Update app.py from anycoder

app.py

@@ -2,381 +2,330 @@ import gradio as gr
 import torch
 import os
 import time
-import copy
 from pathlib import Path
 from typing import Optional, Tuple
 import spaces
+import numpy as np
+from datetime import datetime, timedelta
 
-from vibevoice.modular.modeling_vibevoice_streaming_inference import (
-    VibeVoiceStreamingForConditionalGenerationInference,
-)
-from vibevoice.processor.vibevoice_streaming_processor import (
-    VibeVoiceStreamingProcessor,
-)
-
-
-class VoiceMapper:
-    """Maps speaker names to voice file paths"""
-
+# Mock classes for demonstration
+class MemorySystem:
+    """Persistent memory system for emotional intelligence"""
+    
     def __init__(self):
-        self.setup_voice_presets()
-
-        # Change name according to our preset voice file
-        new_dict = {}
-        for name, path in self.voice_presets.items():
-            if "_" in name:
-                name = name.split("_")[0]
-
-            if "-" in name:
-                name = name.split("-")[-1]
-
-            new_dict[name] = path
-        self.voice_presets.update(new_dict)
-
-    def setup_voice_presets(self):
-        """Setup voice presets by scanning the voices directory."""
-        voices_dir = os.path.join(os.path.dirname(__file__), "demo/voices/streaming_model")
-
-        # Check if voices directory exists
-        if not os.path.exists(voices_dir):
-            print(f"Warning: Voices directory not found at {voices_dir}")
-            self.voice_presets = {}
-            self.available_voices = {}
-            return
-
-        # Scan for all VOICE files in the voices directory
-        self.voice_presets = {}
+        self.user_profiles = {}
+        self.conversation_histories = {}
+        self.emotional_profiles = {}
+        
+    def get_user_memory(self, user_id: str) -> dict:
+        """Get user's memory profile"""
+        return self.user_profiles.get(user_id, {})
+    
+    def update_memory(self, user_id: str, interaction: dict):
+        """Update user memory with new interaction"""
+        if user_id not in self.user_profiles:
+            self.user_profiles[user_id] = {
+                "personality_type": "unknown",
+                "shadow_aspects": [],
+                "core_wounds": [],
+                "emotional_patterns": {},
+                "healing_journey": [],
+                "transformation_milestones": [],
+                "daily_micro_assessments": [],
+                "psychological_frameworks": {
+                    "integration_level": 0,
+                    "authenticity_score": 0,
+                    "fragmentation_index": 0,
+                }
+                
+    def get_user_personality_type(self, user_id: str) -> str:
+        """Determine user's personality type based on accumulated data"""
+        return "Enigma"  # Default personality type
 
-        # Get all .pt files in the voices directory
-        pt_files = [
-            f
-            for f in os.listdir(voices_dir)
-            if f.lower().endswith(".pt") and os.path.isfile(os.path.join(voices_dir, f))
+class NewMeAI:
+    """NewMe AI Persona with astrological personality and emotional intelligence"""
+    
+    def __init__(self, memory_system: MemorySystem):
+        self.memory = memory_system
+        self.conversation_styles = [
+            "Socratic questioning",
+            "Shadow work exploration", 
+            "Integration therapy",
+            "Authenticity assessment",
+            "Transformation guidance",
         ]
+        
+    def initiate_conversation(self, user_id: str) -> str:
+        """Initiate conversation based on user memory"""
+        memory = self.memory.get_user_memory(user_id)
+        
+        # NewMe's signature opening lines
+        opening_phrases = [
+            "The stars are particularly aligned for you today...",
+            "I sense a shift in your energetic field...",
+            "Your celestial chart reveals interesting patterns...",
+        ]
+        
+        return f"✨ NewMe: {np.random.choice(opening_phrases)}"
+        
+    def process_user_speech(self, audio_input: str) -> str:
+        """Process user speech and generate response"""
+        return "Processing your voice... NewMe is responding with astrological wisdom."
 
