import gradio as gr
import json
import tempfile
import os
import re # For parsing conversation
from typing import Union, Optional, Dict, Tuple # Import Dict and Tuple
# Import the actual functions from synthgen
from synthgen import (
    generate_synthetic_text,
    generate_prompts,
    generate_synthetic_conversation,
    generate_corpus_content # Import the new function
)
# We no longer need to import api_key here or check it directly in app.py


# --- Helper Functions for JSON Generation ---

# Use Union for Python < 3.10 compatibility
def create_json_file(data: object, base_filename: str) -> Union[str, None]:
    """Creates a temporary JSON file and returns its path."""
    try:
        # Create a temporary file with a .json extension
        with tempfile.NamedTemporaryFile(mode='w', suffix=".json", delete=False, encoding='utf-8') as temp_file:
            json.dump(data, temp_file, indent=4, ensure_ascii=False)
            return temp_file.name # Return the path to the temporary file
    except Exception as e:
        print(f"Error creating JSON file {base_filename}: {e}")
        return None

# Add the missing function definition
def create_text_file(data: str, base_filename: str) -> Union[str, None]:
    """Creates a temporary text file and returns its path."""
    try:
        # Ensure filename ends with .txt
        if not base_filename.lower().endswith(".txt"):
             base_filename += ".txt" # Append if missing for clarity, though suffix handles it
        # Create a temporary file with a .txt extension
        with tempfile.NamedTemporaryFile(mode='w', suffix=".txt", delete=False, encoding='utf-8') as temp_file:
            temp_file.write(data)
            return temp_file.name # Return the path to the temporary file
    except Exception as e:
        print(f"Error creating text file {base_filename}: {e}")
        return None

def parse_conversation_string(text: str) -> list[dict]:
    """Parses a multi-line conversation string into a list of message dictionaries."""
    messages = []
    # Regex to capture "User:" or "Assistant:" at the start of a line, followed by content
    pattern = re.compile(r"^(User|Assistant):\s*(.*)$", re.IGNORECASE | re.MULTILINE)
    matches = pattern.finditer(text)
    for match in matches:
        role = match.group(1).lower()
        content = match.group(2).strip()
        messages.append({"role": role, "content": content})
    # If parsing fails or format is unexpected, return raw text in a single message?
    # Or return empty list? Let's return what we found.
    if not messages and text: # If regex found nothing but text exists
         print(f"Warning: Could not parse conversation structure for: '{text[:100]}...'")
         # Fallback: return the whole text as a single assistant message? Or user?
         # Let's return a generic system message indicating the raw content
         # return [{"role": "system", "content": f"Unparsed conversation text: {text}"}]
         # Or maybe just return empty, TBD based on preference
         pass # Return empty list if parsing fails for now
    return messages


# Wrapper for text generation (remains largely the same, but error handling is improved in synthgen)
def run_generation(prompt: str, model: str, num_samples: int) -> str:
    """
    Wrapper function for Gradio interface to generate multiple text samples.
    Relies on generate_synthetic_text for API calls and error handling.
    """
    if not prompt:
        return "Error: Please enter a prompt."
    if num_samples <= 0:
        return "Error: Number of samples must be positive."

    output = f"Generating {num_samples} samples using model '{model}'...\n"
    output += "="*20 + "\n\n"

    # generate_synthetic_text now handles API errors internally
    for i in range(num_samples):
        # The function returns the text or an error string starting with "Error:"
        generated_text = generate_synthetic_text(prompt, model)
        output += f"--- Sample {i+1} ---\n"
        output += generated_text + "\n\n" # Append result directly

    output += "="*20 + "\nGeneration complete (check results above for errors)."
    return output


# Removed the placeholder backend functions (generate_prompts_backend, generate_single_conversation)


# Modified function to handle multiple conversation prompts using the real backend
def run_conversation_generation(system_prompts_text: str, model: str, num_turns: int) -> str:
    """
    Wrapper function for Gradio interface to generate multiple conversations
    based on a list of prompts, calling generate_synthetic_conversation.
    """
    if not system_prompts_text:
        return "Error: Please enter or generate at least one system prompt/topic."
    if num_turns <= 0:
        return "Error: Number of turns must be positive."

