"""
Transcriptinator - HuggingFace Spaces Gradio Interface
Audio transcription with Gemini + OpenRouter
"""

import gradio as gr
import os
from transcribe_core import process_audio_file, get_audio_duration
from ai_providers import GeminiProvider, OpenRouterProvider

# Establish absolute paths for Hugging Face Spaces compatibility
CURRENT_DIR = os.path.dirname(os.path.abspath(__file__))
OUTPUT_FOLDER = os.path.join(CURRENT_DIR, "outputs")

def transcribe_audio(audio_file, gemini_key, openrouter_key, model_name):
    """
    Main transcription function for Gradio interface.
    """
    if not audio_file:
        return "❌ Please upload an audio file.", None
    
    if not gemini_key or len(gemini_key.strip()) < 10:
        return "❌ Please provide a valid Gemini API key.", None
    
    try:
        # Create Gemini provider for transcription
        gemini_provider = GeminiProvider(gemini_key, model_name)
        
        # Create OpenRouter provider for summary/ideas (optional)
        openrouter_provider = None
        if openrouter_key and len(openrouter_key.strip()) > 10:
            openrouter_provider = OpenRouterProvider(openrouter_key)
        
        # Get audio duration and file size for estimate
        duration = get_audio_duration(audio_file)
        duration_min = duration / 60
        file_size_mb = os.path.getsize(audio_file) / (1024 * 1024)
        
        # Process the audio file - ensure this function in core uses absolute paths
        output_path, is_zip = process_audio_file(
            audio_file,
            gemini_provider,
            openrouter_provider,
            progress_callback=lambda msg, progress: None
        )
        
        # Determine file type for success message
        if str(is_zip) == "True":
            file_type = "ZIP archive"
            file_desc = "Multiple transcript files (chunked audio)"
        else:
            file_type = "Markdown file"
            file_desc = "Single transcript file"
        
        text_provider = "OpenRouter (DeepSeek R1)" if openrouter_provider else "Gemini"
        
        success_msg = f"""✅ **Transcription Complete!**

📝 Original file: {os.path.basename(audio_file)}
⏱️ Duration: {duration_min:.1f} minutes
💾 Size: {file_size_mb:.1f} MB
🎙️ Transcription: Gemini ({model_name})
💡 Summary/Ideas: {text_provider}
📄 Output: {file_type}

{file_desc}

Click below to download your transcript(s)."""
        
        # Return the absolute file path - Gradio handles the download via proxy
        return success_msg, output_path
        
    except Exception as e:
        error_msg = f"""❌ **Error during transcription:**

{str(e)}

**Common issues:**
- Invalid API key
- Audio file too large or corrupted
- Network connection issues"""
        return error_msg, None


# Create Gradio interface
with gr.Blocks(title="Transcriptinator", theme=gr.themes.Soft()) as app:
    gr.Markdown("""
    # 🎙️ Transcriptinator
    ### AI-Powered Audio Transcription
    
    **Powered by:** Gemini (transcription) + OpenRouter DeepSeek R1 (summarization)
    """)
    
    with gr.Row():
        with gr.Column(scale=2):
            # Audio upload
            audio_input = gr.Audio(
                label="Upload Audio File",
                type="filepath",
                sources=["upload"],
            )
            
            gr.Markdown("""
            **Supported formats:** MP3, WAV, M4A, OGG, FLAC, WEBM  
            **Large files (>30MB):** Automatically chunked and processed
            """)
            
            # Model selection
            model_dropdown = gr.Dropdown(
                choices=list(GeminiProvider.AVAILABLE_MODELS.keys()),
                value="Gemini 2.5 Flash",
                label="Gemini Model",
                info="Select which Gemini model to use for transcription"
            )
            
            # API keys
            gemini_key_input = gr.Textbox(
                label="Gemini API Key (Required)",
                placeholder="Enter your Gemini API key...",
                type="password",
                info="Get one free at: https://aistudio.google.com/app/apikey"
            )
            
            openrouter_key_input = gr.Textbox(
                label="OpenRouter API Key (Optional)",
                placeholder="Enter your OpenRouter key for better summaries...",
                type="password",
                info="Leave empty to use Gemini for all tasks | Get free at: https://openrouter.ai"
            )
            
            # Submit button
            submit_btn = gr.Button("🚀 Transcribe Audio", variant="primary", size="lg")
        
        with gr.Column(scale=1):
            # Status output
            status_output = gr.Markdown(label="Status")
            
            # Download component - removed 'interactive=False' for better stability
            download_output = gr.File(label="📥 Download Transcript")
    
    # Information section ... (remains unchanged)
    gr.Markdown("""
    ---
    ### 🎯 What you'll get:
    - 📝 **Full transcription** with timestamps and speaker detection
    - 📊 **Summary** in 2-3 sentences
    - 💡 **Key ideas** with descriptions
    - 📄 **Markdown file** ready to download
    """)
    
    # Connect the transcription function
    submit_btn.click(
        fn=transcribe_audio,
        inputs=[audio_input, gemini_key_input, openrouter_key_input, model_dropdown],
        outputs=[status_output, download_output]
    )

# Launch the app with queuing and allowed_paths for file access
if __name__ == "__main__":
    os.makedirs(OUTPUT_FOLDER, exist_ok=True)
    app.queue().launch(allowed_paths=[OUTPUT_FOLDER])