Upload app.py

app.py

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+from dotenv import load_dotenv
+import streamlit as st
+import os
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings.openai import OpenAIEmbeddings
+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
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain import HuggingFaceHub, LLMChain
+from langchain.embeddings import HuggingFaceHubEmbeddings
+token = os.environ['HF_TOKEN']
+repo_id = "sentence-transformers/all-mpnet-base-v2"
+hf = HuggingFaceHubEmbeddings(
+    repo_id=repo_id,
+    task="feature-extraction",
+    huggingfacehub_api_token= token,
+)
+
+
+
+
+def main():
+    load_dotenv()
+    st.set_page_config(page_title="Ask your PDF")
+    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 = OpenAIEmbeddings()
+      # embeddings = query(chunks)
+      # embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2")
+      embeddings = hf
+      knowledge_base = FAISS.from_texts(chunks, embeddings)
+      
+      # 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 = OpenAI()
+
+        hub_llm = HuggingFaceHub(
+        repo_id='HuggingFaceH4/zephyr-7b-beta',
+    model_kwargs={'temperature':0.01,"max_length": 2048,},
+    huggingfacehub_api_token=token)
+        llm = hub_llm
+        chain = load_qa_chain(llm, chain_type="stuff")
+        with get_openai_callback() as cb:
+          response = chain.run(input_documents=docs, question=user_question)
+          print(cb)
+           
+        st.write(response)
+    
+
+if __name__ == '__main__':
+    main()