v1.0.0

app.py

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+import gradio as gr
+import cv2
+import torch
+from PIL import Image
+from pathlib import Path
+from threading import Thread
+from transformers import AutoProcessor, Gemma3ForConditionalGeneration, TextIteratorStreamer
+import spaces
+import time
+
+# model config
+model_12b_name = "google/gemma-3-12b-it"
+model_4b_name = "google/gemma-3-4b-it"
+model_12b = Gemma3ForConditionalGeneration.from_pretrained(
+    model_12b_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+).eval()
+processor_12b = AutoProcessor.from_pretrained(model_12b_name)
+model_4b = Gemma3ForConditionalGeneration.from_pretrained(
+    model_4b_name,
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+).eval()
+processor_4b = AutoProcessor.from_pretrained(model_4b_name)
+# I will add timestamp later
+def extract_video_frames(video_path, num_frames=8):
+    cap = cv2.VideoCapture(video_path)
+    frames = []
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    step = max(total_frames // num_frames, 1)
+    
+    for i in range(num_frames):
+        cap.set(cv2.CAP_PROP_POS_FRAMES, i * step)
+        ret, frame = cap.read()
+        if ret:
+            frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+            frames.append(Image.fromarray(frame))
+    cap.release()
+    return frames
+
+def format_message(content, files):
+    
+    message_content = []
+
+    if content:
+        parts = content.split('<image>')
+        for i, part in enumerate(parts):
+            if part.strip():
+                message_content.append({"type": "text", "text": part.strip()})
+            if i < len(parts) - 1 and files:
+                img = Image.open(files.pop(0))
+                message_content.append({"type": "image", "image": img})
+    for file in files:
+        file_path = file if isinstance(file, str) else file.name
+        if Path(file_path).suffix.lower() in ['.jpg', '.jpeg', '.png']:
+            img = Image.open(file_path)
+            message_content.append({"type": "image", "image": img})
+        elif Path(file_path).suffix.lower() in ['.mp4', '.mov']:
+            frames = extract_video_frames(file_path)
+            for frame in frames:
+                message_content.append({"type": "image", "image": frame})
+    return message_content
+
+def format_conversation_history(chat_history):
+    messages = []
+    current_user_content = []
+    for item in chat_history:
+        role = item["role"]
+        content = item["content"]
+        if role == "user":
+            if isinstance(content, str):
+                current_user_content.append({"type": "text", "text": content})
+            elif isinstance(content, list):
+                current_user_content.extend(content)
+            else:
+                current_user_content.append({"type": "text", "text": str(content)})
+        elif role == "assistant":
+            if current_user_content:
+                messages.append({"role": "user", "content": current_user_content})
+                current_user_content = []
+            messages.append({"role": "assistant", "content": [{"type": "text", "text": str(content)}]})
+    if current_user_content:
+        messages.append({"role": "user", "content": current_user_content})
+    return messages
+
+@spaces.GPU
+def generate_response(input_data, chat_history, model_choice, max_new_tokens, system_prompt, temperature, top_p, top_k, repetition_penalty):
+    if isinstance(input_data, dict) and "text" in input_data:
+        text = input_data["text"]
+        files = input_data.get("files", [])
+    else:
+        text = str(input_data)
+        files = []
+
+    new_message_content = format_message(text, files)
+    new_message = {"role": "user", "content": new_message_content}
+    system_message = [{"role": "system", "content": [{"type": "text", "text": system_prompt}]}] if system_prompt else []
+    processed_history = format_conversation_history(chat_history)
+    messages = system_message + processed_history
+    if messages and messages[-1]["role"] == "user":
+        messages[-1]["content"].extend(new_message["content"])
+    else:
+        messages.append(new_message)
+    if model_choice == "Gemma 3 12B":
+        model = model_12b
+        processor = processor_12b
+    else:
+        model = model_4b
+        processor = processor_4b
+    inputs = processor.apply_chat_template(
+        messages,
+        add_generation_prompt=True,
+        tokenize=True,
+        return_tensors="pt",
+        return_dict=True
+    ).to(model.device)
+    streamer = TextIteratorStreamer(processor, skip_prompt=True, skip_special_tokens=True)
+    generation_kwargs = dict(
+        inputs,
+        streamer=streamer,
+        max_new_tokens=max_new_tokens,
+        do_sample=True,
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        repetition_penalty=repetition_penalty
+    )
+    thread = Thread(target=model.generate, kwargs=generation_kwargs)
+    thread.start()
+    
+    outputs = []
+    for text in streamer:
+        outputs.append(text)
+        yield "".join(outputs)
+
+demo = gr.ChatInterface(
+    fn=generate_response,
+    additional_inputs=[
+        gr.Dropdown(
+            label="Model",
+            choices=["Gemma 3 12B", "Gemma 3 4B"],
+            value="Gemma 3 12B"
+        ),
+        gr.Slider(label="Max new tokens", minimum=100, maximum=2000, step=1, value=512),
+        gr.Textbox(
+            label="System Prompt",
+            value="You are a friendly chatbot. ",
+            lines=4,
+            placeholder="Change system prompt"
+        ),
+        gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, step=0.1, value=0.7),
+        gr.Slider(label="Top-p", minimum=0.05, maximum=1.0, step=0.05, value=0.9),
+        gr.Slider(label="Top-k", minimum=1, maximum=100, step=1, value=50),
+        gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, step=0.05, value=1.0),
+    ],
+    examples=[
+        [{"text": "Explain this image", "files": ["examples/image1.jpg"]}],
+    ],
+    cache_examples=False,
+    type="messages",
+    description="""
+    #Gemma 3
+    You can pick your model 12B or 4B, upload images or videos, and adjust settings below to customize your experience.
+    """,
+    fill_height=True,
+    textbox=gr.MultimodalTextbox(
+        label="Query Input",
+        file_types=["image", "video"],
+        file_count="multiple",
+        placeholder="Type your message or upload media"
+    ),
+    stop_btn="Stop Generation",
+    multimodal=True,
+    theme=gr.themes.Soft(),
+)
+
+if __name__ == "__main__":
+    demo.launch()