import gradio as gr
import torch
import torchvision.transforms as transforms
from sentence_transformers import SentenceTransformer, util
import json
import os
import matplotlib.pyplot as plt
import random

import torch
import torch.nn as nn

# The Generator model
class Generator(nn.Module):
    def __init__(self, channels, noise_dim=100, embed_dim=1024, embed_out_dim=128):
        super(Generator, self).__init__()
        self.channels = channels
        self.noise_dim = noise_dim
        self.embed_dim = embed_dim
        self.embed_out_dim = embed_out_dim

        # Text embedding layers
        self.text_embedding = nn.Sequential(
            nn.Linear(self.embed_dim, self.embed_out_dim),
            nn.BatchNorm1d(1),
            nn.LeakyReLU(0.2, inplace=True)
        )

        # Generator architecture
        model = []
        model += self._create_layer(self.noise_dim + self.embed_out_dim, 512, 4, stride=1, padding=0)
        model += self._create_layer(512, 256, 4, stride=2, padding=1)
        model += self._create_layer(256, 128, 4, stride=2, padding=1)
        model += self._create_layer(128, 64, 4, stride=2, padding=1)
        model += self._create_layer(64, 32, 4, stride=2, padding=1)
        model += self._create_layer(32, self.channels, 4, stride=2, padding=1, output=True)

        self.model = nn.Sequential(*model)

    def _create_layer(self, size_in, size_out, kernel_size=4, stride=2, padding=1, output=False):
        layers = [nn.ConvTranspose2d(size_in, size_out, kernel_size, stride=stride, padding=padding, bias=False)]
        if output:
            layers.append(nn.Tanh())  # Tanh activation for the output layer
        else:
            layers += [nn.BatchNorm2d(size_out), nn.ReLU(True)]  # Batch normalization and ReLU for other layers
        return layers

    def forward(self, noise, text):
        # Apply text embedding to the input text
        text = self.text_embedding(text)
        text = text.view(text.shape[0], text.shape[2], 1, 1)  # Reshape to match the generator input size
        z = torch.cat([text, noise], 1)  # Concatenate text embedding with noise
        return self.model(z)

noise_dim = 16
embed_dim = 384
embed_out_dim = 256
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Path to your .pth file
gen_weight = 'generator_20240421_3.pth'

# Load the weights
weights_gen = torch.load(gen_weight, map_location=torch.device(device))

# Apply the weights to your model
generator.load_state_dict(weights_gen)
        

# Load your model and other components here
model = SentenceTransformer('sentence-transformers/all-MiniLM-L12-v2')
with open(os.path.join("descriptions.json"), 'r') as file:
    dataset = json.load(file)

classes = [e["text"] for e in dataset]
embeddings_list = {cls: model.encode(cls, convert_to_tensor=True) for cls in classes}

def generate_image(caption):
    noise_dim = 16
    results = [(util.pytorch_cos_sim(model.encode(caption, convert_to_tensor=True), embeddings[cls]).item(), cls) for cls in classes]
    sorted_results = sorted(results, key=lambda x: x[0], reverse=True)[:5]

    threshold = 0.40
    coeff = 0.89

    if sorted_results[0][0] <= threshold:
        caption = sorted_results[0][1]
        results = [(util.pytorch_cos_sim(model.encode(caption, convert_to_tensor=True), embeddings[cls]).item(), cls) for cls in classes]
        sorted_results = sorted(results, key=lambda x: x[0], reverse=True)[:5]

    if sorted_results[0][0] >= 0.99:
        coeff = 0.85

    last_score = sorted_results[0][0]
    filtered_results = []
    for score, cls in sorted_results:
        if score >= last_score * coeff:
            filtered_results.append((score, cls))
            last_score = score
        else:
            break

    items = [cls for score, cls in filtered_results]
    probabilities = [score for score, cls in filtered_results]
    sampled_item = random.choices(items, weights=probabilities, k=1)[0]

    noise = torch.randn(1, noise_dim, 1, 1, device=device)  # Adjust noise_dim if different
    fake_images = generator(noise, embeddings[sampled_item].unsqueeze(0).unsqueeze(0))
    img = fake_images.squeeze(0).permute(1, 2, 0).cpu().detach().numpy()
    img = (img - img.min()) / (img.max() - img.min())

    return img

iface = gr.Interface(fn=generate_image,
                     inputs=gr.Textbox(lines=2, placeholder="Enter Caption Here..."),
                     outputs=gr.Image(type="numpy"),
                     title="Text-to-Image Generation",
                     description="Enter a caption to generate an image.")

iface.launch()