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| import gradio as gr | |
| from transformers import AutoProcessor, Gemma3nForConditionalGeneration | |
| from PIL import Image | |
| import requests | |
| import torch | |
| import io | |
| import os | |
| from huggingface_hub import login | |
| hf_token = os.environ.get("HF_TOKEN") | |
| login(token=hf_token) | |
| # Initialize the model and processor | |
| model_id = "google/gemma-3n-e4b-it" | |
| try: | |
| model = Gemma3nForConditionalGeneration.from_pretrained( | |
| model_id, device_map="auto", torch_dtype=torch.bfloat16 | |
| ).eval() | |
| processor = AutoProcessor.from_pretrained(model_id) | |
| except Exception as e: | |
| raise Exception(f"Failed to load model or processor: {str(e)}") | |
| def process_inputs(image_input, image_url, text_prompt): | |
| """ | |
| Process image (from file or URL) and text prompt to generate a response using the Gemma model. | |
| Args: | |
| image_input: Uploaded image file | |
| image_url: URL of an image | |
| text_prompt: Text input from the user | |
| Returns: | |
| Generated text response from the model | |
| """ | |
| try: | |
| # Handle image input: prioritize uploaded image, then URL, then None | |
| image = None | |
| if image_input is not None: | |
| image = Image.open(image_input).convert("RGB") | |
| elif image_url: | |
| response = requests.get(image_url, stream=True) | |
| response.raise_for_status() | |
| image = Image.open(io.BytesIO(response.content)).convert("RGB") | |
| # Prepare messages for the model | |
| messages = [ | |
| { | |
| "role": "system", | |
| "content": [{"type": "text", "text": "You are a helpful assistant."}] | |
| }, | |
| { | |
| "role": "user", | |
| "content": [] | |
| } | |
| ] | |
| # Add image to content if provided | |
| if image is not None: | |
| messages[1]["content"].append({"type": "image", "image": image}) | |
| # Add text prompt if provided | |
| if text_prompt: | |
| messages[1]["content"].append({"type": "text", "text": text_prompt}) | |
| else: | |
| return "Please provide a text prompt." | |
| # Process inputs using the processor | |
| inputs = processor.apply_chat_template( | |
| messages, | |
| add_generation_prompt=True, | |
| tokenize=True, | |
| return_dict=True, | |
| return_tensors="pt", | |
| ).to(model.device) | |
| input_len = inputs["input_ids"].shape[-1] | |
| # Generate response | |
| with torch.inference_mode(): | |
| generation = model.generate(**inputs, max_new_tokens=500, do_sample=False) | |
| generation = generation[0][input_len:] | |
| # Decode and return the response | |
| decoded = processor.decode(generation, skip_special_tokens=True) | |
| return decoded | |
| except Exception as e: | |
| return f"Error: {str(e)}" | |
| # Define the Gradio interface | |
| iface = gr.Interface( | |
| fn=process_inputs, | |
| inputs=[ | |
| gr.Image(type="file", label="Upload Image (optional)"), | |
| gr.Textbox(label="Image URL (optional)", placeholder="Enter image URL"), | |
| gr.Textbox(label="Text Prompt", placeholder="Enter your prompt here") | |
| ], | |
| outputs=gr.Textbox(label="Model Response"), | |
| title="Gemma-3 Multimodal App (Authenticated)", | |
| description="Upload an image or provide an image URL, and enter a text prompt to interact with the Gemma-3 model. Ensure you have authenticated with a valid Hugging Face access token.", | |
| allow_flagging="never" | |
| ) | |
| # Launch the app | |
| iface.launch() |