app.py

import gradio as gr
import torch
import numpy as np
from transformers import AutoProcessor, AutoModel
from PIL import Image
import cv2 

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

# Check for GPU and set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load model and processor
processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME).to(device).eval()

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):
    cap = cv2.VideoCapture(file_path)
    frames = []
    for idx in indices:
        cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
        ret, frame = cap.read()
        if ret:
            frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
    cap.release()
    return frames

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 get_concatenation_layout(clip_len):
    # Modify as needed for other clip lengths
    if clip_len == 32:
        return 4, 8

def concatenate_frames(frames, clip_len):
    rows, cols = get_concatenation_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,
    ).to(device)  # Move inputs to GPU if available

    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])
        logit = float(logits_per_video[0][i])
        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]) * 100

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

iface = gr.Interface(
    fn=model_interface,
    inputs=[
        gr.components.Video(label="Upload a video file"),
        gr.components.Textbox(default="dancing", label="Desired Activity to Recognize"),
    ],
    outputs=[
        gr.components.Image(type="pil", label="Sampled Frames"),
        gr.components.Textbox(type="text", label="Probabilities"),
        gr.components.Textbox(type="text", label="Raw Scores"),
        gr.components.Textbox(type="text", label="Top Prediction")
    ],
    live=False
)

iface.launch()