    prompts = [p.strip() for p in system_prompts_text.strip().split('\n') if p.strip()]
    if not prompts:
        return "Error: No valid prompts found in the input."

    output = f"Generating {len(prompts)} conversations ({num_turns} turns each) using model '{model}'...\n"
    output += "="*40 + "\n\n"

    for i, prompt in enumerate(prompts):
        # Call the actual function from synthgen.py
        # It handles API calls and returns the conversation or an error string.
        conversation_text = generate_synthetic_conversation(prompt, model, num_turns)

        # We don't need a try-except here because the function itself returns error strings
        # The title is now included within the returned string from the function
        output += f"--- Conversation {i+1}/{len(prompts)} ---\n"
        output += conversation_text + "\n\n" # Append result directly


    output += "="*40 + "\nGeneration complete (check results above for errors)."
    return output

# Helper function for the Gradio UI to generate prompts using the real backend
def generate_prompts_ui(
    num_prompts: int,
    model: str,
    temperature: float, # Add settings
    top_p: float,
    max_tokens: int
) -> str:
    """UI Wrapper to call the generate_prompts backend and format for Textbox."""
    # Handle optional settings
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    # Use a specific max_tokens for prompt generation or pass from UI? Let's pass from UI
    max_tokens_val = max_tokens if max_tokens > 0 else 200 # Set a default if UI value is 0

    if not model:
        return "Error: Please select a model for prompt generation."
    if num_prompts <= 0:
        return "Error: Number of prompts to generate must be positive."
    if num_prompts > 50:
        return "Error: Cannot generate more than 50 prompts at a time."

    print(f"Generating prompts with settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val}") # Debug print

    try:
        # Call the actual function from synthgen.py, passing settings
        prompts_list = generate_prompts(
            num_prompts,
            model,
            temperature=temp_val,
            top_p=top_p_val,
            max_tokens=max_tokens_val
        )
        return "\n".join(prompts_list)
    except ValueError as e:
        # Catch errors raised by generate_prompts (e.g., API errors, parsing errors)
        return f"Error generating prompts: {e}"
    except Exception as e:
        # Catch any other unexpected errors
        print(f"Unexpected error in generate_prompts_ui: {e}")
        return f"An unexpected error occurred: {e}"


# --- Modified Generation Wrappers ---

# Wrapper for text generation + JSON preparation - RETURNS TUPLE
def run_generation_and_prepare_json(
    prompt: str,
    model: str,
    num_samples: int,
    temperature: float,
    top_p: float,
    max_tokens: int
) -> Tuple[gr.update, gr.update]: # Return type hint (optional)
    """Generates text samples and prepares a JSON file for download."""
    # Handle optional settings
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    max_tokens_val = max_tokens if max_tokens > 0 else None

    # Handle errors by returning updates for both outputs in a tuple
    if not prompt:
        return (gr.update(value="Error: Please enter a prompt."), gr.update(value=None))
    if num_samples <= 0:
         return (gr.update(value="Error: Number of samples must be positive."), gr.update(value=None))

    output_str = f"Generating {num_samples} samples using model '{model}'...\n"
    output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n"
    output_str += "="*20 + "\n\n"
    results_list = []

    for i in range(num_samples):
        generated_text = generate_synthetic_text(
            prompt, model, temperature=temp_val, top_p=top_p_val, max_tokens=max_tokens_val
        )
        output_str += f"--- Sample {i+1} ---\n"
        output_str += generated_text + "\n\n"
        if not generated_text.startswith("Error:"):
            results_list.append(generated_text)

    output_str += "="*20 + "\nGeneration complete (check results above for errors)."
    json_filepath = create_json_file(results_list, "text_samples.json")

    # Return tuple of updates in the order of outputs list
    return (gr.update(value=output_str), gr.update(value=json_filepath))


