Upload app.py

app.py

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+
+import gradio as gr
+import torch
+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer
+from transformers import pipeline
+
+import torch
+import os
+import numpy as np
+from matplotlib import pyplot as plt
+from PIL import Image
+
+from pytorch_pretrained_biggan import BigGAN, truncated_noise_sample, one_hot_from_names, one_hot_from_int
+
+config = {
+    "model_name": "smangrul/Multimodal-Challenge",
+    "base_model_name": "distilbert-base-uncased",
+    "image_gen_model": "biggan-deep-128",
+    "max_length": 20,
+    "freeze_text_model": True,
+    "freeze_image_gen_model": True,
+    "text_embedding_dim": 768,
+    "class_embedding_dim": 128
+}
+truncation=0.4
+
+is_gpu = False
+device = torch.device('cuda') if is_gpu else torch.device('cpu')
+print(device)
+
+model = AutoModelForSequenceClassification.from_pretrained(config["model_name"], use_auth_token=os.environ.get(
+    'huggingface-api-token'))
+tokenizer = AutoTokenizer.from_pretrained(config["base_model_name"])
+model.to(device)
+model.eval()
+
+gan_model = BigGAN.from_pretrained(config["image_gen_model"])
+gan_model.to(device)
+gan_model.eval()
+print("Models were loaded")
+
+
+def generate_image(dense_class_vector=None, int_index=None, noise_seed_vector=None, truncation=0.4):
+    seed = int(noise_seed_vector.sum().item()) if noise_seed_vector is not None else None
+    noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1, seed=seed)
+    noise_vector = torch.from_numpy(noise_vector)
+    if int_index is not None:
+        class_vector = one_hot_from_int([int_index], batch_size=1)
+        class_vector = torch.from_numpy(class_vector)
+        dense_class_vector = gan_model.embeddings(class_vector)
+    else:
+        if isinstance(dense_class_vector, np.ndarray):
+            dense_class_vector = torch.tensor(dense_class_vector)
+        dense_class_vector = dense_class_vector.view(1, 128)
+
+    input_vector = torch.cat([noise_vector, dense_class_vector], dim=1)
+
+    # Generate an image
+    with torch.no_grad():
+        output = gan_model.generator(input_vector, truncation)
+    output = output.cpu().numpy()
+    output = output.transpose((0, 2, 3, 1))
+    output = ((output + 1.0) / 2.0) * 256
+    output.clip(0, 255, out=output)
+    output = np.asarray(np.uint8(output[0]), dtype=np.uint8)
+    return output
+
+
+def print_image(numpy_array):
+    """ Utility function to print a numpy uint8 array as an image
+    """
+    img = Image.fromarray(numpy_array)
+    plt.imshow(img)
+    plt.show()
+
+
+def text_to_image(text):
+    tokens = tokenizer.encode(text, add_special_tokens=True, return_tensors='pt').to(device)
+    with torch.no_grad():
+        lm_output = model(tokens, return_dict=True)
+        pred_int_index = torch.argmax(lm_output.logits[0], dim=-1).cpu().detach().numpy().tolist()
+        print(pred_int_index)
+
+    # Now generate an image (a numpy array)
+    numpy_image = generate_image(int_index=pred_int_index,
+                                 truncation=truncation,
+                                 noise_seed_vector=tokens)
+
+    img = Image.fromarray(numpy_image)
+    #print_image(numpy_image)
+    return img
+
+examples = ["a high resoltuion photo of a pizza from famous food magzine.",
+            "this is a photo of my pet golden retriever.",
+            "this is a photo of a trouble some street cat.",
+            "a blur image of coral reef.",
+            "a yellow taxi cab commonly found in USA.",
+            "Once upon a time, there was a black ship full of pirates.",
+            "a photo of a large castle.",
+            "a sketch of an old Church"]
+
+if __name__ == '__main__':
+    interFace = gr.Interface(fn=text_to_image,
+                             inputs=gr.inputs.Textbox(placeholder="Enter the text to generate an image", label="Text "
+                                                                                                           "query",
+                                                      lines=1),
+                             outputs=gr.outputs.Image(type="auto", label="Generated Image"),
+                             verbose=True,
+                             examples=examples,
+                             title="Generate Image from Text",
+                             description="",
+                             theme="huggingface")
+    interFace.launch()