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import gradio as gr
import torch
from transformers import AutoConfig, AutoProcessor, AutoModelForCausalLM
# === Diagnostic Code Start ===
# Load the configuration with remote code enabled
config = AutoConfig.from_pretrained("lmms-lab/LLaVA-Video-7B-Qwen2", trust_remote_code=True)
print("Configuration type:", type(config))
print("Configuration architectures:", config.architectures)
# === Diagnostic Code End ===
# Load processor and model with remote code enabled.
processor = AutoProcessor.from_pretrained(
"lmms-lab/LLaVA-Video-7B-Qwen2",
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
"lmms-lab/LLaVA-Video-7B-Qwen2",
trust_remote_code=True
)
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
def analyze_video(video_path):
prompt = "Analyze this video of a concert and determine the moment when the crowd is most engaged."
inputs = processor(text=prompt, video=video_path, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}
outputs = model.generate(**inputs, max_new_tokens=100)
answer = processor.decode(outputs[0], skip_special_tokens=True)
return answer
iface = gr.Interface(
fn=analyze_video,
inputs=gr.Video(label="Upload Concert/Event Video", type="filepath"),
outputs=gr.Textbox(label="Engagement Analysis"),
title="Crowd Engagement Analyzer",
description="Upload a video of a concert or event and the model will analyze the moment when the crowd is most engaged."
)
if __name__ == "__main__":
iface.launch()
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