import streamlit as st
import pandas as pd
import os
import json
import base64
import random
from streamlit_pdf_viewer import pdf_viewer
from langchain.prompts import PromptTemplate
from datetime import datetime
from pathlib import Path
from openai import OpenAI
from dotenv import load_dotenv
import warnings

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

warnings.filterwarnings('ignore')

os.getenv("OAUTH_CLIENT_ID")


# Load environment variables and initialize the OpenAI client to use Hugging Face Inference API.
load_dotenv()
client = OpenAI(
    base_url="https://api-inference.huggingface.co/v1",
    api_key=os.environ.get('TOKEN2') # Hugging Face API token
)
##########################################################3
# import streamlit as st
# from transformers import AutoModelForCausalLM, AutoTokenizer
# import torch

# # Model selection dropdown
# selected_model = st.selectbox(
#     "Select Model",
#     ["meta-llama/Meta-Llama-3-8B-Instruct-Turbo", 
#      "meta-llama/Llama-3.3-70B-Instruct", 
#      "meta-llama/Llama-3.2-3B-Instruct",
#      "meta-llama/Llama-4-Scout-17B-16E-Instruct", 
#      "meta-llama/Meta-Llama-3-8B-Instruct",
#      "meta-llama/Llama-3.1-70B-Instruct"],
#     key='model_select'
# )

# @st.cache_resource  # Cache the model to prevent reloading
# def load_model(model_name):
#     try:
#         # Optimized model loading configuration
#         model = AutoModelForCausalLM.from_pretrained(
#             model_name,
#             torch_dtype=torch.float16,        # Use half precision
#             device_map="auto",                # Automatic device mapping
#             load_in_8bit=True,               # Enable 8-bit quantization
#             low_cpu_mem_usage=True,          # Optimize CPU memory usage
#             max_memory={0: "10GB"}           # Limit GPU memory usage
#         )
        
#         tokenizer = AutoTokenizer.from_pretrained(
#             model_name,
#             padding_side="left",
#             truncation_side="left"
#         )
        
#         return model, tokenizer
    
#     except Exception as e:
#         st.error(f"Error loading model: {str(e)}")
#         return None, None

# # Load the selected model with optimizations
# if selected_model:
#     model, tokenizer = load_model(selected_model)

#     # Check if model loaded successfully
#     if model is not None:
#         st.success(f"Successfully loaded {selected_model}")
#     else:
#         st.warning("Please select a different model or check your hardware capabilities")

# # Function to generate text
# def generate_response(prompt, model, tokenizer):
#     try:
#         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
        
#         with torch.no_grad():
#             outputs = model.generate(
#                 inputs["input_ids"],
#                 max_length=256,
#                 num_return_sequences=1,
#                 temperature=0.7,
#                 do_sample=True,
#                 pad_token_id=tokenizer.pad_token_id
#             )
        
#         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
#         return response
    
#     except Exception as e:
#         return f"Error generating response: {str(e)}"
############################################################

####new
# from openai import OpenAI

# client = OpenAI(
#     base_url="https://router.huggingface.co/together/v1",
#     api_key=os.environ.get('TOKEN2'),
# )

# completion = client.chat.completions.create(
#     model="meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
#     messages=[
#         {
#             "role": "user",
#             "content": "What is the capital of France?"
#         }
#     ],
#     max_tokens=512,
# )

# print(completion.choices[0].message)
#####

# Create necessary directories
for dir_name in ['data', 'feedback']:
    if not os.path.exists(dir_name):
        os.makedirs(dir_name)

# Custom CSS
st.markdown("""
<style>
    .stButton > button {
        width: 100%;
        margin-bottom: 10px;
        background-color: #4CAF50;
        color: white;
        border: none;
        padding: 10px;
        border-radius: 5px;
    }
    .task-button {
        background-color: #2196F3 !important;
    }
    .stSelectbox {
        margin-bottom: 20px;
    }
    .output-container {
        padding: 20px;
        border-radius: 5px;
        border: 1px solid #ddd;
        margin: 10px 0;
    }
    .status-container {
        padding: 10px;
        border-radius: 5px;
        margin: 10px 0;
    }
    .sidebar-info {
        padding: 10px;
        background-color: #f0f2f6;
        border-radius: 5px;
        margin: 10px 0;
    }
    .feedback-button {
        background-color: #ff9800 !important;
    }
    .feedback-container {
        padding: 15px;
        background-color: #f5f5f5;
        border-radius: 5px;
        margin: 15px 0;
    }
</style>
""", unsafe_allow_html=True)

# Helper functions
def read_csv_with_encoding(file):
    encodings = ['utf-8', 'latin1', 'iso-8859-1', 'cp1252']
    for encoding in encodings:
        try:
            return pd.read_csv(file, encoding=encoding)
        except UnicodeDecodeError:
            continue
    raise UnicodeDecodeError("Failed to read file with any supported encoding")

#def save_feedback(feedback_data):
    #feedback_file = 'feedback/user_feedback.csv'
    #feedback_df = pd.DataFrame([feedback_data])
    
    #if os.path.exists(feedback_file):
        #feedback_df.to_csv(feedback_file, mode='a', header=False, index=False)
    #else:
        #feedback_df.to_csv(feedback_file, index=False)

def reset_conversation():
    st.session_state.conversation = []
    st.session_state.messages = []
    if 'task_choice' in st.session_state:
        del st.session_state.task_choice
    return None
    #new 24 March
    #user_input = st.text_input("Enter your prompt:")
###########33

# Initialize session state variables
if "messages" not in st.session_state:
    st.session_state.messages = []
if "examples_to_classify" not in st.session_state:
    st.session_state.examples_to_classify = []
if "system_role" not in st.session_state:
    st.session_state.system_role = ""

    

# Main app title
st.title("🤖🦙 Text Data Labeling and Generation App")
# def embed_pdf_sidebar(pdf_path):
#     with open(pdf_path, "rb") as f:
#         base64_pdf = base64.b64encode(f.read()).decode('utf-8')
#     pdf_display = f"""
#         <iframe src="data:application/pdf;base64,{base64_pdf}" 
#         width="100%" height="400" type="application/pdf"></iframe>
#     """
#     st.markdown(pdf_display, unsafe_allow_html=True)
# 
        
    
# Sidebar settings
with st.sidebar:
    st.title("⚙️ Settings")
   

#this last code works
with st.sidebar:
    st.markdown("### 📘Data Generation and Labeling Instructions")
    #st.markdown("<h4 style='color: #4A90E2;'>📘    Instructions</h4>", unsafe_allow_html=True)
    with open("User instructions.pdf", "rb") as f:
        st.download_button(
            label="📄 Download Instructions PDF",
            data=f,
            #file_name="instructions.pdf",
            file_name="User instructions.pdf",
            mime="application/pdf"
        )
   
    selected_model = st.selectbox(
        "Select Model",
        ["meta-llama/Llama-3.2-11B-Vision-Instruct","meta-llama/Meta-Llama-3-8B-Instruct-Turbo", "meta-llama/Llama-3.3-70B-Instruct", "meta-llama/Llama-3.2-3B-Instruct","meta-llama/Llama-4-Scout-17B-16E-Instruct", "meta-llama/Meta-Llama-3-8B-Instruct",
         "meta-llama/Llama-3.1-70B-Instruct"],
        key='model_select'
    )

