_excel_text(excel_docs):
-  text = ""
-  for excel_doc in excel_docs:
-      workbook = openpyxl.load_workbook(filename=excel_doc)
-      for sheet in workbook:
-          for row in sheet:
-              for cell in row:
-                  text += str(cell.value) + " "
-  return text.strip()
-
-
-def get_text_chunks(text):
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
-    chunks = text_splitter.split_text(text)
-    return chunks
-
-def get_vector_store(text_chunks):
-    embeddings = GooglePalmEmbeddings()
-    vector_store = FAISS.from_texts(text_chunks, embedding=embeddings)
-    return vector_store
-
-def get_conversational_chain(vector_store):
-    llm = HuggingFaceTransformers(model_name="HanNayeoniee/LHK_DPO_v1")
-    memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
-    conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=vector_store.as_retriever(), memory=memory)
-    return conversation_chain
+    text = ""
+    for excel_doc in excel_docs:
+        workbook = openpyxl.load_workbook(filename=excel_doc)
+        for sheet in workbook:
+            for row in sheet:
+                for cell in row:
+                    text += str(cell.value) + " "
+    return text.strip()
 
-def get_user_input(user_question):
+def get_user_input(user_question, qa_pipeline):
     with st.container():
-        response = st.session_state.conversation({'question': user_question})
-        st.session_state.chatHistory = response['chat_history']
-        file_contents = ""
-        left , right = st.columns((2,1))
-        with left:
-            for i, message in enumerate(st.session_state.chatHistory):
-                if i % 2 == 0:
-                    st.write("User: ", message.content)
-                else:
-                    st.write("Bot: ", message.content)
-            st.success("Done !")
-        with right:
-            for message in st.session_state.chatHistory:
-                file_contents += f"{message.content}\n"
-            file_name = "Chat_History.txt"
+        response = qa_pipeline(question=user_question, context=st.session_state.raw_text)
+        st.write("Answer:", response["answer"])
 
 def main():
-  st.set_page_config("DocChat")
-  st.header("DocChat - Chat with multiple documents")
-  st.write("---")
-  with st.container():
-    with st.sidebar:
-      st.title("Settings")
-      st.subheader("Upload Documents")
-      st.markdown("**PDF files:**")
-      pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True)
-      if st.button("Process PDF file"):
-        with st.spinner("Processing PDFs..."):
-          raw_text = get_pdf_text(pdf_docs)
-          text_chunks = get_text_chunks(raw_text)
-          vector_store = get_vector_store(text_chunks)
-          st.session_state.conversation = get_conversational_chain(vector_store)
-          st.success("PDF processed successfully!")
+    st.set_page_config("DocChat")
+    st.header("DocChat - Chat with multiple documents")
+    st.write("---")
+    
+    qa_pipeline = None
+    
+    with st.container():
+        with st.sidebar:
+            st.title("Settings")
+            st.subheader("Upload Documents")
+            st.markdown("**PDF files:**")
+            pdf_docs = st.file_uploader("Upload PDF Files", accept_multiple_files=True)
+            if st.button("Process PDF file"):
+                with st.spinner("Processing PDFs..."):
+                    raw_text = get_pdf_text(pdf_docs)
+                    st.session_state.raw_text = raw_text
+                    st.success("PDF processed successfully!")
 
-      st.markdown("**Excel files:**")
-      excel_docs = st.file_uploader("Upload Excel Files", accept_multiple_files=True)
-      if st.button("Process Excel file"):
-        with st.spinner("Processing Excel files..."):
-          raw_text = get_excel_text(excel_docs)
-          text_chunks = get_text_chunks(raw_text)
-          vector_store = get_vector_store(text_chunks)
-          st.session_state.conversation = get_conversational_chain(vector_store)
-          st.success("Excel file processed successfully!")
+            st.markdown("**Excel files:**")
+            excel_docs = st.file_uploader("Upload Excel Files", accept_multiple_files=True)
+            if st.button("Process Excel file"):
+                with st.spinner("Processing Excel files..."):
+                    raw_text = get_excel_text(excel_docs)
+                    st.session_state.raw_text = raw_text
+                    st.success("Excel file processed successfully!")
 
-  with st.container():
-      st.subheader("Document Q&A")
-      st.write('Ask a question : ')
-      user_question = st.text_input("Ask a Question from the document")
-      if "conversation" not in st.session_state:
-          st.session_state.conversation = None
-      if "chatHistory" not in st.session_state:
-          st.session_state.chatHistory = None
-      if user_question:
-          get_user_input(user_question)
+    with st.container():
+        st.subheader("Document Q&A")
+        st.write('Ask a question : ')
+        user_question = st.text_input("Ask a Question from the document")
+        if user_question:
+            if not qa_pipeline and "raw_text" in st.session_state:
+                model_name = "notabaka/DocQA"
+                tokenizer = AutoTokenizer.from_pretrained(model_name)
+                model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+                qa_pipeline = pipeline("question-answering", model=model, tokenizer=tokenizer)
+            if qa_pipeline:
+                get_user_input(user_question, qa_pipeline)
 
 if __name__ == "__main__":
-    main()
+    main()

requirements.txt

@@ -3,4 +3,6 @@ langchain
 PyPDF2
 faiss-cpu
 streamlit
-openpyxl
+openpyxl
+transformers
+torch