-        # Create dictionary with filename (without extension) as key
-        for pt_file in pt_files:
-            # Remove .pt extension to get the name
-            name = os.path.splitext(pt_file)[0]
-            # Create full path
-            full_path = os.path.join(voices_dir, pt_file)
-            self.voice_presets[name] = full_path
-
-        # Sort the voice presets alphabetically by name for better UI
-        self.voice_presets = dict(sorted(self.voice_presets.items()))
-
-        # Filter out voices that don't exist (this is now redundant but kept for safety)
-        self.available_voices = {
-            name: path for name, path in self.voice_presets.items() if os.path.exists(path)
-        }
-
-        print(f"Found {len(self.available_voices)} voice files in {voices_dir}")
-        print(f"Available voices: {', '.join(self.available_voices.keys())}")
-
-    def get_voice_path(self, speaker_name: str) -> str:
-        """Get voice file path for a given speaker name"""
-        # First try exact match
-        if speaker_name in self.voice_presets:
-            return self.voice_presets[speaker_name]
-
-        # Try partial matching (case insensitive)
-        speaker_lower = speaker_name.lower()
-        for preset_name, path in self.voice_presets.items():
-            if preset_name.lower() in speaker_lower or speaker_lower in preset_name.lower():
-                return path
-
-        # Default to first voice if no match found
-        default_voice = list(self.voice_presets.values())[0]
-        print(
-            f"Warning: No voice preset found for '{speaker_name}', using default voice: {default_voice}"
-        )
-        return default_voice
-
-
-# Load model and processor directly
-print("Loading VibeVoice-Realtime model...")
-
-MODEL_PATH = "microsoft/VibeVoice-Realtime-0.5B"
+class NewomenPlatform:
+    """Main platform orchestrating all components"""
+    
+    def __init__(self):
+        self.memory_system = MemorySystem()
+        self.newme_ai = NewMeAI(self.memory_system)
+        self.assessments = self._create_assessments()
+        
+    def _create_assessments(self) -> list:
+        """Create the 20+ assessments for authenticated users"""
+        return [
+            "Shadow Integration Assessment",
+            "Authenticity Meter Test",
+            "Psychological Fragmentation Index",
+            "Emotional Intelligence Scale",
+            "Astrological Personality Mapping",
+            "Core Wound Identification",
+            "Transformation Path Analysis",
+            "Relationship Compatibility Matrix",
+            "Life Purpose Alignment Quiz",
+            "Inner Child Connection Test",
+            "Karmic Pattern Recognition",
+            "Soul Contract Exploration",
+            "Past Life Regression Assessment",
+            "Chakra Balance Evaluation",
+            "Narrative Identity Exploration",
+            "Psychological Archetype Mapping",
+            "Emotional Blockage Detection",
+            "Spiritual Growth Assessment",
+            "Psychological Integration Scale",
+            "Authentic Self Alignment Test",
+            "Shadow Work Progress Meter",
+            "Transformation Journey Milestone Tracker",
+        ]
 
-# Load processor (CPU operation)
-PROCESSOR = VibeVoiceStreamingProcessor.from_pretrained(MODEL_PATH)
+# Load models and initialize platform
+print("🚀 Initializing Newomen Platform...")
 
-# Load model on CPU initially (will be moved to GPU by @spaces.GPU decorator)
-MODEL = VibeVoiceStreamingForConditionalGenerationInference.from_pretrained(
-    MODEL_PATH,
-    torch_dtype=torch.float16,
-    device_map="cpu",
-    attn_implementation="sdpa",
-)
+PLATFORM = NewomenPlatform()
 
-MODEL.eval()
-MODEL.set_ddpm_inference_steps(num_steps=5)
+print("✅ Platform initialized successfully!")
 
-# Initialize voice mapper
-VOICE_MAPPER = VoiceMapper()
 
-print("Model loaded successfully!")
+@spaces.GPU(duration=60)
+def process_user_interaction(
+    user_input: str,
+    voice_sample: Optional[str] = None,
+    user_id: str = "default_user"
+) -> Tuple[Optional[str], str]:
+    """
+    Process user interaction with NewMe AI
+    
+    Args:
+        user_input: User's text or voice input
+        voice_sample: Optional voice sample for personalized responses
+        user_id: Unique identifier for user
+    
+    Returns:
+        Tuple of (audio_response, status_message)
+    """
+    try:
+        # Get user memory
+        user_memory = PLATFORM.memory_system.get_user_memory(user_id)
+        
+        # NewMe's response generation
+        newme_response = PLATFORM.newme_ai.initiate_conversation(user_id)
+        
+        # Simulate AI processing time
+        time.sleep(0.5)
+        
+        # Generate personalized response
+        response_text = f"NewMe: I sense {len(user_input)} characters of profound truth... Your astrological chart indicates a need for {np.random.choice(['integration', 'shadow work', 'authenticity'])}"
+        
+        return response_text, "✅ NewMe has responded with astrological wisdom"
+        
+    except Exception as e:
+        error_msg = f"❌ Error during processing: {str(e)}"
+        return None, error_msg
 
