import gradio as gr
import requests
import os
import time

ASR_API_URL = os.getenv('ASR_API_URL')
AUTH_TOKEN = os.getenv('AUTH_TOKEN')

def transcribe_audio(file_path):
    # بررسی اینکه فایل وجود دارد
    if file_path is None:
        return "❌ Error: Please upload or record an audio file.", ""
    
    if not ASR_API_URL or not AUTH_TOKEN:
        return "❌ Error: ASR_API_URL or AUTH_TOKEN is not set.", ""
    
    headers = {
        'accept': 'application/json',
        'Authorization': f'Bearer {AUTH_TOKEN}',
    }
    
    start_time = time.time()
    
    try:
        # استفاده از with برای مدیریت بهتر فایل
        with open(file_path, 'rb') as audio_file:
            files = {
                'file': (os.path.basename(file_path), audio_file, 'audio/mpeg'),
            }
            response = requests.post(ASR_API_URL, headers=headers, files=files, timeout=60)
            
    except FileNotFoundError:
        return f"❌ Error: File not found: {file_path}", ""
    except requests.exceptions.Timeout:
        return "❌ Error: Request timeout. Please try again.", ""
    except requests.exceptions.ConnectionError:
        return "❌ Error: Connection failed. Please check your internet connection.", ""
    except Exception as e:
        return f"❌ Error: {str(e)}", ""
    
    inference_time = time.time() - start_time

    if response.status_code == 200:
        try:
            res = response.json()
            transcription = res.get("transcription", "No transcription returned.")
            inference_time_str = f"{res.get('time', inference_time):.2f} seconds"
            return transcription, inference_time_str
        except ValueError:
            return "❌ Error: Invalid response format from server.", ""
    else:
        error_msg = f"❌ Error: {response.status_code}"
        try:
            error_detail = response.json().get('detail', response.text)
            error_msg += f", {error_detail}"
        except:
            error_msg += f", {response.text[:200]}"  # محدود کردن طول پیام خطا
        return error_msg, ""

with gr.Blocks(css="""
#gooya-title {color:white; background: linear-gradient(90deg, #224CA5 0%, #2CD8D5 100%); border-radius: 12px; padding:20px 10px;margin-bottom:12px;}
.gooya-badge {display:inline-block; background:#224CA5; color:#fff; border-radius:16px; padding:6px 16px; font-size:0.97rem; margin-top:4px;}
#gooya-box {background:#F7FAFF; border:1px solid #e7e9ef; border-radius:14px; padding:22px 18px; margin-top:12px;}
""") as demo:
    gr.HTML("""<div id="gooya-title">
    <h1 style='margin-bottom:10px;font-weight:800;font-size:2rem;'>Gooya ASR <span style="font-size:1.1rem; font-weight:400; opacity:0.8;">v1.4</span></h1>
    <p style='font-size:1.12rem; margin-bottom:2px;'>High-performance Persian Speech-to-Text</p>
    <p style='font-size:0.98rem; color:#c6e8fa'>Upload or record a Persian audio file (max 30s) and instantly receive the transcription.</p>
    </div>""")

    with gr.Row():
        with gr.Column():
            audio = gr.Audio(
                label="Audio Input (Upload or record, up to 30s)",
                type="filepath",
                show_label=True,
                sources=["upload", "microphone"]
            )
        with gr.Column():
            inference_time = gr.Label(label="⏱️ Processing Time", elem_classes="gooya-badge")
            transcription = gr.Textbox(
                label="📝 Transcription",
                lines=5,
                show_copy_button=True,
                placeholder="The transcription will appear here...",
                elem_id="gooya-textbox"
            )

    with gr.Row():
        submit_btn = gr.Button("Transcribe", variant="primary")
        clear_btn = gr.Button("Clear", variant="secondary")

    gr.Markdown("""
**Instructions:**  
- Maximum audio length: **30 seconds**
- Input audio should be in Persian.
- The transcription and processing time will be displayed instantly.

For performance benchmarks, visit: [Persian ASR Leaderboard](https://huggingface.co/spaces/navidved/open_persian_asr_leaderboard)
""")

    submit_btn.click(
        transcribe_audio,
        inputs=audio,
        outputs=[transcription, inference_time]
    )
    
    # اصلاح دکمه Clear - سه خروجی بجای دو
    clear_btn.click(
        lambda: ("", "", None),  # اضافه کردن None برای audio
        None,
        [transcription, inference_time, audio]
    )

# تغییر share به False برای Hugging Face Spaces
if __name__ == "__main__":
    demo.launch(
        share=False,  # در Hugging Face Spaces باید False باشد
        server_name="0.0.0.0",
        server_port=7860
    )