Create app.py

Initial commit

app.py

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+"""
+File: app.py
+Description: Chat with the vision language model Gemma3.
+Author: Didier Guillevic
+Date: 2025-03-16
+"""
+
+import gradio as gr
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration
+from transformers import TextIteratorStreamer
+from threading import Thread
+import torch
+
+def process(message, history):
+    """Generate the model response in streaming mode given message and history
+    """
+    print(f"{history=}")
+    # 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})
+    
+    # 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
+
+
+#
+# User interface
+#
+with gr.Blocks() as demo:
+    chat_interface = gr.ChatInterface(
+        fn=process,
+        title="Multimedia Chat",
+        description="Chat with text or text+image.",
+        multimodal=True,
+        examples=[
+            "How can we rationalize quantum entanglement?",
+            {'files': ['./sample_ID.jpeg',], 'text': 'Describe this image in a few words.'},
+            "Peux-tu expliquer le 'quantum spin'?"
+        ]
+    )
+
+demo.launch()