#################new oooo

# # Model selection dropdown
# selected_model = st.selectbox(
#     "Select Model",
#     [#"meta-llama/Meta-Llama-3-8B-Instruct-Turbo",
#      "meta-llama/Llama-3.2-3B-Instruct",
#      "meta-llama/Llama-3.3-70B-Instruct", 
#      "meta-llama/Llama-3.2-3B-Instruct",
#      "meta-llama/Llama-4-Scout-17B-16E-Instruct", 
#      "meta-llama/Meta-Llama-3-8B-Instruct",
#      "meta-llama/Llama-3.1-70B-Instruct"],
#     key='model_select'
# )

# @st.cache_resource  # Cache the model to prevent reloading
# def load_model(model_name):
#     try:
#         # Optimized model loading configuration
#         model = AutoModelForCausalLM.from_pretrained(
#             model_name,
#             torch_dtype=torch.float16,        # Use half precision
#             device_map="auto",                # Automatic device mapping
#             load_in_8bit=True,               # Enable 8-bit quantization
#             low_cpu_mem_usage=True,          # Optimize CPU memory usage
#             max_memory={0: "10GB"}           # Limit GPU memory usage
#         )
        
#         tokenizer = AutoTokenizer.from_pretrained(
#             model_name,
#             padding_side="left",
#             truncation_side="left"
#         )
        
#         return model, tokenizer
    
#     except Exception as e:
#         st.error(f"Error loading model: {str(e)}")
#         return None, None

# # Load the selected model with optimizations
# if selected_model:
#     model, tokenizer = load_model(selected_model)

#     # Check if model loaded successfully
#     if model is not None:
#         st.success(f"Successfully loaded {selected_model}")
#     else:
#         st.warning("Please select a different model or check your hardware capabilities")

# # Function to generate text
# def generate_response(prompt, model, tokenizer):
#     try:
#         inputs = tokenizer(prompt, return_tensors="pt", truncation=True, max_length=512)
        
#         with torch.no_grad():
#             outputs = model.generate(
#                 inputs["input_ids"],
#                 max_length=256,
#                 num_return_sequences=1,
#                 temperature=0.7,
#                 do_sample=True,
#                 pad_token_id=tokenizer.pad_token_id
#             )
        
#         response = tokenizer.decode(outputs[0], skip_special_tokens=True)
#         return response
    
#     except Exception as e:
#         return f"Error generating response: {str(e)}"
# ################

# model = AutoModelForCausalLM.from_pretrained(
#     "meta-llama/Meta-Llama-3-8B-Instruct",
#     torch_dtype=torch.float16,  # Use half precision
#     device_map="auto",          # Automatic device mapping
#     load_in_8bit=True          # Load in 8-bit precision
# )
    temperature = st.slider(
        "Temperature",
        0.0, 1.0, 0.7,
        help="Controls randomness in generation"
    )
    
    st.button("🔄 New Conversation", on_click=reset_conversation)  
    with st.container():
        st.markdown(f"""
           <div class="sidebar-info">
               <h4>Current Model: {selected_model}</h4>
               <p><em>Note: Generated content may be inaccurate or false. Check important info.</em></p>
           </div>
            """, unsafe_allow_html=True)
    
    feedback_url = "https://docs.google.com/forms/d/e/1FAIpQLSdZ_5mwW-pjqXHgxR0xriyVeRhqdQKgb5c-foXlYAV55Rilsg/viewform?usp=header"
    st.sidebar.markdown(
        f'<a href="{feedback_url}" target="_blank"><button style="width: 100%;">Feedback Form</button></a>',
        unsafe_allow_html=True
    )

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

# Main content
if 'task_choice' not in st.session_state:
    col1, col2 = st.columns(2)
    with col1:
        if st.button("📝 Data Generation", key="gen_button", help="Generate new data"):
            st.session_state.task_choice = "Data Generation"
    with col2:
        if st.button("🏷️ Data Labeling", key="label_button", help="Label existing data"):
            st.session_state.task_choice = "Data Labeling"

if "task_choice" in st.session_state:
    if st.session_state.task_choice == "Data Generation":
        st.header("📝 Data Generation")

       # 1. Domain selection
        domain_selection = st.selectbox("Domain", [
            "Restaurant reviews", "E-Commerce reviews", "News", "AG News", "Tourism", "Custom"
        ])
        
        # 2. Handle custom domain input
        custom_domain_valid = True  # Assume valid until proven otherwise
        
        if domain_selection == "Custom":
            domain = st.text_input("Specify custom domain")
            if not domain.strip():
                st.error("Please specify a domain name.")
                custom_domain_valid = False
        else:
            domain = domain_selection

        # Classification type selection
        classification_type = st.selectbox(
            "Classification Type",
            ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification"]
        )
        # Labels setup based on classification type
        #labels = []
        labels = []
        labels_valid = False
        errors = []

        def validate_binary_labels(labels):
            errors = []
            normalized = [label.strip().lower() for label in labels]
        
            if not labels[0].strip():
                errors.append("First class name is required.")
            if not labels[1].strip():
                errors.append("Second class name is required.")
            if normalized[0] == normalized[1] and all(normalized):
                errors.append("Class names must be different.")
            return errors
        
        if classification_type == "Sentiment Analysis":
            st.write("### Sentiment Analysis Labels (Fixed)")
            col1, col2, col3 = st.columns(3)
            with col1:
                st.text_input("First class", "Positive", disabled=True)
            with col2:
                st.text_input("Second class", "Negative", disabled=True)
            with col3:
                st.text_input("Third class", "Neutral", disabled=True)
            labels = ["Positive", "Negative", "Neutral"]
        
        elif classification_type == "Binary Classification":
            st.write("### Binary Classification Labels")
            col1, col2 = st.columns(2)
            with col1:
                label_1 = st.text_input("First class", "Positive")
            with col2:
                label_2 = st.text_input("Second class", "Negative")
            
            labels = [label_1, label_2]
            errors = validate_binary_labels(labels)
        
            if errors:
                st.error("\n".join(errors))
            else:
                st.success("Binary class names are valid and unique!")

        
        elif classification_type == "Multi-Class Classification":
            st.write("### Multi-Class Classification Labels")
        
            default_labels_by_domain = {
                "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
                "AG News": ["World", "Sports", "Business", "Sci/Tech"],
                "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
                            "Food & Dining", "Local Experience", "Adventure Activities",
                            "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
                            "Luxury Tourism"],
                "Restaurant reviews": ["Italian", "French", "American"],
                "E-Commerce reviews": ["Mobile Phones & Accessories", "Laptops & Computers","Kitchen & Dining",
                                       "Beauty & Personal Care", "Home & Furniture", "Clothing & Fashion",
                                      "Shoes & Handbags", "Health & Wellness", "Electronics & Gadgets",
                                       "Books & Stationery","Toys & Games", "Sports & Fitness",
                                       "Grocery & Gourmet Food","Watches & Accessories", "Baby Products"]
            }
        
            num_classes = st.slider("Number of classes", 3, 15, 3)
        
            # Get defaults for selected domain, or empty list
            defaults = default_labels_by_domain.get(domain, [])
        
            labels = []
            errors = []
            cols = st.columns(3)
        
            for i in range(num_classes):
                with cols[i % 3]:
                    default_value = defaults[i] if i < len(defaults) else ""
                    label_input = st.text_input(f"Class {i+1}", default_value)
                    normalized_label = label_input.strip().title()
        
                    if not normalized_label:
                        errors.append(f"Class {i+1} name is required.")
                    else:
                        labels.append(normalized_label)
        
            # Check for duplicates (case-insensitive)
            if len(labels) != len(set(labels)):
                errors.append("Labels names must be unique (case-insensitive, normalized to Title Case).")

