Upload vlm.py

vlm.py

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+"""
+File: vlm.py
+Description: Vision language model utility functions.
+Author: Didier Guillevic
+Date: 2025-03-16
+"""
+
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+from transformers import TextIteratorStreamer
+from threading import Thread
+import torch
+
+#
+# Load the model: google/gemma-3-4b-it
+#
+device = 'mps'
+model_id = "google/gemma-3-4b-it"
+processor = AutoProcessor.from_pretrained(model_id, use_fast=True, padding_side="left")
+model = Gemma3ForConditionalGeneration.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16
+).to(device).eval()
+
+#
+# Build messages
+#
+def build_messages(message: dict, history: list[tuple]):
+    """Build messages given message & history from a **multimodal** chat interface.
+
+    Args:
+        message: dictionary with keys: 'text', 'files'
+        history: list of tuples with (message, response)
+    
+    Returns:
+        list of messages (to be sent to the model)
+    """
+    # Get the user's text and list of images
+    user_text = message.get("text", "")
+    user_images = message.get("files", [])  # List of images
+
+    # Build the message list including history
+    messages = []
+    combined_user_input = [] #Combine images and text if found in same turn.
+    for user_turn, bot_turn in history:
+        if isinstance(user_turn, tuple):  # Image input
+            image_content = [{"type": "image", "url": image_url} for image_url in user_turn]
+            combined_user_input.extend(image_content)
+        elif isinstance(user_turn, str): #Text input
+            combined_user_input.append({"type":"text", "text": user_turn})
+        if combined_user_input and bot_turn:
+            messages.append({'role': 'user', 'content': combined_user_input})
+            messages.append({'role': 'assistant', 'content': [{"type": "text", "text": bot_turn}]})
+            combined_user_input = [] #reset the combined user input.
+    
+    # Build the user message's content from the provided message
+    user_content = []
+    if user_text:
+        user_content.append({"type": "text", "text": user_text})
+    for image in user_images:
+        user_content.append({"type": "image", "url": image})
+    
+    messages.append({'role': 'user', 'content': user_content})
+
+    return messages
+
+
+#
+# Streaming response
+#
+@torch.inference_mode()
+def stream_response(messages: list[dict]):
+    """Stream the model's response to the chat interface.
+    
+    Args:
+        messages: list of messages to send to the model
+    """
+    # Generate model's response
+    inputs = processor.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=True,
+        return_dict=True, return_tensors="pt"
+    ).to(model.device, dtype=torch.bfloat16)
+
+    streamer = TextIteratorStreamer(
+        processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=1_024,
+        do_sample=False
+    )
+
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+
+    partial_message = ""
+    for new_text in streamer:
+        partial_message += new_text
+        yield partial_message
+
+
+#
+# Response (non-streaming)
+#
+@torch.inference_mode()
+def get_response(messages: list[dict]):
+    """Get the model's response.
+    
+    Args:
+        messages: list of messages to send to the model
+    """
+    # Generate model's response
+    inputs = processor.apply_chat_template(
+        messages, add_generation_prompt=True, tokenize=True,
+        return_dict=True, return_tensors="pt"
+    ).to(model.device, dtype=torch.bfloat16)
+
+    input_len = inputs["input_ids"].shape[-1]
+
+    with torch.inference_mode():
+        generation = model.generate(**inputs, max_new_tokens=100, do_sample=False)
+        generation = generation[0][input_len:]
+
+    decoded = processor.decode(generation, skip_special_tokens=True)
+    
+    return decoded