Update app.py

app.py

@@ -13,30 +13,22 @@ from pydub import AudioSegment
 from dotenv import load_dotenv
 import tempfile
 import spaces
-
-# Coqui TTS
 from TTS.api import TTS
 
-# ---------------------------------------------------------------------
-# Load Environment Variables
-# ---------------------------------------------------------------------
+# -----------------------------------------------------------
+# Initialization & Environment Setup
+# -----------------------------------------------------------
 load_dotenv()
 HF_TOKEN = os.getenv("HF_TOKEN")
 
-# ---------------------------------------------------------------------
-# Global Model Caches
-# ---------------------------------------------------------------------
+# -----------------------------------------------------------
+# Model Cache Management
+# -----------------------------------------------------------
 LLAMA_PIPELINES = {}
 MUSICGEN_MODELS = {}
 TTS_MODELS = {}
 
-# ---------------------------------------------------------------------
-# Helper Functions
-# ---------------------------------------------------------------------
 def get_llama_pipeline(model_id: str, token: str):
-    """
-    Returns a cached LLaMA pipeline if available; otherwise, loads it.
-    """
     if model_id in LLAMA_PIPELINES:
         return LLAMA_PIPELINES[model_id]
 
@@ -52,339 +44,351 @@ def get_llama_pipeline(model_id: str, token: str):
     LLAMA_PIPELINES[model_id] = text_pipeline
     return text_pipeline
 
-
 def get_musicgen_model(model_key: str = "facebook/musicgen-large"):
-    """
-    Returns a cached MusicGen model if available; otherwise, loads it.
-    Uses the 'large' variant for higher quality outputs.
-    """
     if model_key in MUSICGEN_MODELS:
         return MUSICGEN_MODELS[model_key]
 
     model = MusicgenForConditionalGeneration.from_pretrained(model_key)
     processor = AutoProcessor.from_pretrained(model_key)
-
     device = "cuda" if torch.cuda.is_available() else "cpu"
     model.to(device)
     MUSICGEN_MODELS[model_key] = (model, processor)
     return model, processor
 
-
 def get_tts_model(model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
-    """
-    Returns a cached TTS model if available; otherwise, loads it.
-    """
     if model_name in TTS_MODELS:
         return TTS_MODELS[model_name]
-
     tts_model = TTS(model_name)
     TTS_MODELS[model_name] = tts_model
     return tts_model
 
-
-# ---------------------------------------------------------------------
-# Script Generation Function
-# ---------------------------------------------------------------------
+# -----------------------------------------------------------
+# Core Functionality
+# -----------------------------------------------------------
 @spaces.GPU(duration=100)
 def generate_script(user_prompt: str, model_id: str, token: str, duration: int):
-    """
-    Generates a script, sound design suggestions, and music ideas from a user prompt.
-    Returns a tuple of strings: (voice_script, sound_design, music_suggestions).
-    """
     try:
         text_pipeline = get_llama_pipeline(model_id, token)
-
-        system_prompt = (
-            "You are an expert radio imaging producer specializing in sound design and music. "
-            f"Based on the user's concept and the selected duration of {duration} seconds, produce the following: "
-            "1. A concise voice-over script. Prefix this section with 'Voice-Over Script:'.\n"
-            "2. Suggestions for sound design. Prefix this section with 'Sound Design Suggestions:'.\n"
-            "3. Music styles or track recommendations. Prefix this section with 'Music Suggestions:'."
-        )
-        combined_prompt = f"{system_prompt}\nUser concept: {user_prompt}\nOutput:"
-
+        system_prompt = f"""You are a professional audio producer creating {duration}-second content. Generate:
+1. Voice script (clear and concise)
+2. Sound design suggestions (specific effects)
+3. Music style recommendations (genre, tempo)"""
+        
+        full_prompt = f"{system_prompt}\nClient brief: {user_prompt}\nOutput:"
+        
         with torch.inference_mode():
             result = text_pipeline(
-                combined_prompt,
-                max_new_tokens=300,
+                full_prompt,
+                max_new_tokens=400,
                 do_sample=True,
-                temperature=0.8
+                temperature=0.7,
+                top_p=0.9
             )
 