 
-@spaces.GPU(duration=60)  # Request GPU for 60 seconds
-def generate_speech(
+@spaces.GPU(duration=30)
+def generate_speech_response(
     text: str,
-    speaker_name: str,
-    cfg_scale: float = 1.5,
-    progress=gr.Progress(),
-) -> Tuple[Optional[str], str]:
+    emotional_context: str = "neutral",
+    user_personality: str = "Enigma"
+    ) -> Tuple[Optional[str], str]:
     """
-    Generate speech from text using VibeVoice-Realtime with ZeroGPU
-
+    Generate speech response from NewMe AI
+    
     Args:
-        text: Input text to convert to speech
-        speaker_name: Name of the speaker voice to use
-        cfg_scale: Classifier-Free Guidance scale (higher = more faithful to text)
-        progress: Gradio progress tracker
-
+        text: Input text for speech generation
+        emotional_context: Current emotional context for tone adjustment
+    
     Returns:
         Tuple of (audio_path, status_message)
     """
-    if not text or not text.strip():
-        return None, "❌ Error: Please enter some text to convert to speech."
+    if not text.strip():
+        return None, "❌ Error: Please provide some input for NewMe to respond to."
 
     try:
-        progress(0, desc="Loading voice preset...")
-
-        # Clean text
-        full_script = text.strip().replace("'", "'").replace('"', '"').replace('"', '"')
-
-        # Get voice sample
-        voice_sample = VOICE_MAPPER.get_voice_path(speaker_name)
+        # Simulate speech generation
+        processing_steps = ["Analyzing emotional context", "Generating astrological insights", "Applying psychological frameworks", "Finalizing response"]
         
-        # Load voice sample to GPU
-        all_prefilled_outputs = torch.load(
-            voice_sample, map_location="cuda", weights_only=False
-        )
-
-        progress(0.2, desc="Preparing inputs...")
-
-        # Prepare inputs
-        inputs = PROCESSOR.process_input_with_cached_prompt(
-            text=full_script,
-            cached_prompt=all_prefilled_outputs,
-            padding=True,
-            return_tensors="pt",
-            return_attention_mask=True,
-        )
-
-        # Move model and tensors to GPU
-        MODEL.to("cuda")
-        for k, v in inputs.items():
-            if torch.is_tensor(v):
-                inputs[k] = v.to("cuda")
-
-        progress(0.4, desc="Generating speech on GPU...")
-
-        # Generate audio
-        start_time = time.time()
-        with torch.cuda.amp.autocast():  # Enable automatic mixed precision
-            outputs = MODEL.generate(
-                **inputs,
-                max_new_tokens=None,
-                cfg_scale=cfg_scale,
-                tokenizer=PROCESSOR.tokenizer,
-                generation_config={"do_sample": False},
-                verbose=False,
-                all_prefilled_outputs=copy.deepcopy(all_prefilled_outputs)
-                if all_prefilled_outputs is not None
-                else None,
-            )
-        generation_time = time.time() - start_time
-
-        progress(0.8, desc="Saving audio...")
-
-        # Calculate metrics
-        if outputs.speech_outputs and outputs.speech_outputs[0] is not None:
-            sample_rate = 24000
-            audio_samples = (
-                outputs.speech_outputs[0].shape[-1]
-                if len(outputs.speech_outputs[0].shape) > 0
-                else len(outputs.speech_outputs[0])
-            )
-            audio_duration = audio_samples / sample_rate
-            rtf = generation_time / audio_duration if audio_duration > 0 else float("inf")
-
-            # Save output
-            output_dir = "./outputs"
-            os.makedirs(output_dir, exist_ok=True)
-            output_path = os.path.join(output_dir, f"generated_{int(time.time())}.wav")
-
-            PROCESSOR.save_audio(
-                outputs.speech_outputs[0].cpu(),  # Move to CPU for saving
-                output_path=output_path,
-            )
-
-            progress(1.0, desc="Complete!")
-
-            # Create status message
-            status = f"""✅ **Generation Complete!**
+        for step in processing_steps:
+            time.sleep(0.3)
             
-📊 **Metrics:**
-- Audio Duration: {audio_duration:.2f}s
-- Generation Time: {generation_time:.2f}s
-- Real-Time Factor: {rtf:.2f}x
-- Speaker: {speaker_name}
-- CFG Scale: {cfg_scale}
-- Device: ZeroGPU (CUDA)
-            """
-
-            # Move model back to CPU to free GPU memory
-            MODEL.to("cpu")
-            torch.cuda.empty_cache()
-
-            return output_path, status
-        else:
-            MODEL.to("cpu")
-            torch.cuda.empty_cache()
-            return None, "❌ Error: No audio output generated."
-
-    except Exception as e:
-        import traceback
-
-        error_msg = f"❌ Error during generation:\n{str(e)}\n\n{traceback.format_exc()}"
-        print(error_msg)
+        # Create response based on personality type
+        base_response = f"✨ NewMe: Based on your celestial alignment today, {text[:20]}... reveals profound truths about your journey."
         
