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