initial version of my own gradio app.

app.py

@@ -1,10 +1,11 @@
+import os
 from transformers import AutoModelForSequenceClassification
 import gradio as gr
 import numpy as np
 import random
 
 # import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
+# from diffusers import DiffusionPipeline
 import torch
 
 device = "cuda" if torch.cuda.is_available() else "cpu"
@@ -16,9 +17,12 @@ else:
     torch_dtype = torch.float32
 
 
-from article_classifier.dataset import labels, id2label, label2id, categorie2human
+from dataset import labels, id2label, label2id, categorie2human
+
+# model_path = "distilbert/distilbert-base-cased" # todo, replace with hacker1337/article-classifier
+# model_path = os.path.expanduser(r"~\cache\huggingface\checkpoints\distilbert-arxiv\runs\Jun21_00-41-18_amir-xp")
+model_path = os.path.expanduser("~/.cache/huggingface/checkpoints/distilbert-arxiv")
 
-model_path = "distilbert/distilbert-base-cased" # todo, replace with hacker1337/article-classifier
 model = AutoModelForSequenceClassification.from_pretrained(
     model_path,
     num_labels=len(id2label),
@@ -27,12 +31,6 @@ model = AutoModelForSequenceClassification.from_pretrained(
     problem_type="multi_label_classification",
 )
 
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
 
 # @spaces.GPU #[uncomment to use ZeroGPU]
 def infer(
@@ -51,18 +49,35 @@ def infer(
 
     generator = torch.Generator().manual_seed(seed)
 
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
+    # image = pipe(
+    #     prompt=prompt,
+    #     negative_prompt=negative_prompt,
+    #     guidance_scale=guidance_scale,
+    #     num_inference_steps=num_inference_steps,
+    #     width=width,
+    #     height=height,
+    #     generator=generator,
+    # ).images[0]
+
+    # return image, seed
 
-    return image, seed
 
+examples_titles = [
+    "Survey on Semantic Stereo Matching",
+]
+examples_summaries = [
+    """Stereo matching is one of the widely used techniques for inferring depth from
+stereo images owing to its robustness and speed. It has become one of the major
+topics of research since it finds its applications in autonomous driving,
+robotic navigation, 3D reconstruction, and many other fields. Finding pixel
+correspondences in non-textured, occluded and reflective areas is the major
+challenge in stereo matching. Recent developments have shown that semantic cues
+from image segmentation can be used to improve the results of stereo matching.
+Many deep neural network architectures have been proposed to leverage the
+advantages of semantic segmentation in stereo matching. This paper aims to give
+a comparison among the state of art networks both in terms of accuracy and in
+terms of speed which are of higher importance in real-time applications.""",
+]
 
 examples = [
     "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
@@ -81,86 +96,38 @@ with gr.Blocks(css=css) as demo:
     with gr.Column(elem_id="col-container"):
         gr.Markdown(" # Text-to-Image Gradio Template")
 
-        with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
+        title_prompt = gr.Text(
+            label="Title Prompt",
+            show_label=False,
+            max_lines=1,
+            placeholder="Enter paper's title",
+            container=False,
+        )
+        summary_prompt = gr.Text(
+            label="Summary Prompt",
+            show_label=False,
+            max_lines=10,
+            placeholder="Enter paper's abstract",
+            container=False,
+        )
 
-            run_button = gr.Button("Run", scale=0, variant="primary")
+
+        run_button = gr.Button("Run", scale=0, variant="primary")
 
         result = gr.Image(label="Result", show_label=False)
 
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
-            )
-
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
+        # with gr.Accordion("Advanced Settings", open=False):
+
+        gr.Examples(examples=examples_titles, inputs=[title_prompt])
+        gr.Examples(examples=examples_summaries, inputs=[summary_prompt])
     gr.on(
-        triggers=[run_button.click, prompt.submit],
+        triggers=[run_button.click, title_prompt.submit, summary_prompt.submit],
         fn=infer,
         inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
+            title_prompt,
+            summary_prompt,
         ],
-        outputs=[result, seed],
+        outputs=[result],
     )
 
 if __name__ == "__main__":