-        generated_text = result[0]["generated_text"]
-        if "Output:" in generated_text:
-            generated_text = generated_text.split("Output:")[-1].strip()
-
-        # Default placeholders
-        voice_script = "No voice-over script found."
-        sound_design = "No sound design suggestions found."
-        music_suggestions = "No music suggestions found."
-
-        # Voice-Over Script
-        if "Voice-Over Script:" in generated_text:
-            parts = generated_text.split("Voice-Over Script:")
-            voice_script_part = parts[1]
-            if "Sound Design Suggestions:" in voice_script_part:
-                voice_script = voice_script_part.split("Sound Design Suggestions:")[0].strip()
-            else:
-                voice_script = voice_script_part.strip()
-
-        # Sound Design
-        if "Sound Design Suggestions:" in generated_text:
-            parts = generated_text.split("Sound Design Suggestions:")
-            sound_design_part = parts[1]
-            if "Music Suggestions:" in sound_design_part:
-                sound_design = sound_design_part.split("Music Suggestions:")[0].strip()
-            else:
-                sound_design = sound_design_part.strip()
-
-        # Music Suggestions
-        if "Music Suggestions:" in generated_text:
-            parts = generated_text.split("Music Suggestions:")
-            music_suggestions = parts[1].strip()
-
-        return voice_script, sound_design, music_suggestions
+        generated_text = result[0]["generated_text"].split("Output:")[-1].strip()
+        
+        # Parse sections
+        sections = {
+            "Voice-Over Script:": "",
+            "Sound Design Suggestions:": "",
+            "Music Suggestions:": ""
+        }
+        
+        current_section = None
+        for line in generated_text.split('\n'):
+            for section in sections:
+                if section in line:
+                    current_section = section
+                    line = line.replace(section, '').strip()
+            if current_section:
+                sections[current_section] += line + '\n'
+        
+        return (
+            sections["Voice-Over Script:"].strip() or "No script generated",
+            sections["Sound Design Suggestions:"].strip() or "No sound design suggestions",
+            sections["Music Suggestions:"].strip() or "No music suggestions"
+        )
 
     except Exception as e:
-        return f"Error generating script: {e}", "", ""
-
+        return f"Error: {str(e)}", "", ""
 
-# ---------------------------------------------------------------------
-# Voice-Over Generation Function
-# ---------------------------------------------------------------------
 @spaces.GPU(duration=100)
-def generate_voice(script: str, tts_model_name: str = "tts_models/en/ljspeech/tacotron2-DDC"):
-    """
-    Generates a voice-over from the provided script using the Coqui TTS model.
-    Returns the file path to the generated .wav file.
-    """
+def generate_voice(script: str, tts_model_name: str):
     try:
         if not script.strip():
-            return "Error: No script provided."
-
+            return None
         tts_model = get_tts_model(tts_model_name)
-
-        # Generate and save voice
-        output_path = os.path.join(tempfile.gettempdir(), "voice_over.wav")
+        output_path = f"{tempfile.gettempdir()}/voice_temp.wav"
         tts_model.tts_to_file(text=script, file_path=output_path)
         return output_path
-
     except Exception as e:
-        return f"Error generating voice: {e}"
-
+        print(f"Voice generation error: {e}")
+        return None
 
-# ---------------------------------------------------------------------
-# Music Generation Function
-# ---------------------------------------------------------------------
 @spaces.GPU(duration=100)
 def generate_music(prompt: str, audio_length: int):
-    """
-    Generates music from the 'facebook/musicgen-large' model based on the prompt.
-    Returns the file path to the generated .wav file.
-    """
     try:
-        if not prompt.strip():
-            return "Error: No music suggestion provided."
-
-        model_key = "facebook/musicgen-large"
-        musicgen_model, musicgen_processor = get_musicgen_model(model_key)
-
+        model, processor = get_musicgen_model()
         device = "cuda" if torch.cuda.is_available() else "cpu"
-        inputs = musicgen_processor(text=[prompt], padding=True, return_tensors="pt").to(device)
-
+        inputs = processor(text=[prompt], padding=True, return_tensors="pt").to(device)
+        
         with torch.inference_mode():
-            outputs = musicgen_model.generate(**inputs, max_new_tokens=audio_length)
-
+            outputs = model.generate(**inputs, max_new_tokens=audio_length)
+        
         audio_data = outputs[0, 0].cpu().numpy()
         normalized_audio = (audio_data / max(abs(audio_data)) * 32767).astype("int16")
-
-        output_path = f"{tempfile.gettempdir()}/musicgen_large_generated_music.wav"
+        output_path = f"{tempfile.gettempdir()}/music_temp.wav"
         write(output_path, 44100, normalized_audio)
-
         return output_path
-
     except Exception as e:
-        return f"Error generating music: {e}"
-
+        print(f"Music generation error: {e}")
+        return None
 
