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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 = []
return None
# Define the available models
models = [key for key in model_links.keys()]
# Sidebar for model selection
selected_model = st.sidebar.selectbox("Select Model", models)
# Temperature slider with default adjusted for labeling consistency
temp_values = st.sidebar.slider('Select a temperature value', 0.1, 1.0, 0.3)
# 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}", key=f"few_shot_{i}"), "label": st.selectbox(f"Label for example {i + 1}", labels, key=f"label_{i}")}
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=100, value=10)
# User prompt text field
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"):
# Generate examples by concatenating all inputs and sending it to the model
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 = ""
for chunk in stream:
response += chunk['choices'][0]['delta']['content']
st.session_state.messages.append({"role": "assistant", "content": response})
st.markdown(response)
except Exception as e:
st.write("Error during generation. Please try again.")
st.write(e)
else:
# Data labeling workflow
st.write("Data Labeling functionality")
# Initialize session state variables for classification
if "labels" not in st.session_state:
st.session_state.labels = []
if "few_shot_examples" not in st.session_state:
st.session_state.few_shot_examples = []
if "examples_to_classify" not in st.session_state:
st.session_state.examples_to_classify = []
# Step 1: Classification Type Selection
classification_type = st.selectbox("Choose Classification Type", ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"])
# Step 2: Define Labels based on Classification Type
if classification_type == "Sentiment Analysis":
labels = ["Positive", "Negative", "Neutral"]
st.write("Sentiment Analysis 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")
if label_1 and label_2:
labels = [label_1, label_2]
else:
labels = []
elif classification_type is "Multi-Class Classification":
num_classes = st.slider("How many classes?", 3, 10, 3)
labels = [st.text_input(f"Class {i + 1}", key=f"multi_class_{i}") for i in range(num_classes)]
# Save labels to session state
st.session_state.labels = labels
# Step 3: Few-Shot Examples
use_few_shot = st.radio("Do you want to use few-shot examples?", ["Yes", "No"])
if use_few_shot == "Yes":
num_examples = st.slider("How many few-shot examples?", 1, 5, 1)
st.session_state.few_shot_examples |