Delete instaragworking.py

instaragworking.py

@@ -1,165 +0,0 @@
-from typing import Any, List, Tuple
-import gradio as gr
-from langchain_openai import OpenAIEmbeddings
-from langchain_community.vectorstores import Chroma
-from langchain.chains import ConversationalRetrievalChain
-from langchain_openai import ChatOpenAI
-from langchain_community.document_loaders import PyMuPDFLoader
-import fitz
-from PIL import Image
-import os
-import re
-import openai
-
-openai.api_key = "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"
-
-def add_text(history: List[Tuple[str, str]], text: str) -> List[Tuple[str, str]]:
-    if not text:
-        raise gr.Error("Enter text")
-    history.append((text, ""))
-    return history
-
-class MyApp:
-    def __init__(self) -> None:
-        self.OPENAI_API_KEY: str = openai.api_key
-        self.chain = None
-        self.chat_history: list = []
-        self.documents = None
-        self.file_name = None
-
-    def __call__(self, file: str) -> ConversationalRetrievalChain:
-        if self.chain is None:
-            self.chain = self.build_chain(file)
-        return self.chain
-
-    def process_file(self, file) -> Image.Image:
-        loader = PyMuPDFLoader(file.name)
-        self.documents = loader.load()
-        pattern = r"/([^/]+)$"
-        match = re.search(pattern, file.name)
-        try:
-            self.file_name = match.group(1)
-        except:
-            self.file_name = os.path.basename(file)
-        doc = fitz.open(file.name)
-        page = doc[0]
-        pix = page.get_pixmap(dpi=150)
-        image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-        return image
-
-    def build_chain(self, file) -> str:
-        embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
-        pdfsearch = Chroma.from_documents(
-            self.documents,
-            embeddings,
-            collection_name=self.file_name,
-        )
-        self.chain = ConversationalRetrievalChain.from_llm(
-            ChatOpenAI(temperature=0.0, openai_api_key=self.OPENAI_API_KEY),
-            retriever=pdfsearch.as_retriever(search_kwargs={"k": 1}),
-            return_source_documents=True,
-        )
-        return "Vector database built successfully!"
-
-def get_response(history, query, file):
-    if not file:
-        raise gr.Error(message="Upload a PDF")
-    chain = app(file)
-    try:
-        result = chain.invoke(
-            {"question": query, "chat_history": app.chat_history}
-        )
-        app.chat_history.append((query, result["answer"]))
-        source_docs = result["source_documents"]
-        source_texts = []
-        for doc in source_docs:
-            source_texts.append(f"Page {doc.metadata['page'] + 1}: {doc.page_content}")
-        source_texts_str = "\n\n".join(source_texts)
-        for char in result["answer"]:
-            history[-1][-1] += char
-        return history, source_texts_str
-    except Exception:
-        app.chat_history.append((query, "I have no information about it. Feed me knowledge, please!"))
-        return history, "I have no information about it. Feed me knowledge, please!"
-
-def render_file(file) -> Image.Image:
-    doc = fitz.open(file.name)
-    page = doc[0]
-    pix = page.get_pixmap(dpi=150)
-    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-    return image
-
-def purge_chat_and_render_first(file) -> Tuple[Image.Image, list]:
-    app.chat_history = []
-    doc = fitz.open(file.name)
-    page = doc[0]
-    pix = page.get_pixmap(dpi=150)
-    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-    return image, []
-
-def refresh_chat():
-    app.chat_history = []
-    return []
-
-app = MyApp()
-
-with gr.Blocks() as demo:
-    with gr.Tab("Step 1: Upload PDF"):
-        btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])
-        show_img = gr.Image(label="Uploaded PDF")
-
-    with gr.Tab("Step 2: Process File"):
-        process_btn = gr.Button("Process PDF")
-        show_img_processed = gr.Image(label="Processed PDF")
-        process_status = gr.Textbox(label="Processing Status", interactive=False)
-
-    with gr.Tab("Step 3: Build Vector Database"):
-        build_vector_btn = gr.Button("Build Vector Database")
-        status_text = gr.Textbox(label="Status", value="", interactive=False)
-
-    with gr.Tab("Step 4: Ask Questions"):
-        chatbot = gr.Chatbot(elem_id="chatbot")
-        txt = gr.Textbox(
-            show_label=False,
-            placeholder="Enter text and press submit",
-            scale=2
-        )
-        submit_btn = gr.Button("Submit", scale=1)
-        refresh_btn = gr.Button("Refresh Chat", scale=1)
-        source_texts_output = gr.Textbox(label="Source Texts", interactive=False)
-
-    btn.upload(
-        fn=purge_chat_and_render_first,
-        inputs=[btn],
-        outputs=[show_img, chatbot],
-    )
-
-    process_btn.click(
-        fn=lambda file: (app.process_file(file), "Processing complete!"),
-        inputs=[btn],
-        outputs=[show_img_processed, process_status],
-    )
-
-    build_vector_btn.click(
-        fn=app.build_chain,
-        inputs=[btn],
-        outputs=[status_text],
-    )
-
-    submit_btn.click(
-        fn=add_text,
-        inputs=[chatbot, txt],
-        outputs=[chatbot],
-        queue=False,
-    ).success(
-        fn=get_response, inputs=[chatbot, txt, btn], outputs=[chatbot, source_texts_output]
-    )
-
-    refresh_btn.click(
-        fn=refresh_chat,
-        inputs=[],
-        outputs=[chatbot],
-    )
-
-demo.queue()
-demo.launch()