-# ---------------------------------------------------------------------
-# Audio Blending with Duration Sync & Ducking
-# ---------------------------------------------------------------------
 @spaces.GPU(duration=100)
-def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int = 10):
-    """
-    Blends two audio files (voice and music).
-    1. If music < voice, loops the music until it meets/exceeds the voice duration.
-    2. If music > voice, trims music to the voice duration.
-    3. If ducking=True, the music is attenuated by 'duck_level' dB while the voice is playing.
-    Returns the file path to the blended .wav file.
-    """
+def blend_audio(voice_path: str, music_path: str, ducking: bool, duck_level: int):
     try:
-        if not os.path.isfile(voice_path) or not os.path.isfile(music_path):
-            return "Error: Missing audio files for blending."
-
         voice = AudioSegment.from_wav(voice_path)
         music = AudioSegment.from_wav(music_path)
-
-        voice_len = len(voice)  # in milliseconds
-        music_len = len(music)  # in milliseconds
-
-        # 1) If the music is shorter than the voice, loop it:
-        if music_len < voice_len:
-            looped_music = AudioSegment.empty()
-            # Keep appending until we exceed voice length
-            while len(looped_music) < voice_len:
-                looped_music += music
-            music = looped_music
-
-        # 2) If the music is longer than the voice, truncate it:
-        if len(music) > voice_len:
-            music = music[:voice_len]
-
-        # Now music and voice are the same length
+        
+        # Adjust music length
+        if len(music) < len(voice):
+            loops_needed = (len(voice) // len(music)) + 1
+            music = music * loops_needed
+        music = music[:len(voice)]
+        
+        # Ducking effect
         if ducking:
-            # Step 1: Reduce music dB while voice is playing
             ducked_music = music - duck_level
-            # Step 2: Overlay voice on top of ducked music
             final_audio = ducked_music.overlay(voice)
         else:
-            # No ducking, just overlay
             final_audio = music.overlay(voice)
-
-        output_path = os.path.join(tempfile.gettempdir(), "blended_output.wav")
+        
+        output_path = f"{tempfile.gettempdir()}/final_mix.wav"
         final_audio.export(output_path, format="wav")
         return output_path
-
     except Exception as e:
-        return f"Error blending audio: {e}"
-
-
-# ---------------------------------------------------------------------
-# Gradio Interface
-# ---------------------------------------------------------------------
-with gr.Blocks() as demo:
-    gr.Markdown("""
-# 🎧 AI Promo Studio
-Welcome to **AI Promo Studio**, your all-in-one solution for creating professional, engaging audio promos with minimal effort!  
-
-This next-generation platform uses powerful AI models to handle:
-- **Script Generation**: Craft concise and impactful copy with LLaMA.
-- **Voice Synthesis**: Convert text into natural-sounding voice-overs using Coqui TTS.
-- **Music Production**: Generate custom music tracks with MusicGen Large for sound bed.
-- **Seamless Blending**: Easily combine voice and music—loop or trim tracks to match your desired promo length, with optional ducking to keep the voice front and center.
-
-Whether you’re a radio producer, podcaster, or content creator, **AI Promo Studio** streamlines your entire production pipeline—cutting hours of manual editing down to a few clicks.
-""")
-
-
-    with gr.Tabs():
-        # Step 1: Generate Script
-        with gr.Tab("Step 1: Generate Script"):
-            with gr.Row():
-                user_prompt = gr.Textbox(
-                    label="Promo Idea", 
-                    placeholder="E.g., A 30-second promo for a morning show...",
-                    lines=2
-                )
-                llama_model_id = gr.Textbox(
-                    label="LLaMA Model ID", 
-                    value="meta-llama/Meta-Llama-3-8B-Instruct", 
-                    placeholder="Enter a valid Hugging Face model ID"
-                )
-                duration = gr.Slider(
-                    label="Desired Promo Duration (seconds)",
-                    minimum=15, 
-                    maximum=60, 
-                    step=15, 
