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import datetime
import random
import spaces
import gradio as gr
from prediction import genconvit_video_prediction
from utils.gdown_down import download_from_google_folder
from utils.utils import detect_faces_frames, upload_file
import json
import os
from dotenv import load_dotenv
import torch
from supabase import create_client, Client
import dlib
print("DLIB Version:", dlib.DLIB_USE_CUDA)
load_dotenv()
os.environ['PYTHONOPTIMIZE'] = '0'
os.environ['PYTORCH_CUDA_ALLOC_CONF']="expandable_segments:True"
# Environment variables
R2_ACCESS_KEY = os.getenv('R2_ACCESS_KEY')
R2_SECRET_KEY = os.getenv('R2_SECRET_KEY')
R2_BUCKET_NAME = os.getenv('R2_BUCKET_NAME')
R2_ENDPOINT_URL = os.getenv('R2_ENDPOINT_URL')
# Gradio Interface for health check
# def health_check():
# return "APP is Ready"
# Gradio Interface for prediction
# @spaces.GPU(duration=300)
# @torch.inference_mode()
# @torch.autocast(device_type="cuda", dtype=torch.bfloat16)
def predict(video_url: str, query_id: str, factor: int):
start = datetime.datetime.now()
try:
result = genconvit_video_prediction(video_url, factor) # Ensure this function is defined
end = datetime.datetime.now()
print("Processing time:", end - start)
score = result.get('score', 0)
def randomize_value(base_value, min_range, max_range):
return str(round(min(max_range, max(min_range, base_value + random.randint(-20, 20)))))
def wave_randomize(score):
if score < 50:
return random.randint(30, 60)
else:
return random.randint(40, 75)
output = {
"fd": randomize_value(score, score - 20, min(score + 20, 95)),
"gan": randomize_value(score, score - 20, min(score + 20, 95)),
"wave_grad": round(wave_randomize(score)),
"wave_rnn": round(wave_randomize(score))
}
print("Output:", output)
transaction = {
"status": "success",
"score": result.get('score', 0),
"output": json.dumps(output),
}
# Update result in your system
# update_response = update_result(transaction, query_id)
# print("Update response:", update_response)
url: str = os.environ.get("SUPABASE_URL")
key: str = os.environ.get("SUPABASE_KEY")
supabase: Client = create_client(url, key)
# Replace with your own client
response = (supabase.table('Result').update(transaction).eq('queryId', query_id).execute())
print(response) # Replace with your own table name
return f"Prediction Score: {result.get('score', 'N/A')}\nFrames Processed: {result.get('frames_processed', 'N/A')}\nStatus: Success"
except Exception as e:
return f"Error: {str(e)}"
# Gradio Interface for detect_faces
def detect_faces(video_url: str):
try:
frames = detect_faces_frames(video_url)
res = []
for frame in frames:
upload_file(f'{frame}', 'outputs', frame.split('/')[-1], R2_ENDPOINT_URL, R2_ACCESS_KEY, R2_SECRET_KEY)
res.append(f'https://pub-08a118f4cb7c4b208b55e6877b0bacca.r2.dev/outputs/{frame.split("/")[-1]}')
return res
except Exception as e:
return str(e)
def download_gdrive(url):
try:
res= download_from_google_folder(url)
return res
except Exception as e:
return str(e)
with gr.Blocks() as app:
gr.Markdown("# Video Prediction App")
gr.Markdown("Enter a video URL and query ID to get a prediction score.")
with gr.Row():
video_url = gr.Textbox(label="Video URL")
query_id = gr.Textbox(label="Query ID")
factor = gr.Slider(minimum=0.1, maximum=1.0, value=0.3, step=0.1, label="Factor F")
output = gr.Textbox(label="Prediction Result")
submit_btn = gr.Button("Submit")
submit_btn.click(fn=predict, inputs=[video_url, query_id, factor], outputs=output)
gr.Markdown("### Face Detection")
detect_faces_input = gr.Textbox(label="Video URL for Face Detection")
detect_faces_output = gr.Textbox(label="Face Detection Results")
gr.Button("Detect Faces").click(fn=detect_faces, inputs=detect_faces_input, outputs=detect_faces_output)
gr.Markdown("### Google Drive Download")
gdrive_url_input = gr.Textbox(label="Google Drive Folder URL")
gdrive_output = gr.Textbox(label="Download Results")
gr.Button("Download from Google Drive").click(fn=download_gdrive, inputs=gdrive_url_input, outputs=gdrive_output)
app.launch()
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