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import gradio as gr
import torch
from transformers import AutoModel, AutoTokenizer
# Set the model path (this is the repository/model ID on Hugging Face)
model_path = "OpenGVLab/InternVideo2_5_Chat_8B"
# Load the tokenizer and model with remote code enabled.
# .half() converts the model to FP16 and .cuda() moves it to GPU (if available).
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True).half().cuda()
# Get the image processor from the vision tower (if needed by the model's implementation)
image_processor = model.get_vision_tower().image_processor
# Evaluation settings
max_num_frames = 512
generation_config = {
"do_sample": False,
"temperature": 0.0,
"max_new_tokens": 1024,
"top_p": 0.1,
"num_beams": 1,
}
# Define a chat function that performs either single-turn or multi-turn conversation.
def chat_interface(video_path, user_prompt, chat_history):
"""
Performs a chat turn with the model. If no chat_history is provided,
it starts a new conversation.
Parameters:
video_path (str): The filepath of the uploaded video.
user_prompt (str): The user's question.
chat_history (list): The conversation history (empty list for a new conversation).
Returns:
A tuple containing the model's output (str) and the updated chat history (list).
"""
if chat_history is None:
chat_history = []
# The model.chat() method returns output and updated history.
output, new_history = model.chat(
video_path=video_path,
tokenizer=tokenizer,
user_prompt=user_prompt,
chat_history=chat_history,
return_history=True,
max_num_frames=max_num_frames,
generation_config=generation_config
)
return output, new_history
# Build the Gradio interface.
with gr.Blocks() as demo:
gr.Markdown("## InternVideo2_5_Chat_8B Chat Interface")
with gr.Row():
video_input = gr.Video(label="Upload Video", type="filepath")
question_input = gr.Textbox(label="Enter your question", placeholder="Type your question here...")
# We'll use a hidden state to keep the conversation history.
chat_state = gr.State([])
output_text = gr.Textbox(label="Model Response")
def process_chat(video, question, history):
response, new_history = chat_interface(video, question, history)
return response, new_history
send_btn = gr.Button("Send")
send_btn.click(
process_chat,
inputs=[video_input, question_input, chat_state],
outputs=[output_text, chat_state]
)
# Launch the app.
if __name__ == "__main__":
demo.launch()
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