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| import sys | |
| import subprocess | |
| def upgrade_packages(): | |
| try: | |
| print("Upgrading transformers and accelerate...") | |
| subprocess.check_call([ | |
| sys.executable, "-m", "pip", "install", "--upgrade", | |
| "transformers>=4.31.0", "accelerate>=0.20.0" | |
| ]) | |
| print("Upgrade complete.") | |
| except Exception as e: | |
| print("Error upgrading packages:", e) | |
| # Attempt to upgrade the packages | |
| upgrade_packages() | |
| # Now import the libraries | |
| import torch | |
| from transformers import CLIPProcessor, CLIPModel, WhisperProcessor, WhisperForConditionalGeneration, AutoTokenizer, AutoModelForCausalLM | |
| import gradio as gr | |
| import soundfile as sf | |
| # ------------------------------ | |
| # Load Pretrained Models & Processors | |
| # ------------------------------ | |
| print("Loading CLIP model...") | |
| clip_model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32") | |
| clip_processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32") | |
| print("Loading Whisper model...") | |
| whisper_model = WhisperForConditionalGeneration.from_pretrained("openai/whisper-small") | |
| whisper_processor = WhisperProcessor.from_pretrained("openai/whisper-small") | |
| print("Loading GPT-2 model (placeholder for your text model)...") | |
| tokenizer = AutoTokenizer.from_pretrained("gpt2") | |
| text_model = AutoModelForCausalLM.from_pretrained("gpt2") | |
| # ------------------------------ | |
| # Define Projection Layers | |
| # ------------------------------ | |
| print("Initializing image projection layer...") | |
| image_projection = torch.nn.Linear(512, 768) | |
| # ------------------------------ | |
| # Multi-Modal Inference Function | |
| # ------------------------------ | |
| def multimodal_inference(text_input, image_input, audio_input): | |
| """ | |
| Processes text, image, and audio inputs. | |
| - Text is added directly. | |
| - The image is processed via CLIP, its embedding is projected, and a placeholder tag is appended. | |
| - Audio is transcribed using Whisper and appended. | |
| The final prompt is sent to the text model for generation. | |
| """ | |
| prompt = "" | |
| # Process text input | |
| if text_input: | |
| prompt += text_input.strip() | |
| # Process image input if provided | |
| if image_input is not None: | |
| try: | |
| clip_inputs = clip_processor(images=image_input, return_tensors="pt") | |
| with torch.no_grad(): | |
| image_features = clip_model.get_image_features(**clip_inputs) | |
| # Normalize image features | |
| image_features = image_features / image_features.norm(p=2, dim=-1, keepdim=True) | |
| # Project image embedding into GPT-2's embedding space | |
| projected_image = image_projection(image_features) | |
| # For demo purposes, we append a placeholder tag. | |
| prompt += " [IMAGE_EMBEDDING]" | |
| except Exception as e: | |
| print("Error processing image:", e) | |
| prompt += " [IMAGE_ERROR]" | |
| # Process audio input if provided | |
| if audio_input is not None: | |
| try: | |
| audio, sr = sf.read(audio_input) | |
| except Exception as e: | |
| print("Error reading audio file:", e) | |
| return "Error processing audio input." | |
| try: | |
| whisper_inputs = whisper_processor(audio, sampling_rate=sr, return_tensors="pt") | |
| with torch.no_grad(): | |
| predicted_ids = whisper_model.generate(whisper_inputs.input_features) | |
| transcription = whisper_processor.batch_decode(predicted_ids, skip_special_tokens=True)[0] | |
| prompt += " " + transcription.strip() | |
| except Exception as e: | |
| print("Error during audio transcription:", e) | |
| prompt += " [AUDIO_ERROR]" | |
| print("Final fused prompt:", prompt) | |
| # Generate text response using the text model | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = text_model.generate(**inputs, max_length=200) | |
| generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| return generated_text | |
| # ------------------------------ | |
| # Gradio Interface for Hugging Face Spaces | |
| # ------------------------------ | |
| iface = gr.Interface( | |
| fn=multimodal_inference, | |
| inputs=[ | |
| gr.inputs.Textbox(lines=5, placeholder="Enter your text here...", label="Text Input"), | |
| gr.inputs.Image(type="pil", label="Image Input (Optional)"), | |
| gr.inputs.Audio(source="upload", type="filepath", label="Audio Input (Optional)") | |
| ], | |
| outputs="text", | |
| title="Multi-Modal LLM Demo", | |
| description="This demo accepts text, image, and audio inputs, processes each modality, and produces a text response." | |
| ) | |
| if __name__ == "__main__": | |
| iface.launch() | |