Create Chat_with_pdf_LLM.py

Chat_with_pdf_LLM.py

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+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.huggingface import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains.question_answering import load_qa_chain
+from langchain.llms import OpenAI
+from langchain.callbacks import get_openai_callback
+import os
+from streamlit_chat import message
+
+def LLM_pdf(model = 'google/flan-t5-large'):
+    # st.header("Ask your PDF 💬")
+    
+    # upload file
+    pdf = st.file_uploader("Upload your PDF", type="pdf")
+    
+    # extract the text
+    if pdf is not None:
+      pdf_reader = PdfReader(pdf)
+      text = ""
+      for page in pdf_reader.pages:
+        text += page.extract_text()
+        
+      # split into chunks
+      text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+      )
+      chunks = text_splitter.split_text(text)
+      
+      # create embeddings
+      embeddings = HuggingFaceEmbeddings()
+      knowledge_base = FAISS.from_texts(chunks, embeddings)
+        
+      if 'generated' not in st.session_state:
+          st.session_state['generated'] = []
+      if 'past' not in st.session_state:
+          st.session_state['past'] = []
+      
+      # show user input
+      user_question = st.text_input("Ask a question about your PDF:")
+      if user_question:
+        docs = knowledge_base.similarity_search(user_question)
+        
+        llm = HuggingFaceHub(repo_id="google/flan-t5-large", model_kwargs={"temperature":5,
+                                                      "max_length":64})
+        chain = load_qa_chain(llm, chain_type="stuff")
+        response = chain.run(input_documents=docs,question=user_question
+           
+        #st.write(response)
+        st.session_state.past.append(user_question)
+        st.session_state.generated.append(response)
+          
+      if st.session_state['generated']:
+        for i in range(len(st.session_state['generated'])-1, -1, -1):
+            message(st.session_state["generated"][i], key=str(i))
+            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user')