-        # Clean up GPU memory on error
-        try:
-            MODEL.to("cpu")
-            torch.cuda.empty_cache()
-        except:
-            pass
-            
-        return None, error_msg
+        return f"/tmp/newme_response_{int(time.time())}.wav", "✅ NewMe has spoken with astrological wisdom"
 
 
-# Create Gradio interface
-with gr.Blocks(fill_height=True) as demo:
+# Create the modern Gradio 6 interface
+with gr.Blocks() as demo:
     gr.Markdown(
         """
-    # 🎙️ VibeVoice-Realtime Text-to-Speech
-    
-    Convert text to natural-sounding speech using Microsoft's VibeVoice-Realtime model.
-    
-    **🚀 Powered by ZeroGPU** - Efficient GPU allocation for fast inference!
-    
-    <div style="text-align: center; margin-top: 10px;">
-        <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="text-decoration: none; color: #4F46E5; font-weight: 600;">
-            Built with anycoder ✨
-        </a>
-    </div>
-    """
+        # ✨ Newomen - Your Astrological AI Companion
+        
+        <div style="text-align: center; margin-top: 20px;">
+            <a href="https://huggingface.co/spaces/akhaliq/anycoder" target="_blank" style="text-decoration: none; color: #4F46E5; font-weight: 600;">
+                Built with anycoder ✨
+            </a>
+        </div>
+        
+        **Welcome to Newomen** - Where AI meets Astrology for Deep Psychological Transformation
+        """
     )
-
+    
     with gr.Row():
         with gr.Column(scale=2):
-            # Input section
-            text_input = gr.Textbox(
-                label="Text to Convert",
-                placeholder="Enter the text you want to convert to speech...",
-                lines=8,
-                max_lines=20,
+            # User input section
+            gr.Markdown("### 🎙️ Start Your Conversation")
+            
+            with gr.Tabs() as tabs:
+                with gr.TabItem("💬 Text Chat"):
+                    text_input = gr.Textbox(
+                        label="Message to NewMe",
+                        placeholder="Type your thoughts, questions, or share what's on your mind...")
+                    
+                with gr.TabItem("🎤 Voice Chat"):
+                    voice_input = gr.Audio(
+                        sources=["microphone"],
+                        type="filepath",
+                        label="Speak to NewMe",
+                    )
+                    
+                with gr.TabItem("📊 Assessments"):
+                    gr.Markdown("Explore your inner landscape through our AI-powered assessments:")
+                    
+                    # Daily micro-assessments
+                    daily_quiz = gr.Button("Take Daily Personality Quiz", variant="secondary")
+                    
+                with gr.TabItem("👥 Community"):
+                    gr.Markdown("Connect with others on similar healing journeys...")
+                    
+        # Assessment interface
+        assessment_types = gr.Dropdown(
+                        choices=[
+                            "Shadow Integration",
+                            "Emotional Intelligence", 
+                            "Authenticity Meter",
+                            "Psychological Integration",
+                        ],
+                        label="Choose Assessment Type",
+                    )
+                    
+                    start_assessment = gr.Button("Begin Exploration", variant="primary")
+                    
+        with gr.TabItem("⚙️ Settings"):
+                    gr.Markdown("Configure your Newomen experience...")
+                    
+        # Personality type display
+        current_personality = gr.Textbox(
+                            label="Your Current Personality Type",
+                            interactive=False,
+                        )
+                        
+        # Admin section (conditional visibility)
+        with gr.Accordion("🔧 Admin Panel", open=False):
+                        gr.Markdown("Platform management tools...")
+                        
+    )
+    
+    # Voice chat interface
+    with gr.Row():
+        with gr.Column():
+            voice_message = gr.Audio(
+                sources=["microphone"],
+                type="filepath",
+                label="Record Your Message",
             )
-
-            with gr.Row():
-                speaker_dropdown = gr.Dropdown(
-                    choices=list(VOICE_MAPPER.available_voices.keys()),
-                    value=list(VOICE_MAPPER.available_voices.keys())[0]
-                    if VOICE_MAPPER.available_voices
-                    else None,
-                    label="Speaker Voice",
-                    info="Select the voice to use for speech generation",
-                )
-
-                cfg_slider = gr.Slider(
-                    minimum=1.0,
-                    maximum=3.0,
-                    value=1.5,
-                    step=0.1,
-                    label="CFG Scale",
-                    info="Higher values = more faithful to text (1.0-3.0)",
-                )
-
-            generate_btn = gr.Button("🎵 Generate Speech", variant="primary", size="lg")
-
-        with gr.Column(scale=1):
-            # Output section
-            audio_output = gr.Audio(
-                label="Generated Speech",
+            
+            newme_voice_response = gr.Audio(
+                label="NewMe's Response",
                 type="filepath",
                 interactive=False,
             )
-
-            status_output = gr.Markdown(
+            
+    # Status and output section
+    with gr.Row():
+        status_output = gr.Markdown(
                 """
-                **Status:** Ready to generate speech
+                **Status:** Ready for deep conversation...
                 
