Update app.py

app.py

@@ -1,7 +1,218 @@
+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 + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+demo.queue(concurrency_count=20)
+demo.launch(show_error=True)