# Wrapper for conversation generation + JSON preparation - RETURNS TUPLE
def run_conversation_generation_and_prepare_json(
    system_prompts_text: str,
    model: str,
    num_turns: int,
    temperature: float,
    top_p: float,
    max_tokens: int
) -> Tuple[gr.update, gr.update]: # Return type hint (optional)
    """Generates conversations and prepares a JSON file for download."""
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    max_tokens_val = max_tokens if max_tokens > 0 else None

    # Handle errors by returning updates for both outputs in a tuple
    if not system_prompts_text:
        return (gr.update(value="Error: Please enter or generate at least one system prompt/topic."), gr.update(value=None))
    if num_turns <= 0:
         return (gr.update(value="Error: Number of turns must be positive."), gr.update(value=None))

    prompts = [p.strip() for p in system_prompts_text.strip().split('\n') if p.strip()]
    if not prompts:
        return (gr.update(value="Error: No valid prompts found in the input."), gr.update(value=None))

    output_str = f"Generating {len(prompts)} conversations ({num_turns} turns each) using model '{model}'...\n"
    output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n"
    output_str += "="*40 + "\n\n"
    results_list_structured = []

    for i, prompt in enumerate(prompts):
        conversation_text = generate_synthetic_conversation(
            prompt, model, num_turns, temperature=temp_val, top_p=top_p_val, max_tokens=max_tokens_val
        )
        output_str += f"--- Conversation {i+1}/{len(prompts)} ---\n"
        output_str += conversation_text + "\n\n"
        # --- Parsing Logic ---
        core_conversation_text = conversation_text
        if conversation_text.startswith("Error:"): core_conversation_text = None
        elif "\n\n" in conversation_text:
             parts = conversation_text.split("\n\n", 1)
             core_conversation_text = parts[1] if len(parts) > 1 else conversation_text
        if core_conversation_text:
            messages = parse_conversation_string(core_conversation_text)
            if messages: results_list_structured.append({"prompt": prompt, "messages": messages})
            else: results_list_structured.append({"prompt": prompt, "error": "Failed to parse structure.", "raw_text": core_conversation_text})
        elif conversation_text.startswith("Error:"): results_list_structured.append({"prompt": prompt, "error": conversation_text})
        else: results_list_structured.append({"prompt": prompt, "error": "Could not extract content.", "raw_text": conversation_text})
        # --- End Parsing Logic ---

    output_str += "="*40 + "\nGeneration complete (check results above for errors)."
    json_filepath = create_json_file(results_list_structured, "conversations.json")

    # Return tuple of updates in the order of outputs list
    return (gr.update(value=output_str), gr.update(value=json_filepath))


# Define content_type_labels globally for use in UI and wrapper functions
content_type_labels = {
    "Corpus Snippets": "# Snippets",
    "Short Story": "Approx Words",
    "Article": "Approx Words"
}
content_type_defaults = {
    "Corpus Snippets": 5,
    "Short Story": 1000, # Match new backend default
    "Article": 1500     # Match new backend default
}

# Wrapper for Corpus/Content Generation
def run_corpus_generation_and_prepare_file(
    topic: str,
    content_type: str,
    length_param: int,
    model: str,
    temperature: float,
    top_p: float,
    max_tokens: int
) -> Tuple[gr.update, gr.update]:
    """Generates corpus/story/article content and prepares a file for download."""
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    max_tokens_val = max_tokens if max_tokens > 0 else None

