Create app.py

app.py

@@ -1,216 +1,7 @@
-import os
-from typing import Any
-
 import gradio as gr
-import openai
-import pandas as pd
-from IPython.display import Markdown, display
-from langchain.document_loaders import PyPDFLoader
-from langchain.embeddings import OpenAIEmbeddings
-from langchain.indexes import VectorstoreIndexCreator
-from langchain.text_splitter import CharacterTextSplitter
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.llms import OpenAI
-from langchain.vectorstores import DocArrayInMemorySearch
-from uuid import uuid4
-
-css_style = """
-.gradio-container {
-    font-family: "IBM Plex Mono";
-}
-"""
-
-
-class myClass:
-    def __init__(self) -> None:
-        self.openapi = ""
-        self.valid_key = False
-        self.docs_ready = False
-        self.status = "⚠️Waiting for documents and key⚠️"
-        self.uuid = uuid4()
-        pass
-
-    def check_status(self):
-        if self.docs_ready and self.valid_key:
-            out = "✨Ready✨"
-        elif self.docs_ready:
-            out = "⚠️Waiting for key⚠️"
-        elif self.valid_key:
-            out = "⚠️Waiting for documents⚠️"
-        else:
-            out = "⚠️Waiting for documents and key⚠️"
-
-        self.status = out
-
-    def validate_key(self, myin):
-        assert isinstance(myin, str)
-        self.valid_key = True
-        self.openai_api_key = myin.strip()
-        self.embedding = OpenAIEmbeddings(openai_api_key=self.openai_api_key)
-        self.llm = OpenAI(openai_api_key=self.openai_api_key)
-
-        self.check_status()
-        return [self.status]
-
-    def request_pathname(self, files, data):
-        if files is None:
-            self.docs_ready = False
-            self.check_status()
-            return (
-                pd.DataFrame(data, columns=["filepath", "citation string", "key"]),
-                self.status,
-            )
-        for file in files:
-            # make sure we're not duplicating things in the dataset
-            if file.name in [x[0] for x in data]:
-                continue
-            data.append([file.name, None, None])
-
-        mydataset = pd.DataFrame(data, columns=["filepath", "citation string", "key"])
-        validation_button = self.validate_dataset(mydataset)
-
-        return mydataset, validation_button
-
-    def validate_dataset(self, dataset):
-        self.docs_ready = dataset.iloc[-1, 0] != ""
-        self.dataset = dataset
-
-        self.check_status()
-
-        if self.status == "✨Ready✨":
-            self.get_index()
-
-        return self.status
-
-    def get_index(self):
-        if self.docs_ready and self.valid_key:
-            # os.environ["OPENAI_API_KEY"] = self.openai_api_key
-
-            # myfile = "Angela Merkel - Wikipedia.pdf"
-            # loader = PyPDFLoader(file_path=myfile)
-            loaders = [PyPDFLoader(f) for f in self.dataset["filepath"]]
-
-            self.index = VectorstoreIndexCreator(
-                vectorstore_cls=DocArrayInMemorySearch, 
-                embedding=self.embedding,
-                text_splitter = RecursiveCharacterTextSplitter(
-                            # Set a really small chunk size, just to show.
-                            chunk_size = 1000,
-                            chunk_overlap  = 20,
-                            length_function = len,
-                            separators="."
-            )
-
-            ).from_loaders(loaders=loaders)
-
-            # del os.environ["OPENAI_API_KEY"]
-
-        pass
-
-    def do_ask(self, question):
-        # os.environ["OPENAI_API_KEY"] = self.openai_api_key
-        # openai.api_key = self.openai_api_key
-
-        if self.status == "✨Ready✨":
-            # os.environ["OPENAI_API_KEY"] = self.openai_api_key
-
-            response = self.index.query(question=question, llm=self.llm)
-            # del os.environ["OPENAI_API_KEY"]
-            yield response
-        pass
-
-
-def validate_key(myInstance: myClass, openai_api_key):
-    if myInstance is None:
-        myInstance = myClass()
-
-    out = myInstance.validate_key(openai_api_key)
-    return myInstance, *out
-
-
-def request_pathname(myInstance: myClass, files, data):
-    if myInstance is None:
-        myInstance = myClass()
-    out = myInstance.request_pathname(files, data)
-    return myInstance, *out
-
-
-def do_ask(myInstance: myClass, question):
-    out = myInstance.do_ask(question)
-    return myInstance, *out
-
-
-with gr.Blocks(css=css_style) as demo:
-    myInstance = gr.State()
-    openai_api_key = gr.State("")
-    docs = gr.State()
-    data = gr.State([])
-    index = gr.State()
-
-    gr.Markdown(
-        """
-    # Document Question and Answer
-    *By D8a.ai*
-    Idea based on https://huggingface.co/spaces/whitead/paper-qa
-    Significant advances in langchain have made it possible to simplify the code.
-    This tool allows you to ask questions of your uploaded text, PDF documents.
-    It uses OpenAI's GPT models, so you need to enter your API key below. This
-    tool is under active development and currently uses a lot of tokens - up to 10,000
-    for a single query. This is $0.10-0.20 per query, so please be careful!
-    * [langchain](https://github.com/hwchase17/langchain) is the main library this tool utilizes.
-    1. Enter API Key ([What is that?](https://platform.openai.com/account/api-keys))
-    2. Upload your documents
-    3. Ask questions
-    """
-    )
-
-    openai_api_key = gr.Textbox(
-        label="OpenAI API Key", placeholder="sk-...", type="password"
-    )
-    with gr.Tab("File upload"):
-        uploaded_files = gr.File(
-            label="Upload your pdf Dokument", file_count="multiple"
-        )
-
-    with gr.Accordion("See Docs:", open=False):
-        dataset = gr.Dataframe(
-            headers=["filepath", "citation string", "key"],
-            datatype=["str", "str", "str"],
-            col_count=(3, "fixed"),
-            interactive=False,
-            label="Documents and Citations",
-            overflow_row_behaviour="paginate",
-            max_rows=5,
-        )
-
-    buildb = gr.Textbox(
-        "⚠️Waiting for documents and key...",
-        label="Status",
-        interactive=False,
-        show_label=True,
-        max_lines=1,
-    )
-
-    query = gr.Textbox(placeholder="Enter your question here...", label="Question")
-    ask = gr.Button("Ask Question")
-    answer = gr.Markdown(label="Answer")
-
-    openai_api_key.change(
-        validate_key, inputs=[myInstance, openai_api_key], outputs=[myInstance, buildb]
-    )
-
-    uploaded_files.change(
-        request_pathname,
-        inputs=[myInstance, uploaded_files, data],
-        outputs=[myInstance, dataset, buildb],
-    )
-
-    ask.click(
-        do_ask,
-        inputs=[myInstance, query],
-        outputs=[myInstance, answer],
-    )
 
+def greet(name):
+    return "Hello " + name + "!!"
 
-demo.queue(concurrency_count=20)
-demo.launch(show_error=True)
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()