Delete app.py

app.py

@@ -1,158 +0,0 @@
-# -*- coding: utf-8 -*-
-"""Doc_chat_vegleges.ipynb
-
-Automatically generated by Colaboratory.
-
-Original file is located at
-    https://colab.research.google.com/drive/1G34ZCuupJZxNy-CFxjMNIa4_I3jynKqC
-
-# Setting up environment
-"""
-
-from PyPDF2 import PdfReader
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.vectorstores import ElasticVectorSearch, Pinecone, Weaviate, FAISS
-
-# Get your API keys from openai, you will need to create an account.
-# Here is the link to get the keys: https://platform.openai.com/account/billing/overview
-import os
-
-"""# Preprocessing document"""
-
-# location of the pdf file/files.
-reader = PdfReader('constitution.pdf')
-#reader = PdfReader('/content/WOW.pdf')
-#reader = PdfReader('/content/the_little_prince.pdf')
-
-# read data from the file
-raw_text = ''
-for i, page in enumerate(reader.pages):
-    text = page.extract_text()
-    if text:
-        raw_text += text
-
-# We need to split the text that we read into smaller chunks so that during information retreival we don't hit the token size limits.
-
-text_splitter = CharacterTextSplitter(
-    separator = "\n",
-    chunk_size = 1000,
-    chunk_overlap  = 200,
-    length_function = len,
-)
-texts = text_splitter.split_text(raw_text)
-
-len(texts)
-
-"""## Setting up doc search"""
-
-embeddings = OpenAIEmbeddings()
-doc_search = FAISS.from_texts(texts, embeddings)
-
-"""# Setting up chatbot"""
-
-from langchain.chains.question_answering import load_qa_chain
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.prompts import PromptTemplate
-from langchain_openai import OpenAI
-
-template = """You are a chatbot having a conversation with a human.
-
-Given the following extracted parts of a long document and a question, create a final answer based on the document ONLY and NOTHING else.
-Any questions outside of the document is irrelevant and you certanly dont know!
-
-{context}
-
-{chat_history}
-Human: {human_input}
-Chatbot:"""
-
-prompt = PromptTemplate(
-    input_variables=["chat_history", "human_input", "context"], template=template
-)
-
-memory = ConversationBufferWindowMemory(memory_key="chat_history", input_key="human_input",k=3)
-chain = load_qa_chain( OpenAI(), chain_type="stuff", memory=memory, prompt=prompt)
-
-"""## The chatbot should know the answer"""
-
-query = "Who wrote the constitution?"
-docs = doc_search.similarity_search(query)
-
-chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)['output_text']
-
-# query = "Acronyms?"
-# docs = doc_search.similarity_search(query)
-
-# chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)['output_text']
-
-# query = "Say 3 of them"
-# docs = doc_search.similarity_search(query)
-
-# chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)['output_text']
-
-"""## The chatbot should not know the answer."""
-
-query = "What is the capital of France?"
-docs = doc_search.similarity_search(query)
-
-chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)['output_text']
-
-"""## Previous chatbot (deprecated)"""
-
-#print(chain.memory.buffer)
-
-# from langchain.chains.question_answering import load_qa_chain
-# from langchain.llms import OpenAI
-
-# embeddings = OpenAIEmbeddings()
-# doc_search = FAISS.from_texts(texts, embeddings)
-
-# chain = load_qa_chain(OpenAI(), chain_type="stuff")
-
-# query = "Who wrote the constitution?"
-
-
-# answer = chain.run(input_documents=docs, question=query)
-# print(answer)
-
-# query = "What is the capital of france?"
-# answer = chain.run(input_documents=docs, question=query)
-# print(answer)
-
-"""# Demo
-
-## Setting up methods
-"""
-
-def chat(query,history):
-  docs = doc_search.similarity_search(query)
-  return chain({"input_documents": docs, "human_input": query}, return_only_outputs=True)['output_text']
-
-"""## Setting up UI with gradio"""
-
-import gradio as gr
-
-css = """
-body {
-    background-color: #FFFFFF; /* White background */
-}
-
-.gradio-chat-interface, .gradio-chat-input {
-    background-color: #E0FFFF; /* Light blue-green background */
-}
-"""
-
-gr.ChatInterface(
-    chat,
-    chatbot=gr.Chatbot(height=500),
-    title="Doc-chat",
-    description="Ask about the constitution!",
-    theme="soft",
-    examples=["Who wrote the constitution?","What is the capital of France?"],
-    cache_examples=True,
-    retry_btn=None,
-    undo_btn="Delete Previous",
-    clear_btn="Clear",
-    css=css
-).launch()