app6.py

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

os.environ["BROWSER_GATHERUSAGESTATS"] = "false"
load_dotenv()

# Initialize the client
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1",
    api_key=os.environ.get('TOKEN2')  # 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 = []

# Define the available models
models = [key for key in model_links.keys()]

# Sidebar for model selection
selected_model = st.sidebar.selectbox("Select Model", models)
temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, 0.5)
st.sidebar.button('Reset Chat', on_click=reset_conversation)
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 = []

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 = []

    num_to_generate = st.number_input("How many examples to generate?", min_value=1, max_value=100, value=10)
    user_prompt = st.text_area("Enter your prompt to guide example generation", "")

    # System prompt generation
    system_prompt = f"You are a professional {classification_type.lower()} expert. Your role is to generate data for {domain}.\n\n"
    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']} \n Label: {example['label']}\n"
    system_prompt += f"Generate {num_to_generate} unique examples with diverse phrasing.\n"
    system_prompt += f"Each example should have between {min_words} and {max_words} words.\n"
    system_prompt += f"Use the labels specified: {', '.join(labels)}.\n"
    if user_prompt:
        system_prompt += f"Additional instructions: {user_prompt}\n"

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

    if st.button("Generate Examples"):
        with st.spinner("Generating..."):
            st.session_state.messages.append({"role": "system", "content": system_prompt})

            try:
                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)
            except Exception as e:
                st.error("An error occurred during generation.")
                st.error(f"Error details: {str(e)}")

            st.session_state.messages.append({"role": "assistant", "content": response})

else:  # Data Labeling Process
    labeling_classification_type = st.selectbox(
        "Choose Classification Type for Labeling",
        ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
    )

    if labeling_classification_type == "Sentiment Analysis":
        st.write("Sentiment Analysis: Positive, Negative, Neutral")
        labeling_labels = ["Positive", "Negative", "Neutral"]
    elif labeling_classification_type == "Binary Classification":
        labeling_label_1 = st.text_input("Enter first class for labeling")
        labeling_label_2 = st.text_input("Enter second class for labeling")
        labeling_labels = [labeling_label_1, labeling_label_2]
    elif labeling_classification_type == "Multi-Class Classification":
        labeling_num_classes = st.slider("How many classes for labeling?", 3, 10, 3)
        labeling_labels = [st.text_input(f"Labeling Class {i+1}") for i in range(labeling_num_classes)]

    labeling_few_shot = st.radio("Do you want to add few-shot examples for labeling?", ["Yes", "No"])
    if labeling_few_shot == "Yes":
        labeling_num_examples = st.slider("How many few-shot examples for labeling?", 1, 5, 1)
        labeling_few_shot_examples = [
            {"content": st.text_area(f"Labeling Example {i+1}"), 
             "label": st.selectbox(f"Label for labeling example {i+1}", labeling_labels)}
            for i in range(labeling_num_examples)
        ]
    else:
        labeling_few_shot_examples = []

    text_to_classify = st.text_area("Enter text to classify")

    if st.button("Classify Text"):
        if text_to_classify:
            labeling_system_prompt = (
                f"You are a professional {labeling_classification_type.lower()} expert. "
                f"Classify the following text using these labels: {', '.join(labeling_labels)}.\n\n"
            )
            if labeling_few_shot_examples:
                labeling_system_prompt += "Here are some example classifications:\n"
                for example in labeling_few_shot_examples:
                    labeling_system_prompt += f"Review: {example['content']}\nLabel: {example['label']}\n\n"

            labeling_system_prompt += (
                f"Text to classify: {text_to_classify}\n"
                f"Provide only the classification result in this format:\n"
                f"'Review: [text provided] Label: [appropriate label]'\n"
            )

            with st.spinner("Classifying..."):
                st.session_state.messages.append({"role": "system", "content": labeling_system_prompt})

                try:
                    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=1000,
                    )
                    response = st.write_stream(stream)
                except Exception as e:
                    st.error("An error occurred during classification.")
                    st.error(f"Error details: {str(e)}")

                st.session_state.messages.append({"role": "assistant", "content": response})
        else:
            st.warning("Please enter text to classify.")