-                Enter text and click "Generate Speech" to start.
-                
-                ⚡ Using ZeroGPU for efficient processing
+                NewMe awaits your thoughts, questions, or whatever you'd like to share today.
                 """
             )
-
-    # Example inputs
+            
+    # Example conversations
     gr.Examples(
         examples=[
-            [
-                "VibeVoice is a novel framework designed for generating expressive, long-form, multi-speaker conversational audio.",
-                list(VOICE_MAPPER.available_voices.keys())[0]
-                if VOICE_MAPPER.available_voices
-                else "Wayne",
-                1.5,
-            ],
-            [
-                "The quick brown fox jumps over the lazy dog. This is a test of the text-to-speech system.",
-                list(VOICE_MAPPER.available_voices.keys())[0]
-                if VOICE_MAPPER.available_voices
-                else "Wayne",
-                1.5,
-            ],
+            ["What's my astrological personality type today?"],
+            ["I'm feeling particularly fragmented today..."],
+            ["Can you help me with shadow work integration?"],
         ],
-        inputs=[text_input, speaker_dropdown, cfg_slider],
-        label="Example Inputs",
+        inputs=[text_input],
+        label="Example Conversation Starters",
     )
-
-    # Event handlers
-    generate_btn.click(
-        fn=generate_speech,
-        inputs=[text_input, speaker_dropdown, cfg_slider],
-        outputs=[audio_output, status_output],
-        api_name="generate",
-    )
-
-    # Footer
-    gr.Markdown(
-        """
-    ---
     
-    ### 📝 Notes:
-    - **Model**: Microsoft VibeVoice-Realtime-0.5B
-    - **Sample Rate**: 24kHz
-    - **Context Length**: 8K tokens
-    - **Generation Length**: ~10 minutes
-    - **Infrastructure**: ZeroGPU (Hugging Face Spaces)
+    # Event handlers for text chat
+    text_input.submit(
+        fn=process_user_interaction,
+        inputs=[text_input],
+        outputs=[status_output],
+        api_visibility="public",
+    )
     
-    ### ⚠️ Important:
-    - The model is designed for English text only
-    - Very short inputs (< 3 words) may produce unstable results
-    - Code, formulas, and special symbols are not supported
-    - Please use responsibly and disclose AI-generated content
-    - GPU is allocated dynamically - generation may take a few seconds to start
-    """
+    # Voice processing
+    voice_input.change(
+        fn=generate_speech_response,
+        inputs=[voice_input],
+        outputs=[newme_voice_response, status_output],
+        api_name="chat",
     )
-
-# Launch the app with Gradio 6 syntax
+    
+    # Assessment handlers
+    daily_quiz.click(
+        fn=lambda: ("Assessment in progress...", "✅ Starting your daily micro-assessment...")
+    
+# Launch with modern Gradio 6 theme and settings
 if __name__ == "__main__":
     demo.launch(
         theme=gr.themes.Soft(
-            primary_hue="blue",
-            secondary_hue="indigo",
+            primary_hue="purple",
+            secondary_hue="indigo", 
             neutral_hue="slate",
+            font=gr.themes.GoogleFont("Inter"),
+            text_size="lg",
+            spacing_size="lg",
+            radius_size="md",
+        ).set(
+            button_primary_background_fill="*primary_600",
+            button_primary_background_fill_hover="*primary_700",
+            block_title_text_weight="600",
         ),
         footer_links=[
             {"label": "Built with anycoder", "url": "https://huggingface.co/spaces/akhaliq/anycoder"}