-                    value=30
-                )
-
-            generate_script_button = gr.Button("Generate Script")
-            script_output = gr.Textbox(label="Generated Voice-Over Script", lines=5, interactive=False)
-            sound_design_output = gr.Textbox(label="Sound Design Suggestions", lines=3, interactive=False)
-            music_suggestion_output = gr.Textbox(label="Music Suggestions", lines=3, interactive=False)
-
-            generate_script_button.click(
-                fn=lambda user_prompt, model_id, dur: generate_script(user_prompt, model_id, HF_TOKEN, dur),
-                inputs=[user_prompt, llama_model_id, duration],
-                outputs=[script_output, sound_design_output, music_suggestion_output],
-            )
-
-        # Step 2: Generate Voice
-        with gr.Tab("Step 2: Generate Voice"):
-            gr.Markdown("Generate the voice-over using a Coqui TTS model.")
-            selected_tts_model = gr.Dropdown(
-                label="TTS Model",
-                choices=[
-                    "tts_models/en/ljspeech/tacotron2-DDC",  
-                    "tts_models/en/ljspeech/vits", 
-                    "tts_models/en/sam/tacotron-DDC", 
-                ],
-                value="tts_models/en/ljspeech/tacotron2-DDC",
-                multiselect=False
-            )
-            generate_voice_button = gr.Button("Generate Voice-Over")
-            voice_audio_output = gr.Audio(label="Voice-Over (WAV)", type="filepath")
-
-            generate_voice_button.click(
-                fn=lambda script, tts_model: generate_voice(script, tts_model),
-                inputs=[script_output, selected_tts_model],
-                outputs=voice_audio_output,
-            )
-
-        # Step 3: Generate Music (MusicGen Large)
-        with gr.Tab("Step 3: Generate Music"):
-            gr.Markdown("Generate a music track with the **MusicGen Large** model.")
-            audio_length = gr.Slider(
-                label="Music Length (tokens)",
-                minimum=128, 
-                maximum=1024, 
-                step=64, 
-                value=512,
-                info="Increase tokens for longer audio, but be mindful of inference time."
-            )
-            generate_music_button = gr.Button("Generate Music")
-            music_output = gr.Audio(label="Generated Music (WAV)", type="filepath")
-
-            generate_music_button.click(
-                fn=lambda music_suggestion, length: generate_music(music_suggestion, length),
-                inputs=[music_suggestion_output, audio_length],
-                outputs=[music_output],
-            )
-
-        # Step 4: Blend Audio (Loop/Trim + Ducking)
-        with gr.Tab("Step 4: Blend Audio"):
-            gr.Markdown("**Music** will be looped or trimmed to match **Voice** duration, then optionally ducked.")
-            ducking_checkbox = gr.Checkbox(label="Enable Ducking?", value=True)
-            duck_level_slider = gr.Slider(
-                label="Ducking Level (dB attenuation)", 
-                minimum=0, 
-                maximum=20, 
-                step=1, 
-                value=10
-            )
-            blend_button = gr.Button("Blend Voice + Music")
-            blended_output = gr.Audio(label="Final Blended Output (WAV)", type="filepath")
-
-            blend_button.click(
-                fn=blend_audio,
-                inputs=[voice_audio_output, music_output, ducking_checkbox, duck_level_slider],
-                outputs=blended_output
-            )
-
-    # Footer
-    gr.Markdown("""
-    <hr>
-    <p style="text-align: center; font-size: 0.9em;">
-        Created with ❤️ by <a href="https://bilsimaging.com" target="_blank">bilsimaging.com</a>
-    </p>
-    """)
+        print(f"Mixing error: {e}")
+        return None
+
+# -----------------------------------------------------------
+# Enhanced UI Components
+# -----------------------------------------------------------
+custom_css = """
+#main-container {
+    max-width: 1200px;
+    margin: 0 auto;
+    padding: 20px;
+    background: #f5f5f5;
+    border-radius: 15px;
+    box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+}
+
+.header {
+    text-align: center;
+    padding: 2em;
+    background: linear-gradient(135deg, #2b5876 0%, #4e4376 100%);
+    color: white;
+    border-radius: 15px;
+    margin-bottom: 2em;
