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app.py

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+import os
+os.system('pip install paddlepaddle')
+os.system('pip install paddleocr')
+from paddleocr import PaddleOCR, draw_ocr
+from PIL import Image
+import gradio as gr
+import torch
+
+torch.hub.download_url_to_file('https://i.imgur.com/aqMBT0i.jpg', 'example.jpg')
+
+def inference(img, lang):
+    ocr = PaddleOCR(use_angle_cls=True, lang=lang,use_gpu=False)
+    img_path = img.name
+    result = ocr.ocr(img_path, cls=True)
+    image = Image.open(img_path).convert('RGB')
+    boxes = [line[0] for line in result]
+    txts = [line[1][0] for line in result]
+    # scores = [line[1][1] for line in result]
+    im_show = draw_ocr(image, boxes, txts,
+                       font_path='simfang.ttf')
+    im_show = Image.fromarray(im_show)
+    im_show.save('result.jpg')
+    return 'result.jpg'
+
+title = 'A Framework for Data-Driven Document Evaluation and scoring - Image to Text Extraction '
+description = 'Demo for Optical character recognition(OCR)'
+article = ""
+examples = [['example.jpg','en']]
+css = ".output_image, .input_image {height: 40rem !important; width: 100% !important;}"
+gr.Interface(
+    inference,
+    [gr.inputs.Image(type='file', label='Input'),gr.inputs.Dropdown(choices=['ch', 'en', 'fr', 'german', 'korean', 'japan'], type="value", default='en', label='language')],
+    gr.outputs.Image(type='file', label='Output'),
+    title=title,
+    description=description,
+    article=article,
+    examples=examples,
+    css=css,
+    enable_queue=True
+    ).launch(debug=True)
+    
+    
+    ##########################################################################################################
+    
+    import os
+import gradio as gr
+from huggingface_hub import snapshot_download
+from prettytable import PrettyTable
+import pandas as pd
+import torch
+import traceback
+
+config = {
+    "model_type": "roberta",
+    "model_name_or_path": "roberta-large",
+    "logic_lambda": 0.5,
+    "prior": "random",
+    "mask_rate": 0.0,
+    "cand_k": 1,
+    "max_seq1_length": 256,
+    "max_seq2_length": 128,
+    "max_num_questions": 8,
+    "do_lower_case": False,
+    "seed": 42,
+    "n_gpu": torch.cuda.device_count(),
+}
+
+os.system('git clone https://github.com/kkpathak91/project_metch/')
+os.system('rm -r project_metch/data/')
+os.system('rm -r project_metch/results/')
+os.system('rm -r project_metch/models/')
+os.system('mv project_metch/* ./')
+
+model_dir = snapshot_download('kkpathak91/FVM')
+config['fc_dir'] = os.path.join(model_dir, 'fact_checking/roberta-large/')
+config['mrc_dir'] = os.path.join(model_dir, 'mrc_seq2seq/bart-base/')
+config['er_dir'] = os.path.join(model_dir, 'evidence_retrieval/')
+
+
+from src.loren import Loren
+
+
+loren = Loren(config, verbose=False)
+try:
+    js = loren.check('Donald Trump won the 2020 U.S. presidential election.')
+except Exception as e:
+    raise ValueError(e)
+
+
+def highlight_phrase(text, phrase):
+    text = loren.fc_client.tokenizer.clean_up_tokenization(text)
+    return text.replace('<mask>', f'<i><b>{phrase}</b></i>')
+
+
+def highlight_entity(text, entity):
+    return text.replace(entity, f'<i><b>{entity}</b></i>')
+
+
+def gradio_formatter(js, output_type):
+    zebra_css = '''
+    tr:nth-child(even) {
+        background: #f1f1f1;
+    }
+    thead{
+        background: #f1f1f1;
+    }'''
+    if output_type == 'e':
+        data = {'Evidence': [highlight_entity(x, e) for x, e in zip(js['evidence'], js['entities'])]}
+    elif output_type == 'z':
+        p_sup, p_ref, p_nei = [], [], []
+        for x in js['phrase_veracity']:
+            max_idx = torch.argmax(torch.tensor(x)).tolist()
+            x = ['%.4f' % xx for xx in x]
+            x[max_idx] = f'<i><b>{x[max_idx]}</b></i>'
+            p_sup.append(x[2])
+            p_ref.append(x[0])
+            p_nei.append(x[1])
+
+        data = {
+            'Claim Phrase': js['claim_phrases'],
+            'Local Premise': [highlight_phrase(q, x[0]) for q, x in zip(js['cloze_qs'], js['evidential'])],
+            'p_SUP': p_sup,
+            'p_REF': p_ref,
+            'p_NEI': p_nei,
+        }
+    else:
+        raise NotImplementedError
+    data = pd.DataFrame(data)
+    pt = PrettyTable(field_names=list(data.columns), 
+        align='l', border=True, hrules=1, vrules=1)
+    for v in data.values:
+        pt.add_row(v)
+    html = pt.get_html_string(attributes={
+        'style': 'border-width: 2px; bordercolor: black'
+    }, format=True)
+    html = f'<head> <style type="text/css"> {zebra_css} </style> </head>\n' + html
+    html = html.replace('&lt;', '<').replace('&gt;', '>')
+    return html
+
+
+def run(claim):
+    try:
+        js = loren.check(claim)
+    except Exception as error_msg:
+        exc = traceback.format_exc()
+        msg = f'[Error]: {error_msg}.\n[Traceback]: {exc}'
+        loren.logger.error(claim)
+        loren.logger.error(msg)
+        return 'Oops, something went wrong.', '', ''
+    label = js['claim_veracity']
+    loren.logger.warning(label + str(js))
+    ev_html = gradio_formatter(js, 'e')
+    z_html = gradio_formatter(js, 'z')
+    return label, z_html, ev_html
+
+
+iface = gr.Interface(
+    fn=run,
+    inputs="text",
+    outputs=[
+        'text',
+        'html',
+        'html',
+    ],
+    examples=['Kanpur is a city in Nepal',
+              'PV Sindhu is an Indian Badminton Player.'],
+    title="A Framework for Data-Driven Document Evaluation and Scoring",
+    layout='horizontal',
+    description="[Student Name: Karan Kumar Pathak] " " [Roll No.: 2020fc04334] ",
+    flagging_dir='results/flagged/',
+    allow_flagging=True,
+    flagging_options=['Interesting!', 'Error: Claim Phrase Parsing', 'Error: Local Premise',
+                      'Error: Require Commonsense', 'Error: Evidence Retrieval'],
+    enable_queue=True
+)
+iface.launch()