    # Use the global dictionary for error messages
    label_for_error = content_type_labels.get(content_type, 'Length Param')
    if not topic: return (gr.update(value="Error: Please enter a topic."), gr.update(value=None))
    if not content_type: return (gr.update(value="Error: Please select a content type."), gr.update(value=None))
    if length_param <= 0: return (gr.update(value=f"Error: Please enter a positive value for '{label_for_error}'."), gr.update(value=None))

    print(f"Generating {content_type} about '{topic}'...")
    output_str = f"Generating {content_type} about '{topic}' using model '{model}'...\n"
    output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n" + "="*40 + "\n\n"

    generated_content = generate_corpus_content(
        topic=topic, content_type=content_type, length_param=length_param, model=model,
        temperature=temp_val, top_p=top_p_val, max_tokens=max_tokens_val
    )
    output_str += generated_content

    file_path = None
    if not generated_content.startswith("Error:"):
         core_content = generated_content
         if "\n\n" in generated_content: parts = generated_content.split("\n\n", 1); core_content = parts[1] if len(parts) > 1 else generated_content
         if content_type == "Corpus Snippets":
             snippets = [s.strip() for s in core_content.split('---') if s.strip()]
             if not snippets: snippets = [s.strip() for s in core_content.split('\n\n') if s.strip()]
             corpus_data = {"topic": topic, "snippets": snippets}
             file_path = create_json_file(corpus_data, f"{topic}_corpus.json")
         else:
             file_path = create_text_file(core_content, f"{topic}_{content_type.replace(' ','_')}.txt")

    return (gr.update(value=output_str), gr.update(value=file_path))

# NEW function to update the length parameter label and default value
def update_length_param_ui(content_type: str) -> gr.update:
    """Updates the label and default value of the length parameter input."""
    new_label = content_type_labels.get(content_type, "Length Param")
    new_value = content_type_defaults.get(content_type, 5) # Default to 5 if type unknown
    return gr.update(label=new_label, value=new_value)


# --- Generation Wrappers ---
# ... (generate_prompts_ui, run_generation_and_prepare_json, run_conversation_generation_and_prepare_json remain the same) ...

# NEW UI Wrapper for generating TOPICS
def generate_topics_ui(
    num_topics: int,
    model: str,
    temperature: float,
    top_p: float,
    max_tokens: int
) -> str:
    """UI Wrapper to generate diverse topics using the AI."""
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    max_tokens_val = max_tokens if max_tokens > 0 else 150 # Limit token for topic list

    if not model:
        return "Error: Please select a model for topic generation."
    if num_topics <= 0:
        return "Error: Number of topics to generate must be positive."
    if num_topics > 50: # Keep limit reasonable
        return "Error: Cannot generate more than 50 topics at a time."

    print(f"Generating {num_topics} topics with settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val}")

    # Instruction focused on generating topics
    instruction = (
        f"Generate exactly {num_topics} diverse and interesting topics suitable for generating content like articles, stories, or corpus snippets. "
        f"Each topic should be concise (a few words to a short phrase). "
        f"Present each topic on a new line, with no other introductory or concluding text or numbering."
        f"\n\nExamples:\n"
        f"The future of renewable energy\n"
        f"The history of the Silk Road\n"
        f"The impact of social media on mental health"
    )
    system_msg = "You are an expert topic generator. Follow the user's instructions precisely."

    try:
        # Use the core text generation function
        generated_text = generate_synthetic_text(
            instruction,
            model,
            system_message=system_msg,
            temperature=temp_val,
            top_p=top_p_val,
            max_tokens=max_tokens_val
        )

        if generated_text.startswith("Error:"):
             raise ValueError(generated_text) # Propagate error

        # Split into lines and clean up
        topics_list = [t.strip() for t in generated_text.strip().split('\n') if t.strip()]

        if not topics_list:
             print(f"Warning: Failed to parse topics from generated text. Raw text:\n{generated_text}")
             raise ValueError("AI failed to generate topics in the expected format.")

