app.py

""" Simple Chatbot
@author: Nigel Gebodh
@email: [email protected]

"""
import numpy as np
import streamlit as st
from openai import OpenAI
import os
from dotenv import load_dotenv

load_dotenv()

# Initialize the client
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1",
    api_key=os.environ.get('HUGGINGFACEHUB_API_TOKEN')  # Add your Huggingface token here
)

# Supported models
model_links = {
    "Meta-Llama-3-8B": "meta-llama/Meta-Llama-3-8B-Instruct"
}

# Reset conversation
def reset_conversation():
    st.session_state.conversation = []
    st.session_state.messages = []
    return None

# Sidebar for model selection
selected_model = st.sidebar.selectbox("Select Model", list(model_links.keys()))

# Temperature slider
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)

# Reset button
st.sidebar.button('Reset Chat', on_click=reset_conversation)

# Model description
st.sidebar.write(f"You're now chatting with **{selected_model}**")
st.sidebar.markdown("*Generated content may be inaccurate or false.*")

# Chat initialization
if "messages" not in st.session_state:
    st.session_state.messages = []

# Display chat messages
for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        st.markdown(message["content"])

# Main logic to choose between data generation and data labeling
task_choice = st.selectbox("Choose Task", ["Data Generation", "Data Labeling"])

if task_choice == "Data Generation":
    classification_type = st.selectbox(
        "Choose Classification Type",
        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
    )

    if classification_type == "Sentiment Analysis":
        st.write("Sentiment Analysis: Positive, Negative, Neutral")
        labels = ["Positive", "Negative", "Neutral"]
    elif classification_type == "Binary Classification":
        label_1 = st.text_input("Enter first class")
        label_2 = st.text_input("Enter second class")
        labels = [label_1, label_2]
    elif classification_type == "Multi-Class Classification":
        num_classes = st.slider("How many classes?", 3, 10, 3)
        labels = [st.text_input(f"Class {i+1}") for i in range(num_classes)]

    domain = st.selectbox("Choose Domain", ["Restaurant reviews", "E-commerce reviews", "Custom"])
    if domain == "Custom":
        domain = st.text_input("Specify custom domain")

    min_words = st.number_input("Minimum words per example", min_value=10, max_value=90, value=10)
    max_words = st.number_input("Maximum words per example", min_value=10, max_value=90, value=90)

    few_shot = st.radio("Do you want to use few-shot examples?", ["Yes", "No"])
    if few_shot == "Yes":
        num_examples = st.slider("How many few-shot examples?", 1, 5, 1)
        few_shot_examples = [
            {"content": st.text_area(f"Example {i+1}"), "label": st.selectbox(f"Label for example {i+1}", labels)}
            for i in range(num_examples)
        ]
    else:
        few_shot_examples = []

    # Ask the user how many examples they need
    num_to_generate = st.number_input("How many examples to generate?", min_value=1, max_value=50, value=10)

    # System prompt generation
    system_prompt = f"You are a professional {classification_type.lower()} expert. Your role is to generate {num_to_generate} data examples for {domain}. "
    system_prompt += f"Each example should have a label and consist of between {min_words} and {max_words} words. "
    system_prompt += "Use the following labels: " + ", ".join(labels) + ". "

    if few_shot_examples:
        system_prompt += "Use the following few-shot examples as a reference:\n"
        for example in few_shot_examples:
            system_prompt += f"Example: {example['content']}, Label: {example['label']}\n"
    
    system_prompt += "Please only provide the examples in the following format:\n"
    system_prompt += "Example: <text>, Label: <label>\n"

    st.write("System Prompt:")
    st.code(system_prompt)

    if st.button("Generate Examples"):
        all_generated_examples = []
        remaining_examples = num_to_generate

        with st.spinner("Generating..."):
            while remaining_examples > 0:
                chunk_size = min(remaining_examples, 5)
                try:
                    st.session_state.messages.append({"role": "system", "content": system_prompt})

                    stream = client.chat.completions.create(
                        model=model_links[selected_model],
                        messages=[
                            {"role": m["role"], "content": m["content"]}
                            for m in st.session_state.messages
                        ],
                        temperature=temp_values,
                        stream=True,
                        max_tokens=3000,
                    )

                    response = st.write_stream(stream)

                    # Split the response into individual examples, assuming each example starts with 'Example: '
                    generated_examples = response.split("Example: ")[1:chunk_size+1]  # Extract up to the chunk size

                    # Store the new examples
                    all_generated_examples.extend(generated_examples)
                    remaining_examples -= chunk_size

                except Exception as e:
                    st.error("Error during generation.")
                    st.write(e)
                    break

        # Display all generated examples
        for idx, example in enumerate(all_generated_examples):
            st.write(f"Example {idx+1}: {example.strip()}")

        # Update session state to prevent repetition of old prompts
        st.session_state.messages = []  # Clear messages after each generation

else:
    # Data labeling workflow (for future implementation based on classification)
    st.write("Data Labeling functionality will go here.")