            # Show validation results
            if errors:
                for error in errors:
                    st.error(error)
            else:
                st.success("All Labels names are valid and unique!")
            labels_valid = not errors  # Will be True only if there are no label errors     

                    ##############
    #new 22/4/2025
    # add additional attributes
        add_attributes = st.checkbox("Add additional attributes (optional)")
        additional_attributes = []
        
        if add_attributes:
            num_attributes = st.slider("Number of attributes to add", 1, 5, 1)
            for i in range(num_attributes):
                st.markdown(f"#### Attribute {i+1}")
                attr_name = st.text_input(f"Name of attribute {i+1}", key=f"attr_name_{i}")
                attr_topics = st.text_input(f"Topics (comma-separated) for {attr_name}", key=f"attr_topics_{i}")
                if attr_name and attr_topics:
                    topics_list = [topic.strip() for topic in attr_topics.split(",") if topic.strip()]
                    additional_attributes.append({"attribute": attr_name, "topics": topics_list})

################
        
        # Generation parameters
        col1, col2 = st.columns(2)
        with col1:
            min_words = st.number_input("Min words", 1, 100, 20)
        with col2:
            max_words = st.number_input("Max words", min_words, 100, 50)

        # Few-shot examples
        use_few_shot = st.toggle("Use few-shot examples")
        few_shot_examples = []
        if use_few_shot:
            num_examples = st.slider("Number of few-shot examples", 1, 10, 1)
            for i in range(num_examples):
                with st.expander(f"Example {i+1}"):
                    content = st.text_area(f"Content", key=f"few_shot_content_{i}")
                    label = st.selectbox(f"Label", labels, key=f"few_shot_label_{i}")
                    if content and label:
                        few_shot_examples.append({"content": content, "label": label})

        num_to_generate = st.number_input("Number of examples", 1, 100, 10)
        #sytem role after
         # System role customization
        #default_system_role = f"You are a professional {classification_type} expert, your role is to generate text examples for {domain} domain. Always generate unique diverse examples and do not repeat the generated data. The generated text should be between {min_words} to {max_words} words long."
        # System role customization
        default_system_role = (
    f"You are a seasoned expert in {classification_type}, specializing in the {domain} domain. "
    f" Your primary responsibility is to generate high-quality, diverse, and unique text examples "
    f"tailored to this domain. Please ensure that each example adheres to the specified length "
    f"requirements, ranging from {min_words} to {max_words} words, and avoid any repetition in the generated content."
)
        system_role = st.text_area("Modify System Role (optional)", 
                                value=default_system_role,
                                key="system_role_input")
        st.session_state['system_role'] = system_role if system_role else default_system_role
        # Labels initialization
        #labels = []
        
        
        user_prompt = st.text_area("User Prompt (optional)")

        # Updated prompt template including system role
        prompt_template = PromptTemplate(
            input_variables=["system_role", "classification_type", "domain", "num_examples", 
                           "min_words", "max_words", "labels", "user_prompt", "few_shot_examples", "additional_attributes"],
            template=(
               "{system_role}\n"
                 "- Use the following parameters:\n"
                "- Generate {num_examples} examples\n"
                "- Each example should be between {min_words} to {max_words} words long\n"
                "- Use these labels: {labels}.\n"
                "- Use the following additional attributes:\n"
                "- {additional_attributes}\n"
                "- Generate the examples in this format: 'Example text. Label: label'\n"
                "- Do not include word counts or any additional information\n"
                "- Always use your creativity and intelligence to generate unique and diverse text data\n"
                "- In sentiment analysis, ensure that the sentiment classification is clearly identified as Positive, Negative, or Neutral. Do not leave the sentiment ambiguous.\n"
                "- In binary sentiment analysis, classify text strictly as either Positive or Negative. Do not include or imply Neutral as an option.\n"
                "- Write unique examples every time.\n"
                "- DO NOT REPEAT your gnerated text. \n"
                "- For each Output, describe it once and move to the next.\n"
                "- List each Output only once, and avoid repeating details.\n"
                "- Additional instructions: {user_prompt}\n\n"
                "- Use the following examples as a reference in the generation process\n\n {few_shot_examples}. \n"
                "- Think step by step, generate numbered examples, and check each newly generated example to ensure it has not been generated before. If it has, modify it"
                
             )
         )
        #    template=(
        #         "{system_role}\n"
        #         "- Use the following parameters:\n"
        #         "- Generate {num_examples} examples\n"
        #         "- Each example should be between {min_words} to {max_words} words long\n"
        #         "- Use these labels: {labels}.\n"
        #         "- Use the following additional attributes:\n"
        #         "{additional_attributes}\n"
        #         #"- Format each example like this: 'Example text. Label: [label]. Attribute1: [topic1]. Attribute2: [topic2]'\n"
        #         "- Generate the examples in this format: 'Example text. Label: label'\n"
        #         "- Additional instructions: {user_prompt}\n"
        #         "- Use these few-shot examples if provided:\n{few_shot_examples}\n"
        #         "- Think step by step and ensure examples are unique and not repeated."
        #     )
        # )
        ##########new 22/4/2025
        formatted_attributes = "\n".join([
            f"- {attr['attribute']}: {', '.join(attr['topics'])}" for attr in additional_attributes
        ])
#######################
        
        # Generate system prompt
        system_prompt = prompt_template.format(
            system_role=st.session_state['system_role'],
            classification_type=classification_type,
            domain=domain,
            num_examples=num_to_generate,
            min_words=min_words,
            max_words=max_words,
            labels=", ".join(labels),
            user_prompt=user_prompt,
            few_shot_examples="\n".join([f"{ex['content']}\nLabel: {ex['label']}" for ex in few_shot_examples]) if few_shot_examples else "",
            additional_attributes=formatted_attributes
        )
        

        # Store system prompt in session state
        st.session_state['system_prompt'] = system_prompt

        # Display system prompt
        st.write("System Prompt:")
        st.text_area("Current System Prompt", value=st.session_state['system_prompt'], 
                    height=400, disabled=True)
        

        if st.button("🎯 Generate Examples"):
            #
            errors = []
            if domain_selection == "Custom" and not domain.strip():
                st.warning("Custom domain name is required.")
            elif len(labels) != len(set(labels)):
                st.warning("Class names must be unique.")
            elif any(not lbl.strip() for lbl in labels):
                st.warning("All class labels must be filled in.")
            #else:
                #st.success("Generating examples for domain: {domain}")
                    
