Delete gradio_pr.py

gradio_pr.py

@@ -1,89 +0,0 @@
-import gradio as gr
-import os
-import pathlib
-import random
-#import torch
-#import transformers
-from langchain.document_loaders import TextLoader
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.embeddings.openai import OpenAIEmbeddings
-from langchain.vectorstores import FAISS
-from langchain.prompts.chat import (
-    ChatPromptTemplate,
-    SystemMessagePromptTemplate,
-    HumanMessagePromptTemplate,
-)
-from langchain.chat_models import ChatOpenAI
-from langchain.chains import RetrievalQAWithSourcesChain
-
-os.environ["OPENAI_API_KEY"] = "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"
-
-# Set the data store directory
-DATA_STORE_DIR = "data_store"
-
-if os.path.exists(DATA_STORE_DIR):
-    vector_store = FAISS.load_local(
-        DATA_STORE_DIR,
-        OpenAIEmbeddings()
-    )
-else:
-    print(f"Missing files. Upload index.faiss and index.pkl files to {DATA_STORE_DIR} directory first")
-
-system_template = """Use the following pieces of context to answer the user's question.
-Take note of the sources and include them in the answer in the format: "SOURCES: source1", use "SOURCES" in capital letters regardless of the number of sources.
-If you don't know the answer, just say "I don't know", don't try to make up an answer.
-----------------
-{summaries}"""
-
-messages = [
-    SystemMessagePromptTemplate.from_template(system_template),
-    HumanMessagePromptTemplate.from_template("{question}")
-]
-prompt = ChatPromptTemplate.from_messages(messages)
-
-llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0, max_tokens=256)
-
-chain_type_kwargs = {"prompt": prompt}
-chain = RetrievalQAWithSourcesChain.from_chain_type(
-    llm=llm,
-    chain_type="stuff",
-    retriever=vector_store.as_retriever(),
-    return_source_documents=True,
-    chain_type_kwargs=chain_type_kwargs
-)
-
-
-class Chatbot:
-    def __init__(self):
-        self.query = None
-
-    def chat(self, query):
-        self.query = query
-        result = chain(query)
-        return result['answer']
-
-
-chatbot = Chatbot()
-
-
-# Create a Gradio interface
-def chat_interface(query):
-    response = chatbot.chat(query)
-    return response
-
-
-# inputs = gr.inputs.Textbox(lines=2, placeholder="Enter your message here...")
-# outputs = gr.outputs.Textbox()
-
-# chat_interface = gr.ChatInterface(chat_interface, inputs=inputs, outputs=outputs)
-#
-# chat_interface.launch()
-gr.ChatInterface(
-    chat_interface,
-    chatbot=gr.Chatbot(height=300),
-    textbox=gr.Textbox(placeholder="Ask me a yes or no question"),
-    description="Ask Yes Man any question",
-    theme="soft",
-    cache_examples=True,
-    clear_btn="Clear",
-).launch()