app1.py

#!/usr/bin/env python3
"""
Voiceover Generator with MiniCPM-o-2.6 (CPU Optimized - No Whisper)
"""

import gradio as gr
import torch
import tempfile
import random
import numpy as np
from pathlib import Path
import warnings
warnings.filterwarnings('ignore')

# ============================================================
# CONFIGURATION
# ============================================================
OUTPUT_DIR = Path("./generated_voiceovers")
OUTPUT_DIR.mkdir(exist_ok=True)

VOICE_STYLES = {
    "1": {"name": "Professional Narrator", "emotion": "neutral", "speed": 0.9},
    "2": {"name": "Enthusiastic YouTuber", "emotion": "happy", "speed": 1.1},
    "3": {"name": "Calm Teacher", "emotion": "neutral", "speed": 0.8},
    "4": {"name": "Energetic Announcer", "emotion": "happy", "speed": 1.2},
    "5": {"name": "Deep Documentary Voice", "emotion": "neutral", "speed": 0.75},
    "6": {"name": "Friendly Explainer", "emotion": "happy", "speed": 1.0},
}

# Templates for voiceover text
INTROS = ["Watch how", "See how", "Discover how", "Learn how", "Ever wondered how"]
OUTROS = ["That's engineering in action!", "Simple mechanics, powerful results!", "That's how industry gets things done!"]

ACTION_DESCS = {
    "increasing torque": ["multiplies rotational force", "boosts pulling power"],
    "reducing speed": ["controls motion safely", "slows down rotation"],
    "transferring power": ["sends energy efficiently", "transmits mechanical force"],
    "lifting heavy loads": ["raises massive weights", "hoists heavy objects"],
    "driving conveyor belts": ["moves products along", "keeps production flowing"],
}

# ============================================================
# LOAD MODEL (WITHOUT WHISPER)
# ============================================================
_model = None
_tokenizer = None

def load_model():
    """Load MiniCPM-o-2.6 without Whisper dependencies"""
    global _model, _tokenizer
    
    if _model is not None:
        return _model, _tokenizer
    
    print("🔄 Loading MiniCPM-o-2.6 model...")
    
    try:
        from transformers import AutoModel, AutoTokenizer
        
        # Load with specific config to avoid Whisper
        _model = AutoModel.from_pretrained(
            'openbmb/MiniCPM-o-2_6',
            trust_remote_code=True,
            torch_dtype=torch.float32,
            low_cpu_mem_usage=True,
            use_safetensors=True
        )
        _model = _model.eval().to('cpu')
        
        _tokenizer = AutoTokenizer.from_pretrained(
            'openbmb/MiniCPM-o-2_6',
            trust_remote_code=True
        )
        
        # Initialize TTS
        if hasattr(_model, 'init_tts'):
            _model.init_tts()
        
        print("✅ Model loaded successfully!")
        return _model, _tokenizer
        
    except ImportError as e:
        print(f"❌ Import Error: {e}")
        print("💡 Run: pip install transformers==4.35.0")
        return None, None
    except Exception as e:
        print(f"❌ Model Error: {e}")
        return None, None

# ============================================================
# FALLBACK TTS (Edge TTS - Works without Whisper)
# ============================================================
def generate_audio_fallback(text, voice_style, output_path):
    """Use Edge TTS as fallback (always works)"""
    import asyncio
    import edge_tts
    
    voice_map = {
        "1": "en-US-JennyNeural",
        "2": "en-US-GuyNeural",
        "3": "en-GB-SoniaNeural",
        "4": "en-US-DavisNeural",
        "5": "en-US-ChristopherNeural",
        "6": "en-US-AnaNeural",
    }
    
    voice = voice_map.get(voice_style, "en-US-JennyNeural")
    
    async def generate():
        communicate = edge_tts.Communicate(text, voice)
        await communicate.save(output_path)
    
    asyncio.run(generate())
    return output_path

# ============================================================
# GENERATE VOICEOVER
# ============================================================
def generate_natural_voiceover(raw_prompt):
    """Convert technical prompt to natural voiceover"""
    if not raw_prompt:
        return ""
    
    parts = raw_prompt.split(" in ", 1)
    mechanism_action = parts[0]
    industry = parts[1].split(" -")[0] if len(parts) > 1 else "industrial setting"
    
    words = mechanism_action.split()
    mechanism = words[0] if words else "mechanism"
    action = " ".join(words[1:]) if len(words) > 1 else "operating"
    
    # Find action description
    action_desc = None
    for key, descs in ACTION_DESCS.items():
        if key in action.lower():
            action_desc = random.choice(descs)
            break
    
    if not action_desc:
        action_desc = f"{action} with precision and reliability"
    
    intro = random.choice(INTROS)
    outro = random.choice(OUTROS)
    article = "an" if mechanism[0].lower() in 'aeiou' else "a"
    
    return f"{intro} {article} {mechanism} {action} inside {article} {industry}. This clever mechanism {action_desc}. {outro}"

def process_prompt(prompt_text, voice_choice, auto_convert):
    """Main processing function"""
    
    if not prompt_text:
        return None, "⚠️ Please enter a prompt!", ""
    
    try:
        # Generate natural text
        if auto_convert:
            voiceover_text = generate_natural_voiceover(prompt_text)
        else:
            voiceover_text = prompt_text
        
        # Generate audio using Edge TTS (reliable fallback)
        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
            audio_path = generate_audio_fallback(voiceover_text, voice_choice, tmp.name)
        
        info = f"""
        ✅ Voiceover Generated!
        
        📝 Prompt: {prompt_text[:80]}...
        🎭 Voice: {VOICE_STYLES[voice_choice]['name']}
        """
        
        return audio_path, info, voiceover_text
        
    except Exception as e:
        return None, f"❌ Error: {str(e)}", ""

# ============================================================
# GRADIO INTERFACE
# ============================================================
def create_interface():
    with gr.Blocks(title="Voiceover Generator", theme=gr.themes.Soft()) as demo:
        gr.Markdown("""
        # 🎙️ AI Voiceover Generator
        
        **Convert technical prompts to natural voiceovers**
        
        Example: `"planetary gear increasing torque in automotive plant"`
        """)
        
        with gr.Row():
            with gr.Column():
                prompt_input = gr.Textbox(
                    label="Technical Prompt",
                    placeholder="planetary gear increasing torque in automotive plant",
                    lines=3
                )
                
                voice_dropdown = gr.Dropdown(
                    choices=list(VOICE_STYLES.keys()),
                    label="Voice Style",
                    value="1"
                )
                
                auto_convert = gr.Checkbox(label="Auto-convert to natural voiceover", value=True)
                
                generate_btn = gr.Button("Generate Voiceover", variant="primary")
            
            with gr.Column():
                audio_output = gr.Audio(label="Generated Voiceover")
                info_output = gr.Textbox(label="Status", lines=5)
                text_output = gr.Textbox(label="Voiceover Script", lines=4)
        
        generate_btn.click(
            fn=process_prompt,
            inputs=[prompt_input, voice_dropdown, auto_convert],
            outputs=[audio_output, info_output, text_output]
        )
    
    return demo

# ============================================================
# MAIN
# ============================================================
if __name__ == "__main__":
    print("=" * 60)
    print("🎙️ Voiceover Generator")
    print("=" * 60)
    
    # Install edge-tts if not present
    try:
        import edge_tts
    except ImportError:
        print("📦 Installing edge-tts...")
        import subprocess
        subprocess.check_call(['pip', 'install', 'edge-tts'])
    
    demo = create_interface()
    demo.launch(
        server_name="0.0.0.0",
        server_port=7860,
        share=True
    )