Update app.py

app.py

@@ -1,78 +1,31 @@
-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 pathlib import Path
+from typing import Union
 
-from langchain.embeddings import HuggingFaceInferenceAPIEmbeddings
+from pypdf import PdfReader
+from transformers import pipeline
+import gradio as gr
 
-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()
 
-        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)
-    
+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)
 
-if __name__ == '__main__':
-    main()
+    # 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()