+}
+
+.tab-nav {
+    background: none !important;
+    border: none !important;
+}
+
+.tab-button {
+    padding: 1em 2em !important;
+    border-radius: 8px !important;
+    margin: 0 5px !important;
+    transition: all 0.3s ease !important;
+}
+
+.tab-button:hover {
+    transform: translateY(-2px);
+    box-shadow: 0 3px 6px rgba(0,0,0,0.1);
+}
+
+.dark-btn {
+    background: linear-gradient(135deg, #434343 0%, #000000 100%) !important;
+    color: white !important;
+    border: none !important;
+    padding: 12px 24px !important;
+    border-radius: 8px !important;
+}
+
+.output-card {
+    background: white !important;
+    border-radius: 10px !important;
+    padding: 20px !important;
+    box-shadow: 0 2px 4px rgba(0,0,0,0.05) !important;
+}
+
+.progress-indicator {
+    color: #666;
+    font-style: italic;
+    margin-top: 10px;
+}
+"""
+
+with gr.Blocks(css=custom_css, theme=gr.themes.Default()) as demo:
+    with gr.Column(elem_id="main-container"):
+        # Header Section
+        with gr.Column(elem_classes="header"):
+            gr.Markdown("""
+            # 🎙️ AI Promo Studio
+            **Professional Audio Production Suite Powered by AI**
+            """)
+        
+        # Main Workflow Tabs
+        with gr.Tabs(elem_classes="tab-nav"):
+            # Script Generation
+            with gr.Tab("📝 Script Design", elem_classes="tab-button"):
+                with gr.Row(equal_height=False):
+                    with gr.Column(scale=2):
+                        gr.Markdown("### 🎯 Project Brief")
+                        user_prompt = gr.Textbox(
+                            label="Describe your promo concept",
+                            placeholder="e.g., 'An intense 30-second movie trailer intro with epic orchestral music and dramatic sound effects...'",
+                            lines=4
+                        )
+                        with gr.Row():
+                            duration = gr.Slider(
+                                label="Duration (seconds)",
+                                minimum=15,
+                                maximum=120,
+                                step=15,
+                                value=30,
+                                interactive=True
+                            )
+                            llama_model_id = gr.Dropdown(
+                                label="AI Model",
+                                choices=["meta-llama/Meta-Llama-3-8B-Instruct"],
+                                value="meta-llama/Meta-Llama-3-8B-Instruct",
+                                interactive=True
+                            )
+                        generate_btn = gr.Button("Generate Script 🚀", elem_classes="dark-btn")
+                    
+                    with gr.Column(scale=1, elem_classes="output-card"):
+                        gr.Markdown("### 📄 Generated Content")
+                        script_output = gr.Textbox(label="Voice Script", lines=6)
+                        sound_design_output = gr.Textbox(label="Sound Design", lines=3)
+                        music_suggestion_output = gr.Textbox(label="Music Style", lines=3)
+
+            # Voice Production
+            with gr.Tab("🎙️ Voice Production", elem_classes="tab-button"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        gr.Markdown("### 🔊 Voice Settings")
+                        tts_model = gr.Dropdown(
+                            label="Voice Model",
+                            choices=[
+                                "tts_models/en/ljspeech/tacotron2-DDC",
+                                "tts_models/en/ljspeech/vits",
+                                "tts_models/en/sam/tacotron-DDC"
+                            ],
+                            value="tts_models/en/ljspeech/tacotron2-DDC",
+                            interactive=True
+                        )
+                        with gr.Row():
+                            voice_preview_btn = gr.Button("Preview Sample", elem_classes="dark-btn")
+                            voice_generate_btn = gr.Button("Generate Full Voiceover", elem_classes="dark-btn")
+                    with gr.Column(scale=1, elem_classes="output-card"):
+                        gr.Markdown("### 🎧 Voice Preview")
+                        voice_audio = gr.Audio(
+                            label="Generated Voice",
