Delete Tryapp.py

Tryapp.py

@@ -1,173 +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 openai
-
-# # MyApp class to handle the processes
-# class MyApp:
-#     def __init__(self) -> None:
-#         self.OPENAI_API_KEY: str = None  # Initialize with None
-#         self.chain = None
-#         self.chat_history: list = []
-#         self.documents = None
-#         self.file_name = None
-
-#     def set_api_key(self, api_key: str):
-#         self.OPENAI_API_KEY = api_key
-#         openai.api_key = api_key
-
-#     def process_file(self, file) -> Image.Image:
-#         loader = PyMuPDFLoader(file.name)
-#         self.documents = loader.load()
-#         self.file_name = os.path.basename(file.name)
-#         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!"
-
-# # Function to add text to chat history
-# 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
-
-# # Function to get response from the model
-# def get_response(history, query):
-#     if app.chain is None:
-#         raise gr.Error("The chain has not been built yet. Please ensure the vector database is built before querying.")
-    
-#     try:
-#         result = app.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)
-#         history[-1] = (history[-1][0], result["answer"])
-#         return history, source_texts_str
-#     except Exception as e:
-#         app.chat_history.append((query, "I have no information about it. Feed me knowledge, please!"))
-#         return history, f"I have no information about it. Feed me knowledge, please! Error: {str(e)}"
-
-# # Function to refresh chat
-# def refresh_chat():
-#     app.chat_history = []
-#     return []
-
-# app = MyApp()
-
-# # Function to set API key
-# def set_api_key(api_key):
-#     app.set_api_key(api_key)
-#     # Pre-process the saved PDF file after setting the API key
-#     saved_file_path = "THEDIA1.pdf"
-#     with open(saved_file_path, 'rb') as saved_file:
-#         app.process_file(saved_file)
-#         app.build_chain(saved_file)
-#     return f"API Key set to {api_key[:4]}...{api_key[-4:]} and vector database built successfully!"
-
-# # List of determined questions
-# questions = [
-#     "What is the primary goal of Dialectical Behaviour Therapy?",
-#     "How can mindfulness help in managing emotions?",
-#     "What are some techniques to handle distressing situations?",
-#     "Can you explain the concept of radical acceptance?",
-#     "How does DBT differ from other types of therapy?",
-#     "What are the four modules of DBT?",
-#     "How can DBT skills be applied in daily life?",
-#     "What is the importance of emotional regulation in DBT?",
-#     "How does DBT address interpersonal effectiveness?",
-#     "What are some common myths about DBT?",
-#     "How can one practice distress tolerance skills?",
-#     "What role does validation play in DBT?",
-#     "How does DBT incorporate cognitive-behavioral techniques?",
-#     "What are the stages of DBT treatment?",
-#     "How can one use DBT skills to improve self-awareness?"
-# ]
-
-# # Gradio interface
-# with gr.Blocks() as demo:
-#     gr.Markdown("🧘‍♀️ **Dialectical Behaviour Therapy**")
-#     gr.Markdown(
-#         "Disclaimer: This chatbot is based on a DBT exercise book that is publicly available. "
-#         "We are not medical practitioners, and the use of this chatbot is at your own responsibility."
-#     )
-    
-#     api_key_input = gr.Textbox(label="OpenAI API Key", type="password", placeholder="Enter your OpenAI API Key")
-#     api_key_btn = gr.Button("Set API Key")
-#     api_key_status = gr.Textbox(value="API Key status", interactive=False)
-
-#     api_key_btn.click(
-#         fn=set_api_key,
-#         inputs=[api_key_input],
-#         outputs=[api_key_status]
-#     )           
-
-#     chatbot_current = gr.Chatbot(elem_id="chatbot_current")
-#     txt_current = gr.Textbox(
-#         show_label=False,
-#         placeholder="Enter text and press submit",
-#         scale=2
-#     )
-#     submit_btn_current = gr.Button("Submit", scale=1)
-#     refresh_btn_current = gr.Button("Refresh Chat", scale=1)
-#     source_texts_output_current = gr.Textbox(label="Source Texts", interactive=False)
-
-#     submit_btn_current.click(
-#         fn=add_text,
-#         inputs=[chatbot_current, txt_current],
-#         outputs=[chatbot_current],
-#         queue=False,
-#     ).success(
-#         fn=get_response, inputs=[chatbot_current, txt_current], outputs=[chatbot_current, source_texts_output_current]
-#     )
-
-#     refresh_btn_current.click(
-#         fn=refresh_chat,
-#         inputs=[],
-#         outputs=[chatbot_current],
-#     )
-
-#     question_dropdown = gr.Dropdown(
-#         label="Select an example question",
-#         choices=questions
-#     )
-#     question_submit_btn = gr.Button("Submit Question")
-    
-#     question_submit_btn.click(
-#         fn=add_text,
-#         inputs=[chatbot_current, question_dropdown],
-#         outputs=[chatbot_current],
-#         queue=False,
-#     ).success(
-#         fn=get_response, inputs=[chatbot_current, question_dropdown], outputs=[chatbot_current, source_texts_output_current]
-#     )
-
-# demo.queue()
-# demo.launch()