-

app.py

@@ -0,0 +1,109 @@
+import os
+import torch
+import re
+import gradio as gr
+from pathlib import Path
+from transformers import AutoTokenizer, AutoFeatureExtractor, VisionEncoderDecoderModel
+
+
+# Pattern to ignore all the text after 2 or more full stops
+regex_pattern = "[.]{2,}"
+
+
+def post_process(text):
+    try:
+        text = text.strip()
+        text = re.split(regex_pattern, text)[0]
+    except Exception as e:
+        print(e)
+        pass
+    return text
+
+
+def set_example_image(example: list) -> dict:
+    return gr.Image.update(value=example[0])
+
+
+def predict(image, max_length=64, num_beams=4):
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+
+    with torch.no_grad():
+        output_ids = model.generate(
+            pixel_values,
+            max_length=max_length,
+            num_beams=num_beams,
+            return_dict_in_generate=True,
+        ).sequences
+
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    pred = post_process(preds[0])
+
+    return pred
+
+
+model_name_or_path = "deepklarity/poster2plot"
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+# Load model.
+
+model = VisionEncoderDecoderModel.from_pretrained(model_name_or_path)
+model.to(device)
+print("Loaded model")
+
+feature_extractor = AutoFeatureExtractor.from_pretrained(model.encoder.name_or_path)
+print("Loaded feature_extractor")
+
+tokenizer = AutoTokenizer.from_pretrained(model.decoder.name_or_path, use_fast=True)
+if model.decoder.name_or_path == "gpt2":
+    tokenizer.pad_token = tokenizer.eos_token
+
+print("Loaded tokenizer")
+
+title = "Poster2Plot: Upload a Movie/T.V show poster to generate a plot"
+description = ""
+
+input = gr.inputs.Image(type="pil")
+
+example_images = sorted(
+    [f.as_posix() for f in Path("examples").glob("*.jpg")]
+)
+print(f"Loaded {len(example_images)} example images")
+
+demo = gr.Blocks()
+filenames = next(os.walk('examples'), (None, None, []))[2]
+examples = [[f"examples/{filename}"] for filename in filenames]
+print(examples)
+
+with demo:
+    with gr.Column():
+        with gr.Row():
+            with gr.Column():
+                input_image = gr.Image()
+                with gr.Row():
+                    clear_button = gr.Button(value="Clear", variant='secondary')
+                    submit_button = gr.Button(value="Submit", variant='primary')
+            with gr.Column():
+                plot = gr.Textbox()
+        with gr.Row():
+            example_images = gr.Dataset(components=[input_image], samples=examples)
+
+    submit_button.click(fn=predict, inputs=[input_image], outputs=[plot])
+    example_images.click(fn=set_example_image, inputs=[example_images], outputs=example_images.components)
+
+demo.launch()
+
+
+interface = gr.Interface(
+    fn=predict,
+    inputs=input,
+    outputs="textbox",
+    title=title,
+    description=description,
+    examples=example_images,
+    examples_per_page=20,
+    live=True,
+    article='<p>Made by: <a href="https://twitter.com/kartik_godawat" target="_blank" rel="noopener noreferrer">dk-crazydiv</a> and <a href="https://twitter.com/dsr_ai" target="_blank" rel="noopener noreferrer">dsr</a></p>'
+)
+
+interface.launch()