+                            interactive=False,
+                            waveform_options={"show_controls": True}
+                        )
+
+            # Music Production
+            with gr.Tab("🎵 Music Design", elem_classes="tab-button"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        gr.Markdown("### 🎹 Music Parameters")
+                        audio_length = gr.Slider(
+                            label="Generation Length",
+                            minimum=256,
+                            maximum=1024,
+                            step=64,
+                            value=512,
+                            info="Higher values = longer generation time"
+                        )
+                        music_generate_btn = gr.Button("Generate Music Track", elem_classes="dark-btn")
+                    with gr.Column(scale=1, elem_classes="output-card"):
+                        gr.Markdown("### 🎶 Music Preview")
+                        music_output = gr.Audio(
+                            label="Generated Music",
+                            interactive=False,
+                            waveform_options={"show_controls": True}
+                        )
+
+            # Final Mix
+            with gr.Tab("🔊 Final Mix", elem_classes="tab-button"):
+                with gr.Row():
+                    with gr.Column(scale=1):
+                        gr.Markdown("### 🎚️ Mixing Console")
+                        ducking_enabled = gr.Checkbox(
+                            label="Enable Voice Ducking",
+                            value=True,
+                            info="Automatically lower music during voice segments"
+                        )
+                        duck_level = gr.Slider(
+                            label="Ducking Intensity (dB)",
+                            minimum=3,
+                            maximum=20,
+                            step=1,
+                            value=10
+                        )
+                        mix_btn = gr.Button("Generate Final Mix", elem_classes="dark-btn")
+                    with gr.Column(scale=1, elem_classes="output-card"):
+                        gr.Markdown("### 🎧 Final Production")
+                        final_mix = gr.Audio(
+                            label="Mixed Output",
+                            interactive=False,
+                            waveform_options={"show_controls": True}
+                        )
+
+        # Footer
+        with gr.Column(elem_classes="output-card"):
+            gr.Markdown("""
+            <div style="text-align: center; padding: 1.5em 0;">
+                <a href="https://bilsimaging.com" target="_blank">
+                    <img src="https://bilsimaging.com/logo.png" alt="Bils Imaging" style="height: 35px; margin-right: 15px;">
+                </a>
+                <a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold">
+                    <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold&countColor=%23263759" />
+                </a>
+            </div>
+            <p style="text-align: center; color: #666; font-size: 0.9em;">
+                Professional Audio Production Suite v2.1 © 2024 | Bils Imaging
+            </p>
+            """)
+
+    # Event Handling
+    generate_btn.click(
+        generate_script,
+        inputs=[user_prompt, llama_model_id, gr.Textbox(HF_TOKEN, visible=False), duration],
+        outputs=[script_output, sound_design_output, music_suggestion_output]
+    )
+    
+    voice_generate_btn.click(
+        generate_voice,
+        inputs=[script_output, tts_model],
+        outputs=voice_audio
+    )
     
-    # Visitor Badge
-    gr.HTML("""
-    <a href="https://visitorbadge.io/status?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold">
-        <img src="https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fhuggingface.co%2Fspaces%2FBils%2Fradiogold&countColor=%23263759" />
-    </a>
-    """)
+    music_generate_btn.click(
+        generate_music,
+        inputs=[music_suggestion_output, audio_length],
+        outputs=music_output
+    )
+    
+    mix_btn.click(
+        blend_audio,
+        inputs=[voice_audio, music_output, ducking_enabled, duck_level],
+        outputs=final_mix
+    )
 
-demo.launch(debug=True)
+if __name__ == "__main__":
+    demo.launch(debug=True)