appvector.py

import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple
import fitz  # PyMuPDF

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")

# Placeholder for the app's state
class MyApp:
    def __init__(self) -> None:
        self.documents = []
        self.load_pdf("THEDIA1.pdf")

    def load_pdf(self, file_path: str) -> None:
        """Extracts text from a PDF file and stores it in the app's documents."""
        doc = fitz.open(file_path)
        self.documents = []
        for page_num in range(len(doc)):
            page = doc[page_num]
            text = page.get_text()
            self.documents.append({"page": page_num + 1, "content": text})
        print("PDF processed successfully!")

    def search_documents(self, query: str, k: int = 3) -> List[str]:
        """Searches for relevant documents containing the query string."""
        results = [doc["content"] for doc in self.documents if query.lower() in doc["content"].lower()]
        return results[:k] if results else ["No relevant documents found."]

app = MyApp()

def respond(
    message: str,
    history: List[Tuple[str, str]],
    system_message: str,
    max_tokens: int,
    temperature: float,
    top_p: float,
):
    system_message = "You are a knowledgeable DBT coach. Use relevant documents to guide users through DBT exercises and provide helpful information."
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    # RAG - Retrieve relevant documents
    retrieved_docs = app.search_documents(message)
    context = "\n".join(retrieved_docs)
    messages.append({"role": "system", "content": "Relevant documents: " + context})

    response = ""
    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content
        response += token
        yield response

demo = gr.Blocks()

with 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."
    )
    
    chatbot = gr.ChatInterface(
        respond,
        additional_inputs=[
            gr.Textbox(value="You are a knowledgeable DBT coach. Use relevant documents to guide users through DBT exercises and provide helpful information.", label="System message"),
            gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
            gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
            gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
        ],
        examples=[
            ["I feel overwhelmed with work."],
            ["Can you guide me through a quick meditation?"],
            ["How do I stop worrying about things I can't control?"],
            ["What are some DBT skills for managing anxiety?"],
            ["Can you explain mindfulness in DBT?"],
            ["What is radical acceptance?"]
        ],
        title='DBT Coach 🧘‍♀️'
    )

if __name__ == "__main__":
    demo.launch()