app.py

import gradio as gr
from huggingface_hub import InferenceClient
from typing import List, Tuple
import fitz  # PyMuPDF
from sentence_transformers import SentenceTransformer
import numpy as np
import faiss

client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
#client = InferenceClient("meta-llama/Llama-2-7b-chat-hf")

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

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

    def build_vector_db(self) -> None:
        """Builds a vector database using the content of the PDFs."""
        model = SentenceTransformer('all-MiniLM-L6-v2')
        self.embeddings = model.encode([doc["content"] for doc in self.documents], show_progress_bar=True)
        self.index = faiss.IndexFlatL2(self.embeddings.shape[1])
        self.index.add(np.array(self.embeddings))
        print("Vector database built successfully!")

    def search_documents(self, query: str, k: int = 3) -> List[str]:
        """Searches for relevant documents using vector similarity."""
        model = SentenceTransformer('all-MiniLM-L6-v2')
        query_embedding = model.encode([query], show_progress_bar=False)
        D, I = self.index.search(np.array(query_embedding), k)
        results = [self.documents[i]["content"] for i in I[0]]
        return results if results else ["No relevant documents found."]

app = MyApp()

def preprocess_response(response: str) -> str:
    """Preprocesses the response to make it more polished and empathetic."""
    response = response.strip()
    response = response.replace("\n\n", "\n")
    response = response.replace(" ,", ",")
    response = response.replace(" .", ".")
    response = " ".join(response.split())
    if not any(word in response.lower() for word in ["sorry", "apologize", "empathy"]):
        response = "I'm here to help. " + response
    return response

def shorten_response(response: str) -> str:
    """Uses the Zephyr model to shorten and refine the response."""
    messages = [{"role": "system", "content": "Greet, Shorten and refine this response in a supportive and empathetic manner."}, {"role": "user", "content": response}]
    result = client.chat_completion(messages, max_tokens=512, temperature=0.5, top_p=0.9)
    return result.choices[0].message['content'].strip()

def respond(message: str, history: List[Tuple[str, str]], system_message: str):
    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 if the query suggests exercises or specific information
    if any(keyword in message.lower() for keyword in ["exercise", "technique", "information", "guide", "help", "how to"]):
        retrieved_docs = app.search_documents(message)
        context = "\n".join(retrieved_docs)
        if context.strip():
            messages.append({"role": "system", "content": "Relevant documents: " + context})

    response = client.chat_completion(messages, max_tokens=1024, temperature=0.7, top_p=0.9)
    response_content = "".join([choice.message['content'] for choice in response.choices if 'content' in choice.message])

    polished_response = preprocess_response(response_content)
    shortened_response = shorten_response(polished_response)

    history.append((message, shortened_response))
    return history, ""

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."
    )

    chatbot = gr.Chatbot()

    with gr.Row():
        pdf_input = gr.File(label="Upload PDFs (minimum 5)", file_count="multiple", type="file")
        txt_input = gr.Textbox(
            show_label=False,
            placeholder="Type your message here...",
            lines=1
        )
        system_input = gr.Textbox(
            label="System Message",
            placeholder="Enter a system message here...",
            lines=2
        )
        submit_btn = gr.Button("Submit", scale=1)
        refresh_btn = gr.Button("Refresh Chat", scale=1, variant="secondary")

    example_questions = [
        ["What are some ways to cope with stress using DBT?"],
        ["Can you guide me through a grounding exercise?"],
        ["How do I use DBT skills to handle intense emotions?"],
        ["What are some self-soothing techniques I can practice?"]
    ]

    gr.Examples(examples=example_questions, inputs=[txt_input])

    def load_and_build_pdfs(pdfs):
        file_paths = [pdf.name for pdf in pdfs]
        if len(file_paths) < 5:
            return [], "Please upload at least 5 PDFs."
        app.load_pdfs(file_paths)
        app.build_vector_db()
        return []

    submit_btn.click(fn=load_and_build_pdfs, inputs=[pdf_input], outputs=[])

    submit_btn.click(fn=respond, inputs=[txt_input, chatbot, system_input], outputs=[chatbot, txt_input])
    refresh_btn.click(lambda: [], None, chatbot)

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