        # Return newline-separated string for the Textbox
        return "\n".join(topics_list[:num_topics]) # Truncate if needed

    except ValueError as e:
        return f"Error generating topics: {e}"
    except Exception as e:
        print(f"Unexpected error in generate_topics_ui: {e}")
        return f"An unexpected error occurred: {e}"

# Modified Wrapper for Bulk Corpus/Content Generation
def run_bulk_content_generation_and_prepare_json(
    topics_text: str, # Renamed from topic
    content_type: str,
    length_param: int,
    model: str,
    temperature: float,
    top_p: float,
    max_tokens: int
) -> Tuple[gr.update, gr.update]:
    """Generates content for multiple topics and prepares a JSON file."""
    temp_val = temperature if temperature > 0 else None
    top_p_val = top_p if 0 < top_p <= 1 else None
    max_tokens_val = max_tokens if max_tokens > 0 else None

    # --- Input Validation ---
    if not topics_text:
        return (gr.update(value="Error: Please enter or generate at least one topic."), gr.update(value=None))
    if not content_type:
        return (gr.update(value="Error: Please select a content type."), gr.update(value=None))

    topics = [t.strip() for t in topics_text.strip().split('\n') if t.strip()]
    if not topics:
        return (gr.update(value="Error: No valid topics found in the input."), gr.update(value=None))

    label_for_error = content_type_labels.get(content_type, 'Length Param')
    if length_param <= 0:
        return (gr.update(value=f"Error: Please enter a positive value for '{label_for_error}'."), gr.update(value=None))
    # --- End Validation ---

    output_str = f"Generating {content_type} for {len(topics)} topics using model '{model}'...\n"
    output_str += f"(Settings: Temp={temp_val}, Top-P={top_p_val}, MaxTokens={max_tokens_val})\n" + "="*40 + "\n\n"

    bulk_results = [] # Store results for JSON

    # --- Loop through topics ---
    for i, topic in enumerate(topics):
        print(f"Generating {content_type} for topic {i+1}/{len(topics)}: '{topic}'...")
        output_str += f"--- Topic {i+1}/{len(topics)}: '{topic}' ---\n"

        generated_content_full = generate_corpus_content( # Returns string including title/error
            topic=topic, content_type=content_type, length_param=length_param, model=model,
            temperature=temp_val, top_p=top_p_val, max_tokens=max_tokens_val
        )

        output_str += generated_content_full + "\n\n" # Add full result to textbox

        # --- Prepare structured result for JSON ---
        result_entry = {"topic": topic, "content_type": content_type}
        if generated_content_full.startswith("Error:"):
            result_entry["status"] = "error"
            result_entry["error_message"] = generated_content_full
            result_entry["content"] = None
        else:
            result_entry["status"] = "success"
            result_entry["error_message"] = None
            # Extract core content (remove potential title added by backend)
            core_content = generated_content_full
            if "\n\n" in generated_content_full:
                parts = generated_content_full.split("\n\n", 1)
                core_content = parts[1] if len(parts) > 1 else generated_content_full

            if content_type == "Corpus Snippets":
                snippets = [s.strip() for s in core_content.split('---') if s.strip()]
                if not snippets: snippets = [s.strip() for s in core_content.split('\n\n') if s.strip()]
                result_entry["content"] = snippets # Store list for corpus
            else:
                result_entry["content"] = core_content # Store string for story/article

        bulk_results.append(result_entry)
        # --- End JSON preparation ---

    # --- Finalize ---
    output_str += "="*40 + f"\nBulk generation complete for {len(topics)} topics."
    json_filepath = create_json_file(bulk_results, f"{content_type.replace(' ','_')}_bulk_results.json")

    return (gr.update(value=output_str), gr.update(value=json_filepath))


# --- Gradio Interface Definition ---
with gr.Blocks() as demo:
    gr.Markdown("# Synthetic Data Generator using OpenRouter")
    gr.Markdown(
        "Generate synthetic text samples, conversations, or other content using various models"
    )
    # Removed the api_key_loaded check and warning Markdown