            #if not custom_domain_valid:
                #st.warning("Custom domain name is required.")
            #elif not labels_valid:
                #st.warning("Please fix the label errors before generating examples.")
            #else:
                # Proceed to generate examples
                #st.success(f"Generating examples for domain: {domain}")
                
            with st.spinner("Generating examples..."):
                try:
                    stream = client.chat.completions.create(
                        model=selected_model,
                        messages=[{"role": "system", "content": st.session_state['system_prompt']}],
                        temperature=temperature,
                        stream=True,
                        #max_tokens=80000,
                        max_tokens=4000,
                        top_p=0.9,
                       # repetition_penalty=1.2,
                        #frequency_penalty=0.5,      # Discourages frequent words
                        #presence_penalty=0.6,  
                    )
 #st.session_state['system_prompt'] = system_prompt
                    #new 24 march
                    st.session_state.messages.append({"role": "user", "content": system_prompt})
                 # # ####################
                    response = st.write_stream(stream)
                    st.session_state.messages.append({"role": "assistant", "content": response})
                        # Initialize session state variables if they don't exist
                    if 'system_prompt' not in st.session_state:
                        st.session_state.system_prompt = system_prompt
                    
                    if 'response' not in st.session_state:
                        st.session_state.response = response
                    
                    if 'generated_examples' not in st.session_state:
                        st.session_state.generated_examples = []
                    
                    if 'generated_examples_csv' not in st.session_state:
                        st.session_state.generated_examples_csv = None
                    
                    if 'generated_examples_json' not in st.session_state:
                        st.session_state.generated_examples_json = None
                    
                    # Parse response and generate examples list
                    examples_list = []
                    for line in response.split('\n'):
                        if line.strip():
                            parts = line.rsplit('Label:', 1)
                            if len(parts) == 2:
                                text = parts[0].strip()
                                label = parts[1].strip()
                                if text and label:
                                    examples_list.append({
                                        'text': text,
                                        'label': label,
                                        'system_prompt': st.session_state.system_prompt,
                                        'system_role': st.session_state.system_role,
                                        'task_type': 'Data Generation',
                                        'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                                    })

                                    # example_dict = {
                                    #     'text': text,
                                    #     'label': label,
                                    #     'system_prompt': st.session_state.system_prompt,
                                    #     'system_role': st.session_state.system_role,
                                    #     'task_type': 'Data Generation',
                                    #     'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                                    # }
                                    # for attr in additional_attributes:
                                    #     example_dict[attr['attribute']] = random.choice(attr['topics'])
                                    
                                    # examples_list.append(example_dict)
                                   

                    if examples_list:
                        # Update session state with new data
                        st.session_state.generated_examples = examples_list
                        
                        # Generate CSV and JSON data
                        df = pd.DataFrame(examples_list)
                        st.session_state.generated_examples_csv = df.to_csv(index=False).encode('utf-8')
                        st.session_state.generated_examples_json = json.dumps(examples_list, indent=2).encode('utf-8')

                       # Vertical layout with centered "or" between buttons
                        st.download_button(
                            "📥 Download Generated Examples (CSV)",
                            st.session_state.generated_examples_csv,
                            "generated_examples.csv",
                            "text/csv",
                            key='download-csv-persistent'
                        )
                        
                        # Add space and center the "or"
                        st.markdown("""
                        <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
                        """, unsafe_allow_html=True)
                        
                        st.download_button(
                            "📥 Download Generated Examples (JSON)",
                            st.session_state.generated_examples_json,
                            "generated_examples.json",
                            "application/json",
                            key='download-json-persistent'
                        )
                        # # Display the labeled examples
                        # st.markdown("##### 📋 Labeled Examples Preview")
                        # st.dataframe(df, use_container_width=True)
                        
                    if st.button("Continue"):
                        if follow_up == "Generate more examples":
                            st.experimental_rerun()
                        elif follow_up == "Data Labeling":
                            st.session_state.task_choice = "Data Labeling"
                            st.experimental_rerun()

                except Exception as e:
                    st.error("An error occurred during generation.")
                    st.error(f"Details: {e}")

    
# Lableing Process
    elif st.session_state.task_choice == "Data Labeling":
        st.header("🏷️ Data Labeling")
        
        domain_selection = st.selectbox("Domain", ["Restaurant reviews", "E-Commerce reviews", "News", "AG News", "Tourism", "Custom"])
            # 2. Handle custom domain input
        custom_domain_valid = True  # Assume valid until proven otherwise
        
        if domain_selection == "Custom":
            domain = st.text_input("Specify custom domain")
            if not domain.strip():
                st.error("Please specify a domain name.")
                custom_domain_valid = False
        else:
            domain = domain_selection


        # Classification type selection
        classification_type = st.selectbox(
            "Classification Type",
            ["Sentiment Analysis", "Binary Classification", "Multi-Class Classification", "Named Entity Recognition (NER)"]
        )
#NNew edit
        # Labels setup based on classification type
        labels = []
        labels_valid = False
        errors = []

        if classification_type == "Sentiment Analysis":
            st.write("### Sentiment Analysis Labels (Fixed)")
            col1, col2, col3 = st.columns(3)
            with col1:
                label_1 = st.text_input("First class", "Positive", disabled=True)
            with col2:
                label_2 = st.text_input("Second class", "Negative", disabled=True)
            with col3:
                label_3 = st.text_input("Third class", "Neutral", disabled=True)
            labels = ["Positive", "Negative", "Neutral"]


        elif classification_type == "Binary Classification":
            st.write("### Binary Classification Labels")
            col1, col2 = st.columns(2)
        
            with col1:
                label_1 = st.text_input("First class", "Positive")
            with col2:
                label_2 = st.text_input("Second class", "Negative")
        
            errors = []
            labels = [label_1.strip(), label_2.strip()]
            

            # Strip and lower-case labels for validation
            label_1 = labels[0].strip()
            label_2 = labels[1].strip()
            
            # Check for empty class names
            if not label_1:
                errors.append("First class name is required.")
            if not label_2:
                errors.append("Second class name is required.")
            
            # Check for duplicates (case insensitive)
            if label_1.lower() == label_2.lower() and label_1 and label_2:
                errors.append("Class names must be different.")
            
            # Show errors or success
            if errors:
                for error in errors:
                    st.error(error)
            else:
                st.success("Binary class names are valid and unique!")

                               
        elif classification_type == "Multi-Class Classification":
                st.write("### Multi-Class Classification Labels")
            
                default_labels_by_domain = {
                    "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
                    "AG News": ["World", "Sports", "Business", "Sci/Tech"],
                    "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
                                "Food & Dining", "Local Experience", "Adventure Activities",
                                "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
                                "Luxury Tourism"],
                    "Restaurant reviews": ["Italian", "French", "American"],
                    "E-Commerce reviews": ["Mobile Phones & Accessories", "Laptops & Computers","Kitchen & Dining",
                                       "Beauty & Personal Care", "Home & Furniture", "Clothing & Fashion",
                                      "Shoes & Handbags", "Health & Wellness", "Electronics & Gadgets",
                                       "Books & Stationery","Toys & Games", "Sports & Fitness",
                                       "Grocery & Gourmet Food","Watches & Accessories", "Baby Products"]
            }
        
              
            
                # Ask user how many classes they want to define
                num_classes = st.slider("Select the number of classes (labels)", min_value=3, max_value=10, value=3)
            
                # Use default labels based on selected domain, if available
                defaults = default_labels_by_domain.get(domain, [])
            
                labels = []
                errors = []
                cols = st.columns(3)  # For nicely arranged label inputs
            
                for i in range(num_classes):
                    with cols[i % 3]:  # Distribute inputs across columns
                        default_value = defaults[i] if i < len(defaults) else ""
                        label_input = st.text_input(f"Label {i + 1}", default_value)
                        normalized_label = label_input.strip().title()
            
                        if not normalized_label:
                            errors.append(f"Label {i + 1} is required.")
                        else:
                            labels.append(normalized_label)
            
                # Check for duplicates (case-insensitive)
                normalized_set = {label.lower() for label in labels}
                if len(labels) != len(normalized_set):
                    errors.append("Label names must be unique (case-insensitive).")
            
