app.py

import os
from openai import OpenAI
from langchain_community.embeddings import HuggingFaceEmbeddings
from datasets import load_dataset, Dataset
from sklearn.neighbors import NearestNeighbors
import numpy as np
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, TextStreamer
import torch
from typing import List
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
import gradio as gr
from huggingface_hub import InferenceClient

# Configuration

# Sample questions:
# 1. What are the key components of an effective persona for prompt generation?
# 2. How can I create a persona that generates creative writing prompts?
# 3. What strategies can I use to make my persona-driven prompts more engaging?

DEFAULT_QUESTION = "Ask me anything in the context of persona-driven prompt generation..."

# Set API keys (make sure these are set in your environment)
os.environ['OPENAI_BASE'] = "https://api.openai.com/v1"
os.environ['OPENAI_MODEL'] = "gpt-4"
os.environ['MODEL_PROVIDER'] = "huggingface"
model_provider = os.environ.get("MODEL_PROVIDER")

# Instantiate the client for openai v1.x
if model_provider.lower() == "openai":
    MODEL_NAME = os.environ['OPENAI_MODEL']
    client = OpenAI(
        base_url=os.environ.get("OPENAI_BASE"),
        api_key=api_key
    )
else:
    MODEL_NAME = "meta-llama/Llama-3.3-70B-Instruct"
    # Initialize Hugging Face InferenceClient
    hf_client = InferenceClient(
        model=MODEL_NAME,
        api_key=os.environ.get("HF_TOKEN")
    )

# Load the Hugging Face dataset
dataset = load_dataset('tosin2013/persona-driven-prompt-generator', streaming=True)
dataset = Dataset.from_list(list(dataset['train']))

# Initialize embeddings
embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

# Extract texts from the dataset
texts = dataset['input']

# Create embeddings for the texts
text_embeddings = embeddings.embed_documents(texts)

# Fit a nearest neighbor model
nn = NearestNeighbors(n_neighbors=5, metric='cosine')
nn.fit(np.array(text_embeddings))

def get_relevant_documents(query, k=5):
    """
    Retrieves the k most relevant documents to the query.
    """
    query_embedding = embeddings.embed_query(query)
    distances, indices = nn.kneighbors([query_embedding], n_neighbors=k)
    relevant_docs = [texts[i] for i in indices[0]]
    return relevant_docs

def generate_response(question, history):
    try:
        print(f"\n[LOG] Received question: {question}")
        
        # Get relevant documents based on the query
        relevant_docs = get_relevant_documents(question, k=3)
        print(f"[LOG] Retrieved {len(relevant_docs)} relevant documents")
        
        # Create the prompt for the LLM
        context = "\n".join(relevant_docs)
        prompt = f"Context: {context}\n\nQuestion: {question}\n\nAnswer:"
        print(f"[LOG] Generated prompt: {prompt[:200]}...")  # Log first 200 chars of prompt
        
        if model_provider.lower() == "huggingface":
            messages = [
                {
                    "role": "system",
                    "content": "You are a helpful AI assistant. Answer the question based on the provided context."
                },
                {
                    "role": "user",
                    "content": prompt
                }
            ]
            
            completion = hf_client.chat.completions.create(
                model=MODEL_NAME,
                messages=messages,
                max_tokens=500
            )
            response = completion.choices[0].message.content
            print(f"[LOG] Using Hugging Face model (serverless): {MODEL_NAME}")
            print(f"[LOG] Hugging Face response: {response[:200]}...")
        
        elif model_provider.lower() == "openai":
            response = client.chat.completions.create(
                model=os.environ.get("OPENAI_MODEL"),
                messages=[
                    {"role": "system", "content": "You are a helpful assistant. Answer the question based on the provided context."},
                    {"role": "user", "content": prompt},
                ]
            )
            response = response.choices[0].message.content
            print(f"[LOG] Using OpenAI model: {os.environ.get('OPENAI_MODEL')}")
            print(f"[LOG] OpenAI response: {response[:200]}...")  # Log first 200 chars of response
        
        # Update chat history with new message pair
        history.append((question, response))
        return history
    except Exception as e:
        error_msg = f"Error generating response: {str(e)}"
        print(f"[ERROR] {error_msg}")
        history.append((question, error_msg))
        return history

# Create Gradio interface
with gr.Blocks() as demo:
    gr.Markdown(f"""
    ## Persona-Driven Prompt Generator QA Agent
    **Current Model:** {MODEL_NAME}
    
    The Custom Prompt Generator is a Python application that leverages Large Language Models (LLMs) and the LiteLLM library to dynamically generate personas, fetch knowledge sources, resolve conflicts, and produce tailored prompts. This application is designed to assist in various software development tasks by providing context-aware prompts based on user input and predefined personas.

    Sample questions:
    1. What are the key components of an effective persona for prompt generation?
    2. How can I create a persona that generates creative writing prompts?
    3. What are the main features of the persona generator?
    
    Related repository: [persona-driven-prompt-generator](https://github.com/tosin2013/persona-driven-prompt-generator)
    """)
    
    with gr.Row():
        chatbot = gr.Chatbot(label="Chat History")
    
    with gr.Row():
        question = gr.Textbox(
            value=DEFAULT_QUESTION,
            label="Your Question",
            placeholder=DEFAULT_QUESTION
        )
    
    with gr.Row():
        submit_btn = gr.Button("Submit")
        clear_btn = gr.Button("Clear")
    
    # Event handlers
    submit_btn.click(
        generate_response,
        inputs=[question, chatbot],
        outputs=[chatbot]
    )
    
    clear_btn.click(
        lambda: (None, ""),
        inputs=[],
        outputs=[chatbot, question]
    )

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