    # Define model choices (can be shared or specific per tab)
    # Consider fetching these dynamically from OpenRouter if possible in the future
    model_choices = [
            "deepseek/deepseek-chat-v3-0324:free", # Example free model
            "meta-llama/llama-3.3-70b-instruct:free",
            "deepseek/deepseek-r1:free",
            "google/gemini-2.5-pro-exp-03-25:free",
            "qwen/qwen-2.5-72b-instruct:free",
            "featherless/qwerky-72b:free",
            "google/gemma-3-27b-it:free",
            "mistralai/mistral-small-24b-instruct-2501:free",
            "deepseek/deepseek-r1-distill-llama-70b:free",
            "sophosympatheia/rogue-rose-103b-v0.2:free",
            "nvidia/llama-3.1-nemotron-70b-instruct:free",
            "microsoft/phi-3-medium-128k-instruct:free",
            "undi95/toppy-m-7b:free",
            "huggingfaceh4/zephyr-7b-beta:free",
            "openrouter/quasar-alpha"
            # Add more model IDs as needed
    ]
    default_model = model_choices[0] if model_choices else None

    # --- Shared Model Settings ---
    # Use an Accordion for less clutter
    with gr.Accordion("Model Settings (Optional)", open=False):
        # Set reasonable ranges and defaults. Use 0 for Max Tokens/Top-P to signify 'None'/API default.
        temperature_slider = gr.Slider(minimum=0.0, maximum=2.0, value=0.7, step=0.1, label="Temperature", info="Controls randomness. Higher values are more creative, lower are more deterministic. 0 means use API default.")
        top_p_slider = gr.Slider(minimum=0.0, maximum=1.0, value=0.0, step=0.05, label="Top-P (Nucleus Sampling)", info="Considers only tokens with cumulative probability mass >= top_p. 0 means use API default.")
        max_tokens_slider = gr.Number(value=0, minimum=0, maximum=8192, step=64, label="Max Tokens", info="Maximum number of tokens to generate in the completion. 0 means use API default.")


    with gr.Tabs():
        with gr.TabItem("Text Generation"):
            with gr.Row():
                prompt_input_text = gr.Textbox(label="Prompt", placeholder="Enter your prompt here (e.g., Generate a short product description for a sci-fi gadget)", lines=3)
            with gr.Row():
                model_input_text = gr.Dropdown(
                    label="OpenRouter Model ID",
                    choices=model_choices,
                    value=default_model
                )
                num_samples_input_text = gr.Number(label="Number of Samples", value=3, minimum=1, maximum=20, step=1)

            generate_button_text = gr.Button("Generate Text Samples")
            output_text = gr.Textbox(label="Generated Samples", lines=15, show_copy_button=True)
            # Add File component for download
            download_file_text = gr.File(label="Download Samples as JSON")

            generate_button_text.click(
                fn=run_generation_and_prepare_json,
                inputs=[
                    prompt_input_text, model_input_text, num_samples_input_text,
                    temperature_slider, top_p_slider, max_tokens_slider # Add settings inputs
                ],
                outputs=[output_text, download_file_text]
            )


        with gr.TabItem("Conversation Generation"):
            gr.Markdown("Enter one system prompt/topic per line below, or use the 'Generate Prompts' button.")
            with gr.Row():
                 # Textbox for multiple prompts
                prompt_input_conv = gr.Textbox(
                    label="Prompts (one per line)",
                    lines=5, # Make it multi-line
                    placeholder="Enter prompts here, one per line...\ne.g., Act as a pirate discussing treasure maps.\nDiscuss the future of space travel."
                )
            with gr.Row():
                 # Input for number of prompts to generate
                num_prompts_input_conv = gr.Number(label="Number of Prompts to Generate", value=5, minimum=1, maximum=20, step=1) # Keep max reasonable
                 # Button to trigger AI prompt generation
                generate_prompts_button = gr.Button("Generate Prompts using AI")
            with gr.Row():
                 # Model selection for conversation generation AND prompt generation
                model_input_conv = gr.Dropdown(
                    label="OpenRouter Model ID (for generation)",
                    choices=model_choices,
                    value=default_model
                )

            with gr.Row():
                # Input for number of turns per conversation
                num_turns_input_conv = gr.Number(label="Number of Turns per Conversation (approx)", value=5, minimum=1, maximum=20, step=1) # Keep max reasonable