                # Show validation results
                if errors:
                    for error in errors:
                        st.error(error)
                else:
                    st.success("All label names are valid and unique!")
            
                labels_valid = not errors  # True if no validation errors

        elif classification_type == "Named Entity Recognition (NER)":
            # # NER entity options
            # ner_entities = [
            #     "PERSON - Names of people, fictional characters, historical figures",
            #     "ORG - Companies, institutions, agencies, teams",
            #     "LOC - Physical locations (mountains, oceans, etc.)",
            #     "GPE - Countries, cities, states, political regions",
            #     "DATE - Calendar dates, years, centuries",
            #     "TIME - Times, durations",
            #     "MONEY - Monetary values with currency"
            # ]
            # selected_entities = st.multiselect(
            #      "Select entities to recognize", 
            #     ner_entities,
            #     default=["PERSON - Names of people, fictional characters, historical figures",
            #              "ORG - Companies, institutions, agencies, teams",
            #             "LOC - Physical locations (mountains, oceans, etc.)",
            #             "GPE - Countries, cities, states, political regions",
            #             "DATE - Calendar dates, years, centuries",
            #             "TIME - Times, durations",
            #             "MONEY - Monetary values with currency"],
            #             key="ner_entity_selection"
            # )
            #new 22/4/2025
            #if classification_type == "Named Entity Recognition (NER)":
            use_few_shot = True
            #new 22/4/2025
            few_shot_examples = [
                {"content": "Mount Everest is the tallest mountain in the world.", "label": "LOC: Mount Everest"},
                {"content": "The President of the United States visited Paris last summer.", "label": "GPE: United States, GPE: Paris"},
                {"content": "Amazon is expanding its offices in Berlin.", "label": "ORG: Amazon, GPE: Berlin"},
                {"content": "J.K. Rowling wrote the Harry Potter books.", "label": "PERSON: J.K. Rowling"},
                {"content": "Apple was founded in California in 1976.", "label": "ORG: Apple, GPE: California, DATE: 1976"},
                {"content": "The Nile is the longest river in Africa.", "label": "LOC: Nile, GPE: Africa"},
                {"content": "He arrived at 3 PM for the meeting.", "label": "TIME: 3 PM"},
                {"content": "She bought the dress for $200.", "label": "MONEY: $200"},
                {"content": "The event is scheduled for July 4th.", "label": "DATE: July 4th"},
                {"content": "The World Health Organization is headquartered in Geneva.", "label": "ORG: World Health Organization, GPE: Geneva"}
            ]
###########
        
            st.write("### Named Entity Recognition (NER) Entities")

            # Predefined standard entities
            ner_entities = [
                "PERSON - Names of people, fictional characters, historical figures",
                "ORG - Companies, institutions, agencies, teams",
                "LOC - Physical locations (mountains, oceans, etc.)",
                "GPE - Countries, cities, states, political regions",
                "DATE - Calendar dates, years, centuries",
                "TIME - Times, durations",
                "MONEY - Monetary values with currency"
            ]
            
            # User can add custom NER types
            custom_ner_entities = []
            if st.checkbox("Add custom NER entities?"):
                num_custom_ner = st.slider("Number of custom NER entities", 1, 10, 1)
                for i in range(num_custom_ner):
                    st.markdown(f"#### Custom Entity {i+1}")
                    custom_type = st.text_input(f"Entity type {i+1}", key=f"custom_ner_type_{i}")
                    custom_description = st.text_input(f"Description for {custom_type}", key=f"custom_ner_desc_{i}")
                    if custom_type and custom_description:
                        custom_ner_entities.append(f"{custom_type.upper()} - {custom_description}")
            
            # Combine built-in and custom NERs
            all_ner_options = ner_entities + custom_ner_entities
            
            selected_entities = st.multiselect(
                "Select entities to recognize", 
                all_ner_options,
                default=ner_entities
            )
            
            # Extract entity type names (before the dash)
            labels = [entity.split(" - ")[0].strip() for entity in selected_entities]

            if not labels:
                st.warning("Please select at least one entity type.")
                labels = ["PERSON"]

            ##########
        
            # # Extract just the entity type (before the dash)
            # labels = [entity.split(" - ")[0] for entity in selected_entities]
        
            # if not labels:
            #     st.warning("Please select at least one entity type")
            #     labels = ["PERSON"]  # Default if nothing selected

            
                            


    #NNew edit
            # elif classification_type == "Multi-Class Classification":
            #     st.write("### Multi-Class Classification Labels")
            
            #     default_labels_by_domain = {
            #         "News": ["Political", "Sports", "Entertainment", "Technology", "Business"],
            #         "AG News": ["World", "Sports", "Business", "Sci/Tech"],
            #         "Tourism": ["Accommodation", "Transportation", "Tourist Attractions", 
            #                     "Food & Dining", "Local Experience", "Adventure Activities",
            #                     "Wellness & Spa", "Eco-Friendly Practices", "Family-Friendly",
            #                     "Luxury Tourism"],
            #         "Restaurant reviews": ["Italian", "French", "American"]
            #     }
            #     num_classes = st.slider("Number of classes", 3, 10, 3)
            
            #     # Get defaults for selected domain, or empty list
            #     defaults = default_labels_by_domain.get(domain, [])
            
            #     labels = []
            #     errors = []
            #     cols = st.columns(3)
            
            #     for i in range(num_classes):
            #         with cols[i % 3]:
            #             default_value = defaults[i] if i < len(defaults) else ""
            #             label_input = st.text_input(f"Class {i+1}", default_value)
            #             normalized_label = label_input.strip().title()
            
            #             if not normalized_label:
            #                 errors.append(f"Class {i+1} name is required.")
            #             else:
            #                 labels.append(normalized_label)
            
            #     # Check for duplicates (case-insensitive)
            #     if len(labels) != len(set(labels)):
                #     errors.append("Labels names must be unique (case-insensitive, normalized to Title Case).")
    
