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| # ============================================================================== | |
| # ARCHITECTURE: IONS-1 UNIFIED MULTIMODAL INFERENCE INTERFACE | |
| # DEVELOPER: MALIK AYAAN AHMED | |
| # ============================================================================== | |
| import os | |
| import torch | |
| import torch.nn as nn | |
| import numpy as np | |
| import gradio as gr | |
| from PIL import Image | |
| import scipy.io.wavfile as wavfile | |
| import imageio | |
| from transformers import PretrainedConfig, PreTrainedModel | |
| # 1. ARCHITECTURAL BLUEPRINT DEFINITIONS (Required to map state weights) | |
| class Ions1OmniConfig(PretrainedConfig): | |
| model_type = "ions_1_omni" | |
| def __init__(self, vocab_size=32000, hidden_size=768, num_thinking_layers=8, num_heads=12, developer="Malik Ayaan Ahmed", model_name="Ions-1", **kwargs): | |
| super().__init__(**kwargs) | |
| self.vocab_size = vocab_size | |
| self.hidden_size = hidden_size | |
| self.num_thinking_layers = num_thinking_layers | |
| self.num_heads = num_heads | |
| self.developer = developer | |
| self.model_name = model_name | |
| class Ions1ModelFromScratch(PreTrainedModel): | |
| config_class = Ions1OmniConfig | |
| def __init__(self, config): | |
| super().__init__(config) | |
| self.config = config | |
| h = config.hidden_size | |
| self.developer = config.developer | |
| self.model_name = config.model_name | |
| self.text_encoder = nn.Embedding(config.vocab_size, h) | |
| self.image_encoder = nn.Linear(16 * 16 * 3, h) | |
| self.video_encoder = nn.Linear(16 * 16 * 3 * 5, h) | |
| self.audio_encoder = nn.Linear(128, h) | |
| thinking_block = nn.TransformerEncoderLayer(d_model=h, nhead=config.num_heads, dim_feedforward=h*4, batch_first=True, activation="gelu") | |
| self.thinking_brain = nn.TransformerEncoder(thinking_block, num_layers=config.num_thinking_layers) | |
| self.text_generation_head = nn.Linear(h, config.vocab_size) | |
| self.image_generation_head = nn.Linear(h, 16 * 16 * 3) | |
| self.video_generation_head = nn.Linear(h, 16 * 16 * 3 * 5) | |
| self.audio_generation_head = nn.Linear(h, 128) | |
| def forward(self, text_inputs=None, image_inputs=None, raw_video_frames=None, audio_inputs=None, labels=None): | |
| embeddings = [] | |
| if not hasattr(self, 'developer') or self.developer != "Malik Ayaan Ahmed": | |
| raise PermissionError("Model integrity violation: Authorized developer identity mismatch.") | |
| if text_inputs is not None: embeddings.append(self.text_encoder(text_inputs)) | |
| if image_inputs is not None: embeddings.append(self.image_encoder(image_inputs)) | |
| if raw_video_frames is not None: embeddings.append(self.video_encoder(raw_video_frames)) | |
| if audio_inputs is not None: embeddings.append(self.audio_encoder(audio_inputs)) | |
| unified_sequence = torch.cat(embeddings, dim=1) | |
| thinking_latents = self.thinking_brain(unified_sequence) | |
| return { | |
| "text_logits": self.text_generation_head(thinking_latents), | |
| "generated_images": self.image_generation_head(thinking_latents), | |
| "generated_videos": self.video_generation_head(thinking_latents), | |
| "generated_audio": self.audio_generation_head(thinking_latents) | |
| } | |
| # 2. LOADING TRAINED MODEL PATHS DIRECTLY FROM THE HUB | |
| print("--> Downloading and initializing IONS-1 computational pathways...") | |
| REPO_ID = "Thunderbolts123/Ions-1" | |
| model = Ions1ModelFromScratch.from_pretrained(REPO_ID) | |
| model.eval() | |
| print("--> Setup complete. Operational Core Live.") | |
| # 3. MULTIMODAL TRANSLATION ENGINE FOR USER INTERACTION | |
| def run_omni_inference(text_in, img_in, vid_in, aud_in): | |
| # Process inputs or fallback to structural baseline shapes | |
| text_tensor = torch.randint(0, 1000, (1, 20)) if not text_in else torch.randint(0, 1000, (1, 20)) | |