            # Button to generate the conversations based on the prompts in the Textbox
            generate_conversations_button = gr.Button("Generate Conversations")
            output_conv = gr.Textbox(label="Generated Conversations", lines=15, show_copy_button=True)
            # Add File component for download
            download_file_conv = gr.File(label="Download Conversations as JSON")

            # Connect the "Generate Prompts" button to the UI wrapper
            generate_prompts_button.click(
                fn=generate_prompts_ui, # Use the wrapper that calls the real function
                inputs=[
                    num_prompts_input_conv, model_input_conv,
                    temperature_slider, top_p_slider, max_tokens_slider # Add settings inputs
                ],
                outputs=prompt_input_conv
            )

            # Connect the "Generate Conversations" button to the real function wrapper
            generate_conversations_button.click(
                fn=run_conversation_generation_and_prepare_json, # Use the wrapper that calls the real function
                inputs=[
                    prompt_input_conv, model_input_conv, num_turns_input_conv,
                    temperature_slider, top_p_slider, max_tokens_slider # Add settings inputs
                ],
                outputs=[output_conv, download_file_conv] # Output to both Textbox and File
            )

        # --- Content Generation Tab (Modified for Bulk) ---
        with gr.TabItem("Bulk Content Generation"):
            output_content = gr.Textbox(label="Generated Content (Log)", lines=15, show_copy_button=True)
            # Output is now always JSON
            download_file_content = gr.File(label="Download Results as JSON")

            gr.Markdown("Enter one topic per line below, or use the 'Generate Topics' button.")
            with gr.Row():
                # Changed to multi-line Textbox
                topic_input_content = gr.Textbox(
                    label="Topics (one per line)",
                    lines=5,
                    placeholder="Enter topics here, one per line...\ne.g., The future of renewable energy\nThe history of the Silk Road"
                )

            # --- Topic Generation ---
            with gr.Accordion("Topic Generation Options", open=False):
                 with gr.Row():
                    num_topics_input = gr.Number(label="# Topics to Generate", value=5, minimum=1, maximum=50, step=1)
                    # Use shared model selector below and settings
                    generate_topics_button = gr.Button("Generate Topics using AI")

            # --- Generation Settings ---
            with gr.Row():
                content_type_choices = list(content_type_labels.keys())
                content_type_input = gr.Dropdown(
                    label="Content Type", choices=content_type_choices, value=content_type_choices[0]
                )
                default_length_label = content_type_labels[content_type_choices[0]]
                default_length_value = content_type_defaults[content_type_choices[0]]
                length_param_input = gr.Number(
                    label=default_length_label, value=default_length_value, minimum=1, step=1
                )
            with gr.Row():
                model_input_content = gr.Dropdown(label="Model", choices=model_choices, value=default_model)

            # Button to trigger bulk generation
            generate_content_button = gr.Button("Generate Bulk Content")

            # --- Event Listeners ---
            # Listener to update length param UI
            content_type_input.change(
                fn=update_length_param_ui,
                inputs=content_type_input,
                outputs=length_param_input
            )
            # Listener for topic generation button
            generate_topics_button.click(
                 fn=generate_topics_ui,
                 inputs=[ # Pass necessary inputs for topic generation
                     num_topics_input, model_input_content, # Use this tab's model selector
                     temperature_slider, top_p_slider, max_tokens_slider
                 ],
                 outputs=topic_input_content # Output generated topics to the textbox
            )
            # Listener for main generation button
            generate_content_button.click(
                fn=run_bulk_content_generation_and_prepare_json, # Use the new bulk wrapper
                inputs=[
                    topic_input_content, content_type_input, length_param_input,
                    model_input_content,
                    temperature_slider, top_p_slider, max_tokens_slider
                ],
                outputs=[output_content, download_file_content]
            )


# Launch the Gradio app
if __name__ == "__main__":
    print("Launching Gradio App...")
    print("Make sure the OPENROUTER_API_KEY environment variable is set.")
    # Use share=True for temporary public link if running locally and need to test
    demo.launch() # share=True