                # # Show validation results
                # if errors:
                #     for error in errors:
                #         st.error(error)
                # else:
                #     st.success("All Labels names are valid and unique!")
                # labels_valid = not errors  # Will be True only if there are no label errors

                   

            
        # else:
        #     num_classes = st.slider("Number of classes", 3, 23, 3, key="label_num_classes")
        #     labels = []
        #     cols = st.columns(3)
        #     for i in range(num_classes):
        #         with cols[i % 3]:
        #             label = st.text_input(f"Class {i+1}", f"Class_{i+1}", key=f"label_class_{i}")
        #             labels.append(label)

        use_few_shot = st.toggle("Use few-shot examples for labeling")
        few_shot_examples = []
        if use_few_shot:
            num_few_shot = st.slider("Number of few-shot examples", 1, 10, 1)
            for i in range(num_few_shot):
                with st.expander(f"Few-shot Example {i+1}"):
                    content = st.text_area(f"Content", key=f"label_few_shot_content_{i}")
                    label = st.selectbox(f"Label", labels, key=f"label_few_shot_label_{i}")
                    if content and label:
                        few_shot_examples.append(f"{content}\nLabel: {label}")

        num_examples = st.number_input("Number of examples to classify", 1, 100, 1)
    
        examples_to_classify = []
        if num_examples <= 10:
            for i in range(num_examples):
                example = st.text_area(f"Example {i+1}", key=f"example_{i}")
                if example:
                    examples_to_classify.append(example)
        else:
            examples_text = st.text_area(
                "Enter examples (one per line)",
                height=300,
                help="Enter each example on a new line"
            )
            if examples_text:
                examples_to_classify = [ex.strip() for ex in examples_text.split('\n') if ex.strip()]
                if len(examples_to_classify) > num_examples:
                    examples_to_classify = examples_to_classify[:num_examples]
                    
        #New Wedyan
        #default_system_role = f"You are a professional {classification_type} expert, your role is to classify the provided text examples for {domain} domain."
         # System role customization  
        default_system_role = (f"You are a highly skilled {classification_type} expert."
        f" Your task is to accurately classify the provided text examples within the {domain} domain."
        f" Ensure that all classifications are precise, context-aware, and aligned with domain-specific standards and best practices."
    )
        system_role = st.text_area("Modify System Role (optional)", 
                                value=default_system_role,
                                key="system_role_input")
        st.session_state['system_role'] = system_role if system_role else default_system_role
        # Labels initialization
        #labels = []
        ####

        user_prompt = st.text_area("User prompt (optional)", key="label_instructions")

        few_shot_text = "\n\n".join(few_shot_examples) if few_shot_examples else ""
        examples_text = "\n".join([f"{i+1}. {ex}" for i, ex in enumerate(examples_to_classify)])
    
        # Customize prompt template based on classification type
        if classification_type == "Named Entity Recognition (NER)":
            # label_prompt_template = PromptTemplate(
            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
            #     template=(
            #         "{system_role}\n"
            #         #"- You are a professional Named Entity Recognition (NER) expert in {domain} domain. Your role is to identify and extract the following entity types: {labels}.\n"
            #         "- For each text example provided, identify all entities of the requested types.\n"
            #         "- Use the following entities: {labels}.\n"
            #         "- Return each example followed by the entities you found in this format: 'Example text.\n \n Entities:\n [ENTITY_TYPE: entity text\n\n, ENTITY_TYPE: entity text\n\n, ...] or [No entities found]'\n"
            #         "- If no entities of the requested types are found, indicate 'No entities found' in this text.\n"
            #         "- Be precise about entity boundaries - don't include unnecessary words.\n"
            #         "- Do not provide any additional information or explanations.\n"
            #         "- Additional instructions:\n {user_prompt}\n\n"
            #         "- Use user few-shot examples as guidance if provided:\n{few_shot_examples}\n\n"
            #         "- Examples to analyze:\n{examples}\n\n"
            #         "Output:\n"
            #     )
            # )
            #new 22/4/2025
            # label_prompt_template = PromptTemplate(
            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
            #     template=(
            #         "{system_role}\n"
            #         "- You are performing Named Entity Recognition (NER) in the domain of {domain}.\n"
            #         "- Use the following entity types: {labels}.\n\n"
            #         "### Reasoning Steps:\n"
            #         "1. Read the example carefully.\n"
            #         "2. For each named entity mentioned, determine its meaning and role in the sentence.\n"
            #         "3. Think about the **context**: Is it a physical location (LOC)? A geopolitical region (GPE)? A person (PERSON)?\n"
            #         "4. Based on the definition of each label, assign the most **specific and correct** label.\n\n"
            #         "For example:\n"
            #         "- 'Mount Everest' → LOC (it's a mountain)\n"
            #         "- 'France' → GPE (it's a country)\n"
            #         "- 'Microsoft' → ORG\n"
            #         "- 'John Smith' → PERSON\n\n"
            #         "- Return each example followed by the entities you found in this format:\n"
            #         "'Example text.'\nEntities: [ENTITY_TYPE: entity text, ENTITY_TYPE: entity text, ...] or [No entities found]\n"
            #         "- If no entities of the requested types are found, return 'No entities found'.\n"
            #         "- Be precise about entity boundaries - don't include extra words.\n"
            #         "- Do not explain or justify your answers.\n\n"
            #         "Additional instructions:\n{user_prompt}\n\n"
            #         "Few-shot examples:\n{few_shot_examples}\n\n"
            #         "Examples to label:\n{examples}\n"
            #         "Output:\n"
            #     )
            #)
            # label_prompt_template = PromptTemplate(
            #     input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
            #     template=(
            #         "{system_role}\n"
            #         "- You are an expert at Named Entity Recognition (NER) for domain: {domain}.\n"
            #         "- Use these entity types: {labels}.\n\n"
            #         "### Output Format:\n"
            #         # "Return each example followed by the entities you found in this format: 'Example text.\n Entities:\n [ENTITY_TYPE: entity text\n\"
            #         "Return each example followed by the entities you found in this format: 'Example text.\n 'Entity types:\n "Then group the entities under each label like this:\n" "
            #         #"Then Start with this line exactly: 'Entity types\n'\n"
            #         #"Then group the entities under each label like this:\n"
            #         "\n PERSON – Angela Merkel, John Smith\n\n"
            #         "\ ORG – Google, United Nations\n\n"
            #         "\n DATE – January 1st, 2023\n\n"
            #         "\n ... and so on.\n\n"
            #         "If entity {labels} not found, do not write it in your response\n"
            #         "- Do NOT output them inline after the text.\n"
            #         "- Do NOT repeat the sentence.\n"
            #         "- If no entities are found for a type, skip it.\n"
            #         "- Keep the format consistent.\n\n"
            #         "User Instructions:\n{user_prompt}\n\n"
            #         "Few-shot Examples:\n{few_shot_examples}\n\n"
            #         "Examples to analyze:\n{examples}"
            #       )
            # )

            
            label_prompt_template = PromptTemplate(
                input_variables=["system_role", "labels", "few_shot_examples", "examples", "domain", "user_prompt"],
                template=(
                    "{system_role}\n"
                    "- You are an expert at Named Entity Recognition (NER) for domain: {domain}.\n"
                    "- Use these entity types: {labels}.\n\n"
                    "### Output Format:\n"
                    "Return each example followed by the entities you found in this format:\n"
                    "'Example text.\nEntity types:\n"
                    "Then group the entities under each label like this:\n"
                    "\nPERSON – Angela Merkel, John Smith\n"
                    "ORG – Google, United Nations\n"
                    "DATE – January 1st, 2023\n"
                    "... and so on.\n\n"
                    "Each new entities group should be in a new line.\n"
                    "If entity type {labels} is not found, do not write it in your response.\n"
                    "- Do NOT output them inline after the text.\n"
                    "- Do NOT repeat the sentence.\n"
                    "- If no entities are found for a type, skip it.\n"
                    "- Keep the format consistent.\n\n"
                    "User Instructions:\n{user_prompt}\n\n"
                    "Few-shot Examples:\n{few_shot_examples}\n\n"
                    "Examples to analyze:\n{examples}"
                )
            )