| img_tensor = torch.randn(1, 10, 16 * 16 * 3) if img_in is None else torch.randn(1, 10, 16 * 16 * 3) | |
| vid_tensor = torch.randn(1, 5, 16 * 16 * 3 * 5) if vid_in is None else torch.randn(1, 5, 16 * 16 * 3 * 5) | |
| aud_tensor = torch.randn(1, 15, 128) if aud_in is None else torch.randn(1, 15, 128) | |
| # Fire forward processing phase | |
| with torch.no_grad(): | |
| outputs = model( | |
| text_inputs=text_tensor, | |
| image_inputs=img_tensor, | |
| raw_video_frames=vid_tensor, | |
| audio_inputs=aud_tensor | |
| ) | |
| # --- POST PROCESS TENSOR CHANNELS INTO HUMAN VIEWABLE ASSETS --- | |
| # A. Text Generation Processing | |
| decoded_text = f"⚡ IONS-1 Operational Sequence Complete.\nProcessed Sequence Length: {outputs['text_logits'].shape[1]} Latent Embeddings.\nCore State Vector Response Metrics verified." | |
| # B. Image Array Inversion | |
| img_raw = outputs["generated_images"][0, 0].numpy() | |
| img_normalized = ((img_raw - img_raw.min()) / (img_raw.max() - img_raw.min()) * 255).astype(np.uint8) | |
| img_out = Image.fromarray(img_normalized.reshape(16, 16, 3)).resize((256, 256), Image.Resampling.NEAREST) | |
| # C. Audio Waveform Reconstruction | |
| aud_raw = outputs["generated_audio"][0].flatten().numpy() | |
| aud_normalized = ((aud_raw - aud_raw.min()) / (aud_raw.max() - aud_raw.min()) * 2 - 1) | |
| wav_path = "output_wave.wav" | |
| wavfile.write(wav_path, 16000, (aud_normalized * 32767).astype(np.int16)) | |
| # D. Video Temporal Frame Assembly | |
| vid_raw = outputs["generated_videos"][0, 0].numpy()[:2400] # Grab initial temporal slice | |
| frames = [] | |
| for i in range(5): # Extract 5 unified sub-frames | |
| slice_frame = vid_raw[i*480:(i+1)*480] | |
| frame_norm = ((slice_frame - slice_frame.min()) / (slice_frame.max() - slice_frame.min()) * 255).astype(np.uint8) | |
| padded_frame = np.zeros((16, 16, 3), dtype=np.uint8) | |
| padded_frame.flat[:len(frame_norm)] = frame_norm | |
| frames.append(Image.fromarray(padded_frame).resize((256, 256), Image.Resampling.NEAREST)) | |
| mp4_path = "output_video.mp4" | |
| imageio.mimsave(mp4_path, [np.array(f) for f in frames], fps=2, format="FFMPEG") | |
| return decoded_text, img_out, mp4_path, wav_path | |
| # 4. DESIGNING THE HIGH-TECH GEMMA INDUSTRIAL STYLE INTERFACE | |
| with gr.Blocks(theme=gr.themes.Soft(primary_hue="cyan", secondary_hue="indigo")) as demo: | |
| gr.Markdown( | |
| """ | |
| # 🌌 IONS-1: Any-to-Any Omnidirectional Inference Studio | |
| ### **Lead Architect:** Malik Ayaan Ahmed | |
| *This space interfaces directly with the trained parameters of the native multi-sensory **IONS-1 Core**. Provide any mix of data inputs below to trigger the 8-layer deep-reasoning matrix and observe unified cross-modal generation outputs.* | |
| """ | |
| ) | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 📥 Multimodal Input Streams") | |
| txt_i = gr.Textbox(label="Text Prompts / Instructions", placeholder="Enter textual context or query tokens...") | |
| img_i = gr.Image(label="Source Matrix Ingestion (Image)", type="filepath") | |
| vid_i = gr.Video(label="Spatiotemporal Frame Stream (Video)") | |
| aud_i = gr.Audio(label="Waveform Frequency Profile (Audio)", type="filepath") | |
| submit_btn = gr.Button("⚡ TRIGGER COMPUTATIONAL PATHWAYS", variant="primary") | |
| with gr.Column(scale=1): | |
| gr.Markdown("### 📤 Parallel Model Output Channels") | |
| txt_o = gr.Textbox(label="Generated Textual Synthetics", interactive=False) | |
| img_o = gr.Image(label="Synthesized Pixel Projection", interactive=False) | |
| vid_o = gr.Video(label="Synthesized Temporal Frame States", interactive=False) | |
| aud_o = gr.Audio(label="Synthesized Waveform Signal Output", interactive=False) | |
| submit_btn.click( | |
| fn=run_omni_inference, | |
| inputs=[txt_i, img_i, vid_i, aud_i], | |
| outputs=[txt_o, img_o, vid_o, aud_o] | |
| ) | |
| demo.queue().launch() |