try run

app.py

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+import gradio as gr
+
+import os
+import glob
+import cv2
+import numpy as np
+import torch
+from rxnscribe import RxnScribe
+
+from huggingface_hub import hf_hub_download
+
+REPO_ID = "yujieq/RxnScribe"
+FILENAME = "pix2seq_reaction_full.ckpt"
+ckpt_path = hf_hub_download(REPO_ID, FILENAME)
+
+device = torch.device('cpu')
+model = RxnScribe(ckpt_path, device)
+
+
+def get_markdown(reaction):
+    output = []
+    for x in ['reactants', 'conditions', 'products']:
+        s = ''
+        for ent in reaction[x]:
+            if 'smiles' in ent:
+                s += ent['smiles'] + '<br>'
+            elif 'text' in ent:
+                s += ' '.join(ent['text']) + '<br>'
+            else:
+                s += ent['category']
+        output.append(s)
+    return output
+
+
+def predict(image, molscribe, ocr):
+    predictions = model.predict_image(image, molscribe=molscribe, ocr=ocr)
+    pred_images = model.draw_predictions(predictions, image=image)
+    markdown = [[i] + get_markdown(reaction) for i, reaction in enumerate(predictions)]
+    return pred_images, markdown
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        with gr.Row():
+            image = gr.Image(label="Upload reaction diagram", show_label=False, type='pil').style(height=256)
+        with gr.Row():
+            molscribe = gr.Checkbox(label="Run MolScribe to recognize molecule structures")
+            ocr = gr.Checkbox(label="Run OCR to recognize text")
+            btn = gr.Button("Submit").style(full_width=False)
+        with gr.Row():
+            gallery = gr.Gallery(
+                label="Predicted reactions", show_label=False, elem_id="gallery"
+            ).style(height="auto")
+            markdown = gr.Dataframe(
+                headers=['#', 'reactant', 'condition', 'product'],
+                datatype=['number'] + ['markdown'] * 3,
+                wrap=False
+            )
+
+    btn.click(predict, inputs=[image, molscribe, ocr], outputs=[gallery, markdown])
+
+demo.launch()