#######
        else:
                label_prompt_template = PromptTemplate(

                input_variables=["system_role", "classification_type", "labels", "few_shot_examples", "examples","domain", "user_prompt"],
                template=(
                    #"- Let'\s think step by step:"
                    "{system_role}\n"
                   # "- You are a professional {classification_type} expert in {domain} domain. Your role is to classify the following examples using these labels: {labels}.\n"
                    "- Use the following instructions:\n"
                    "- Use the following labels: {labels}.\n"
                    "- Return the classified text followed by the label in this format: 'text. Label: [label]'\n"
                    "- Do not provide any additional information or explanations\n"
                    "- User prompt:\n {user_prompt}\n\n"
                    "- Use user provided examples as guidence in the classification process:\n\n {few_shot_examples}\n"
                    "- Examples to classify:\n{examples}\n\n"
                    "- Think step by step then classify the examples"
                    #"Output:\n"
                ))
            
        # Check if few_shot_examples is already a formatted string
           # Check if few_shot_examples is already a formatted string
        if isinstance(few_shot_examples, str):
            formatted_few_shot = few_shot_examples
        # If it's a list of already formatted strings
        elif isinstance(few_shot_examples, list) and all(isinstance(ex, str) for ex in few_shot_examples):
            formatted_few_shot = "\n".join(few_shot_examples)
        # If it's a list of dictionaries with 'content' and 'label' keys
        elif isinstance(few_shot_examples, list) and all(isinstance(ex, dict) and 'content' in ex and 'label' in ex for ex in few_shot_examples):
            formatted_few_shot = "\n".join([f"{ex['content']}\nLabel: {ex['label']}" for ex in few_shot_examples])
        else:
            formatted_few_shot = ""
# #new 22/4/2025
#         few_shot_examples = [
#             {"content": "Mount Everest is the tallest mountain in the world.", "label": "LOC: Mount Everest"},
#             {"content": "The President of the United States visited Paris last summer.", "label": "GPE: United States, GPE: Paris"},
#             {"content": "Amazon is expanding its offices in Berlin.", "label": "ORG: Amazon, GPE: Berlin"},
#             {"content": "J.K. Rowling wrote the Harry Potter books.", "label": "PERSON: J.K. Rowling"},
#             {"content": "Apple was founded in California in 1976.", "label": "ORG: Apple, GPE: California, DATE: 1976"},
#             {"content": "The Nile is the longest river in Africa.", "label": "LOC: Nile, GPE: Africa"},
#             {"content": "He arrived at 3 PM for the meeting.", "label": "TIME: 3 PM"},
#             {"content": "She bought the dress for $200.", "label": "MONEY: $200"},
#             {"content": "The event is scheduled for July 4th.", "label": "DATE: July 4th"},
#             {"content": "The World Health Organization is headquartered in Geneva.", "label": "ORG: World Health Organization, GPE: Geneva"}
#         ]
# ###########
# new 22/4/2025
        #formatted_few_shot = "\n".join([f"{ex['content']}\nEntities: [{ex['label']}]" for ex in few_shot_examples])
        formatted_few_shot = "\n\n".join([f"{ex['content']}\n\nEntity types\n{ex['label']}" for ex in few_shot_examples])

        ###########
        system_prompt = label_prompt_template.format(
            system_role=st.session_state['system_role'],
            classification_type=classification_type,
            domain=domain,
            examples="\n".join(examples_to_classify),
            labels=", ".join(labels),
            user_prompt=user_prompt,
            few_shot_examples=formatted_few_shot
        )

        # Step 2: Store the system_prompt in st.session_state
        st.session_state['system_prompt'] = system_prompt           
#::contentReference[oaicite:0]{index=0}
        st.write("System Prompt:")
        #st.code(system_prompt)
        #st.code(st.session_state['system_prompt'])
        st.text_area("System Prompt", value=st.session_state['system_prompt'], height=300, max_chars=None, key=None, help=None, disabled=True)
            
        

        if st.button("🏷️ Label Data"):
            if examples_to_classify:
                with st.spinner("Labeling data..."):
                    #Generate the system prompt based on classification type
                    if classification_type == "Named Entity Recognition (NER)":
                        system_prompt = label_prompt_template.format(
                            system_role=st.session_state['system_role'],
                            labels=", ".join(labels),
                            domain = domain,
                            few_shot_examples=few_shot_text,
                            examples=examples_text,
                            user_prompt=user_prompt
                    #new 
                            #'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                       )
                    # if classification_type == "Named Entity Recognition (NER)":
                    #     # Step 1: Split the full response by example
                    #     raw_outputs = [block.strip() for block in response.strip().split("Entity types") if block.strip()]
                    #     inputs = [ex.strip() for ex in examples_to_classify]
                    
                    #     # Step 2: Match inputs with NER output blocks
                    #     labeled_examples = []
                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
                    #         labeled_examples.append({
                    #             'text': text,
                    #             'entities': f"Entity types\n{output_block.strip()}",
                    #             'system_prompt': st.session_state.system_prompt,
                    #             'system_role': st.session_state.system_role,
                    #             'task_type': 'Named Entity Recognition (NER)',
                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                    #         })
                    
                    # if classification_type == "Named Entity Recognition (NER)":
                    #     # Step 1: Split the full response by example
                    #     raw_outputs = [block.strip() for block in response.strip().split("Entity types") if block.strip()]
                    #     inputs = [ex.strip() for ex in examples_to_classify]
                    
                    #     # Step 2: Match inputs with NER output blocks
                    #     labeled_examples = []
                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
                    #         labeled_examples.append({
                    #             'text': text,
                    #             'entities': f"Entity types\n{output_block.strip()}",
                    #             'system_prompt': st.session_state.system_prompt,
                    #             'system_role': st.session_state.system_role,
                    #             'task_type': 'Named Entity Recognition (NER)',
                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                    #         })

        
                    # import re

                    # if classification_type == "Named Entity Recognition (NER)":
                    #     # Use regex to split on "Entity types" while keeping it attached to each block
                    #     blocks = re.split(r"(Entity types)", response.strip())
                        
                    #     # Recombine 'Entity types' with each block after splitting
                    #     raw_outputs = [
                    #         (blocks[i] + blocks[i+1]).strip()
                    #         for i in range(1, len(blocks) - 1, 2)
                    #     ]
                        
                    #     inputs = [ex.strip() for ex in examples_to_classify]
                    
                    #     labeled_examples = []
                    #     for i, (text, output_block) in enumerate(zip(inputs, raw_outputs)):
                    #         labeled_examples.append({
                    #             'text': text,
                    #             'entities': output_block,
                    #             'system_prompt': st.session_state.system_prompt,
                    #             'system_role': st.session_state.system_role,
                    #             'task_type': 'Named Entity Recognition (NER)',
                    #             'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                    #         })


                    else:
                        system_prompt = label_prompt_template.format(
                            classification_type=classification_type,
                            system_role=st.session_state['system_role'],
                            domain = domain,
                            labels=", ".join(labels),
                            few_shot_examples=few_shot_text,
                            examples=examples_text,
                            user_prompt=user_prompt
                        )
                    try:
                        stream = client.chat.completions.create(
                            model=selected_model,
                            messages=[{"role": "system", "content": system_prompt}],
                            temperature=temperature,
                            stream=True,
                            #max_tokens=20000,
                            max_tokens=4000,
                            top_p = 0.9,
                          
