Update app.py

app.py

@@ -1,31 +1,78 @@
-from pathlib import Path
-from typing import Union
+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.callbacks import get_openai_callback
+from langchain import HuggingFaceHub, LLMChain
+from langchain.embeddings import HuggingFaceHubEmbeddings,HuggingFaceInferenceAPIEmbeddings
+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,
+)
 
-from pypdf import PdfReader
-from transformers import pipeline
-import gradio as gr
+from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
 
+embeddings = HuggingFaceInferenceAPIEmbeddings(
+    api_key=token, model_name="sentence-transformers/all-MiniLM-l6-v2"
+)
 
-question_answerer = pipeline(task="question-answering", model="deepset/tinyroberta-squad2")
 
+def main():
+    
+    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")
+      
+      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()
 
-def get_text_from_pdf(pdf_file: Union[str, Path]) -> str:
-    """Read the PDF from the given path and return a string with its entire content."""
-    reader = PdfReader(pdf_file)
+        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)
+    
 
-    # Extract text from all pages
-    full_text = ""
-    for page in reader.pages:
-        full_text += page.extract_text()
-    return full_text
-
-
-def answer_doc_question(pdf_file, question):
-    pdf_text = get_text_from_pdf(pdf_file)
-    answer = question_answerer(question, pdf_text)
-    return answer["answer"]
-
-
-pdf_input = gr.File(file_types=[".pdf"], label="Upload a PDF document and ask a question about it.")
-question = gr.Textbox(label="Type a question regarding the uploaded document here.")
-gr.Interface(fn=answer_doc_question, inputs=[pdf_input, question], outputs="text").launch()
+if __name__ == '__main__':
+    main()