app.py

import gradio as gr
import torch
import numpy as np
from transformers import AutoProcessor, AutoModel
from PIL import Image
import cv2
from pathlib import Path
from tempfile import NamedTemporaryFile

MODEL_NAME = "microsoft/xclip-base-patch16-zero-shot"
CLIP_LEN = 32

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME).to(device)

def get_video_length(file_path):
    cap = cv2.VideoCapture(file_path)
    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    cap.release()
    return length

def read_video_opencv(file_path, indices):
    frames = []
    failed_indices = []
    
    cap = cv2.VideoCapture(file_path)
    if not cap.isOpened():
        print(f"Error opening video file: {file_path}")
        return frames

    max_index = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
    for idx in indices:
        if idx <= max_index:
            frame = get_frame_with_opened_cap(cap, idx)
            if frame is not None:
                frames.append(frame)
            else:
                failed_indices.append(idx)
        else:
            failed_indices.append(idx)
    cap.release()

    if failed_indices:
        print(f"Failed to extract frames at indices: {failed_indices}")
    return frames

def get_frame_with_opened_cap(cap, index):
    cap.set(cv2.CAP_PROP_POS_FRAMES, index)
    ret, frame = cap.read()
    if ret:
        return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
    return None

def sample_uniform_frame_indices(clip_len, seg_len):
    if seg_len < clip_len:
        repeat_factor = np.ceil(clip_len / seg_len).astype(int)
        indices = np.arange(seg_len).tolist() * repeat_factor
        indices = indices[:clip_len]
    else:
        spacing = seg_len // clip_len
        indices = [i * spacing for i in range(clip_len)]
    return np.array(indices).astype(np.int64)

def concatenate_frames(frames, clip_len):
    layout = { 32: (4, 8) }
    rows, cols = layout[clip_len]
    combined_image = Image.new('RGB', (frames[0].shape[1]*cols, frames[0].shape[0]*rows))
    frame_iter = iter(frames)
    y_offset = 0
    for i in range(rows):
        x_offset = 0
        for j in range(cols):
            img = Image.fromarray(next(frame_iter))
            combined_image.paste(img, (x_offset, y_offset))
            x_offset += frames[0].shape[1]
        y_offset += frames[0].shape[0]
    return combined_image

def model_interface(uploaded_video, activity):
    video_length = get_video_length(uploaded_video)
    indices = sample_uniform_frame_indices(CLIP_LEN, seg_len=video_length)
    video = read_video_opencv(uploaded_video, indices)
    concatenated_image = concatenate_frames(video, CLIP_LEN)

    activities_list = [activity, "other"]
    inputs = processor(
        text=activities_list,
        videos=list(video),
        return_tensors="pt",
        padding=True,
    )

    for key, value in inputs.items():
        if isinstance(value, torch.Tensor):
            inputs[key] = value.to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    logits_per_video = outputs.logits_per_video
    probs = logits_per_video.softmax(dim=1)

    results_probs = []
    results_logits = []
    max_prob_index = torch.argmax(probs[0]).item()
    for i in range(len(activities_list)):
        current_activity = activities_list[i]
        prob = float(probs[0][i].cpu())  
        logit = float(logits_per_video[0][i].cpu())  
        results_probs.append((current_activity, f"Probability: {prob * 100:.2f}%"))
        results_logits.append((current_activity, f"Raw Score: {logit:.2f}"))

    likely_label = activities_list[max_prob_index]
    likely_probability = float(probs[0][max_prob_index].cpu()) * 100  

    return concatenated_image, results_probs, results_logits, [likely_label, likely_probability]

iface = gr.Interface(
    fn=model_interface,
    inputs=[
        gr.Video(label="Upload a Video"),
        gr.Textbox(label="Activity to Detect")
    ],
    outputs=[
        gr.Image(label="Concatenated Frames"),
        gr.Dataframe(headers=["Activity", "Probability"], label="Probabilities"),
        gr.Dataframe(headers=["Activity", "Raw Score"], label="Raw Scores"),
        gr.Textbox(label="Most Likely Activity")
    ],
    title="Video Activity Classifier",
    description="""
    **Instructions:**

    1. **Upload a Video**: Select a video file to upload. 
    2. **Enter Activity Label**: Specify the activity you want to detect in the video.
    3. **View Results**: 
       - The concatenated frames from the video will be displayed.
       - Probabilities and raw scores for the specified activity and the "other" category will be shown.
       - The most likely activity detected in the video will be displayed.
    """
)

if __name__ == "__main__":
    iface.launch()