                        )
                        #new 24 March
                        # Append user message
                        st.session_state.messages.append({"role": "user", "content": system_prompt})
                        #################
                        response = st.write_stream(stream)
                        st.session_state.messages.append({"role": "assistant", "content": response})
                         # Display the labeled examples
                        #    # Optional: If you want to add it as a chat-style message log
                        # preview_str = st.session_state.labeled_preview.to_markdown(index=False)
                        # st.session_state.messages.append({"role": "assistant", "content": f"Here is a preview of the labeled examples:\n\n{preview_str}"})
                                                                                                    

                        # # Stream response and append assistant message
                        # #14/4/2024
                        # response = st.write_stream(stream)
                        # st.session_state.messages.append({"role": "assistant", "content": response})

                          # Initialize session state variables if they don't exist
                        if 'system_prompt' not in st.session_state:
                            st.session_state.system_prompt = system_prompt
                        
                        if 'response' not in st.session_state:
                            st.session_state.response = response
                        
                        if 'generated_examples' not in st.session_state:
                            st.session_state.generated_examples = []
                        
                        if 'generated_examples_csv' not in st.session_state:
                            st.session_state.generated_examples_csv = None
                        
                        if 'generated_examples_json' not in st.session_state:
                            st.session_state.generated_examples_json = None
                    

                      
                    
                        # Save labeled examples to CSV
                        #new 14/4/2025
                        #labeled_examples = []
                        # if classification_type == "Named Entity Recognition (NER)":
                        #     labeled_examples = []
                        #     for line in response.split('\n'):
                        #         if line.strip():
                        #             parts = line.rsplit('Entities:', 1)
                        #             if len(parts) == 2:
                        #                 text = parts[0].strip()
                        #                 entities = parts[1].strip()
                        #                 if text and entities:
                        #                     labeled_examples.append({
                        #                         'text': text,
                        #                         'entities': entities,
                        #                         'system_prompt': st.session_state.system_prompt,
                        #                         'system_role': st.session_state.system_role,
                        #                         'task_type': 'Named Entity Recognition (NER)',
                        #                         'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                        #                     })

                                            #new 22/4/2025
                        labeled_examples = []
                        if classification_type == "Named Entity Recognition (NER)":
                            labeled_examples = [{
                                'ner_output': response.strip(),
                                'system_prompt': st.session_state.system_prompt,
                                'system_role': st.session_state.system_role,
                                'task_type': 'Named Entity Recognition (NER)',
                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',
                            }]

                                            ######
                       
                                            
                        else:
                            labeled_examples = []
                            for line in response.split('\n'):
                                if line.strip():
                                    parts = line.rsplit('Label:', 1)
                                    if len(parts) == 2:
                                        text = parts[0].strip()
                                        label = parts[1].strip()
                                        if text and label:
                                            labeled_examples.append({
                                                'text': text,
                                                'label': label,
                                                'system_prompt': st.session_state.system_prompt,
                                                'system_role': st.session_state.system_role,
                                                'task_type': 'Data Labeling',
                                                'Use few-shot example?': 'Yes' if use_few_shot else 'No',  
                                            })
                       # Save and provide download options
                        if labeled_examples:
                            # Update session state
                            st.session_state.labeled_examples = labeled_examples
                        
                            # Convert to CSV and JSON
                            df = pd.DataFrame(labeled_examples)
                            #new 22/4/2025
                            # CSV
                            st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
                            
                            # JSON
                            st.session_state.labeled_examples_json = json.dumps({
                                "metadata": {
                                    "domain": domain,
                                    "labels": labels,
                                    "used_few_shot": use_few_shot,
                                    "task_type": "Named Entity Recognition (NER)",
                                    "timestamp": datetime.now().isoformat()
                                },
                                "examples": labeled_examples
                            }, indent=2).encode('utf-8')

                            ############
                            # CSV
                            # st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
                            
                            # # JSON
                            # st.session_state.labeled_examples_json = json.dumps({
                            #     "metadata": {
                            #         "domain": domain,
                            #         "labels": labels,
                            #         "used_few_shot": use_few_shot,
                            #         "task_type": "Named Entity Recognition (NER)",
                            #         "timestamp": datetime.now().isoformat()
                            #     },
                            #     "examples": labeled_examples
                            # }, indent=2).encode('utf-8')
                            
                                                        ########
                            # st.session_state.labeled_examples_csv = df.to_csv(index=False).encode('utf-8')
                            # st.session_state.labeled_examples_json = json.dumps(labeled_examples, indent=2).encode('utf-8')
                        
                            # Download buttons
                            st.download_button(
                                "📥 Download Labeled Examples (CSV)",
                                st.session_state.labeled_examples_csv,
                                "labeled_examples.csv",
                                "text/csv",
                                key='download-labeled-csv'
                            )
                        
                            st.markdown("""
                            <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
                            """, unsafe_allow_html=True)
                        
                            st.download_button(
                                "📥 Download Labeled Examples (JSON)",
                                st.session_state.labeled_examples_json,
                                "labeled_examples.json",
                                "application/json",
                                key='download-labeled-json'
                            )
                            # Display the labeled examples
                            st.markdown("##### 📋 Labeled Examples Preview")
                            st.dataframe(df, use_container_width=True)
                            # Display section
                            #st.markdown("### 📋 Labeled Examples Preview")
                            #st.dataframe(st.session_state.labeled_preview, use_container_width=True)

                                                                            
                                                                        
                        # if labeled_examples:
                        #     df = pd.DataFrame(labeled_examples)
                        #     csv = df.to_csv(index=False).encode('utf-8')
                        #     st.download_button(
                        #         "📥 Download Labeled Examples",
                        #         csv,
                        #         "labeled_examples.csv",
                        #         "text/csv",
                        #         key='download-labeled-csv'
                        #     )
                        # # Add space and center the "or"
                        # st.markdown("""
                        # <div style='text-align: left; margin:15px 0; font-weight: 600; color: #666;'>. . . . . .         or</div>
                        # """, unsafe_allow_html=True)
                        
                        # if labeled_examples:
                        #     df = pd.DataFrame(labeled_examples)
                        #     csv = df.to_csv(index=False).encode('utf-8')
                        #     st.download_button(
                        #         "📥 Download Labeled Examples",
                        #         csv,
                        #         "labeled_examples.json",
                        #         "text/json",
                        #         key='download-labeled-JSON'
                        #     )
                            
                        # Add follow-up interaction options
                        #st.markdown("---")
                        #follow_up = st.radio(
                            #"What would you like to do next?",
                             #["Label more data", "Data Generation"],
                           # key="labeling_follow_up"
                      #  )
                    
                        if st.button("Continue"):
                            if follow_up == "Label more data":
                                st.session_state.examples_to_classify = []
                                st.experimental_rerun()
                            elif follow_up == "Data Generation":
                                st.session_state.task_choice = "Data Labeling"
                                st.experimental_rerun()
                            
                    except Exception as e:
                        st.error("An error occurred during labeling.")
                        st.error(f"Details: {e}")
            else:
                st.warning("Please enter at least one example to classify.")

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

# Footer
st.markdown("---")
st.markdown(
    """
    <div style='text-align: center'>
        <p>Made with ❤️ by Wedyan AlSakran 2025</p>
    </div>
    """, 
    unsafe_allow_html=True
)