Delete rag_pre_trained.py

rag_pre_trained.py

@@ -1,153 +0,0 @@
-
-from typing import Any
-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, text: str):
-    if not text:
-        raise gr.Error("Enter text")
-    history = history + [(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.N: int = 0
-        self.count: int = 0
-
-    def __call__(self, file: str) -> Any:
-        if self.count == 0:
-            self.chain = self.build_chain(file)
-            self.count += 1
-        return self.chain
-
-    def process_file(self, file: str):
-        loader = PyMuPDFLoader(file.name)
-        documents = loader.load()
-        pattern = r"/([^/]+)$"
-        match = re.search(pattern, file.name)
-        try:
-            file_name = match.group(1)
-        except:
-            file_name = os.path.basename(file)
-
-        return documents, file_name
-
-    def build_chain(self, file: str):
-        documents, file_name = self.process_file(file)
-        # Load embeddings model
-        embeddings = OpenAIEmbeddings(openai_api_key=self.OPENAI_API_KEY)
-        pdfsearch = Chroma.from_documents(
-            documents,
-            embeddings,
-            collection_name=file_name,
-        )
-        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 chain
-
-
-def get_response(history, query, file):
-    if not file:
-        raise gr.Error(message="Upload a PDF")
-    chain = app(file)
-    result = chain(
-        {"question": query, "chat_history": app.chat_history}, return_only_outputs=True
-    )
-    app.chat_history += [(query, result["answer"])]
-    app.N = list(result["source_documents"][0])[1][1]["page"]
-    for char in result["answer"]:
-        history[-1][-1] += char
-        yield history, ""
-
-
-def render_file(file):
-    doc = fitz.open(file.name)
-    page = doc[app.N]
-    # Render the page as a PNG image with a resolution of 150 DPI
-    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):
-    print("purge_chat_and_render_first")
-    # Purges the previous chat session so that the bot has no concept of previous documents
-    app.chat_history = []
-    app.count = 0
-
-    # Use PyMuPDF to render the first page of the uploaded document
-    doc = fitz.open(file.name)
-    page = doc[0]
-    # Render the page as a PNG image with a resolution of 150 DPI
-    pix = page.get_pixmap(dpi=150)
-    image = Image.frombytes("RGB", [pix.width, pix.height], pix.samples)
-    return image, []
-
-
-app = MyApp()
-
-with gr.Blocks() as demo:
-    with gr.Column():
-        with gr.Row():
-            with gr.Column(scale=2):
-                with gr.Row():
-                    chatbot = gr.Chatbot(value=[], elem_id="chatbot")
-                with gr.Row():
-                    txt = gr.Textbox(
-                        show_label=False,
-                        placeholder="Enter text and press submit",
-                        scale=2
-                    )
-                    submit_btn = gr.Button("Submit", scale=1)
-
-            with gr.Column(scale=1):
-                with gr.Row():
-                    show_img = gr.Image(label="Upload PDF")
-                with gr.Row():
-                    btn = gr.UploadButton("📁 Upload a PDF", file_types=[".pdf"])
-
-    btn.upload(
-        fn=purge_chat_and_render_first,
-        inputs=[btn],
-        outputs=[show_img, chatbot],
-    )
-
-    submit_btn.click(
-        fn=add_text,
-        inputs=[chatbot, txt],
-        outputs=[
-            chatbot,
-        ],
-        queue=False,
-    ).success(
-        fn=get_response, inputs=[chatbot, txt, btn], outputs=[chatbot, txt]
-    ).success(
-        fn=render_file, inputs=[btn], outputs=[show_img]
-    )
-
-demo.queue()
-demo.launch()
-
-