import torch
import gradio as gr
import torch.nn as nn
import torchvision
import cv2
import numpy as np
import tempfile



class MyModel(nn.Module):
    def __init__(self, num_classes=1):
        super(MyModel, self).__init__()  # Initialize nn.Module

        self.model = torchvision.models.video.r3d_18(pretrained=True)

        self.model.fc = nn.Linear(self.model.fc.in_features, num_classes)

    def preprocess_video(self, video_path, num_frames=40):
        """Preprocess video: sample frames, resize, normalize, and return tensor."""
        cap = cv2.VideoCapture(video_path)
        total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
        frame_indices = np.linspace(0, total_frames - 1, num=num_frames, dtype=int)
        sampled_frames = []

        for idx in frame_indices:
            cap.set(cv2.CAP_PROP_POS_FRAMES, idx)
            ret, frame = cap.read()
            if not ret:
                continue
            frame = cv2.resize(frame, (112, 112))  # Resize to 112x112 for R3D-18
            frame = np.transpose(frame, (2, 0, 1))  # Channels-first
            sampled_frames.append(frame)

        cap.release()

        if len(sampled_frames) < num_frames:
            padding = np.zeros((num_frames - len(sampled_frames), 3, 112, 112))
            sampled_frames = np.concatenate([sampled_frames, padding], axis=0)

        # Convert to tensor and rearrange dimensions to (3, num_frames, 112, 112)
        return torch.tensor(sampled_frames).float().permute(1, 0, 2, 3).unsqueeze(0)


    def forward(self, x):
        return self.model(x)

    def predict(self, video_path):
        """Test the model on the given videos and compute accuracy."""


        self.model.eval()

        predictions = []

        with torch.no_grad():

              X = self.preprocess_video(video_path)

              output = self.model(X)
              pred = torch.sigmoid(output)  # Apply sigmoid for binary classification

              # Track predictions
              predictions.append(pred.item())


        return predictions

    def save_model(self, filepath):
        torch.save({
            'model_state_dict': self.state_dict(),
        }, filepath)


    @staticmethod
    def load_model(filepath, num_classes=1):
        model = MyModel(num_classes)
        checkpoint = torch.load(filepath, weights_only=True)  
        model.load_state_dict(checkpoint['model_state_dict'])
 
        model.eval()  
        return model
    

model = MyModel().load_model('pre_3D_model.h5')



def classify_video(video):
    prob = model.predict(video)

    label = "Non-violent" if prob[0] >= 0.5 else "Violent"
    violent_prob_percentage = f"{round((1 - prob[0]) * 100, 2)}% chance of being violent"

    return label, violent_prob_percentage

# Set up the Gradio interface
interface = gr.Interface(
    fn=classify_video,
    inputs=gr.Video(),  # Allows video upload
    outputs=[
        gr.Text(label="Classification"),  # Label for classification output
        gr.Text(label="Violence Probability")  # Label for probability output with text
    ],
    title="Violence Detection in Videos",
    description="Upload a video to classify it as violent or non-violent with a probability score."
)

interface.launch(share=True, debug=True)