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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()
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