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| from cProfile import label | |
| from fastapi import File | |
| import gradio as gr | |
| from gib_detect_module import detect | |
| import csv | |
| from transformers import AutoModelForSequenceClassification, AutoTokenizer | |
| DLmodel = AutoModelForSequenceClassification.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) | |
| tokenizer = AutoTokenizer.from_pretrained("madhurjindal/autonlp-Gibberish-Detector-492513457", use_auth_token=True) | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| def detect_gibberish(line,f): | |
| if line: | |
| if detect(line): | |
| return "Valid!!!!", None,None | |
| else: | |
| return "Bollocks Giberrish",None,None | |
| elif f: | |
| return None, annotate_csv(f), None | |
| def annotate_csv(f): | |
| with open(f.name) as csvfile: | |
| creader = csv.reader(csvfile, delimiter=',', quotechar='"') | |
| with open('out.csv', 'w', newline='') as csvout: | |
| cwriter = csv.writer(csvout, delimiter=',', | |
| quotechar='"', quoting=csv.QUOTE_MINIMAL) | |
| for row in creader: | |
| row.append(str(detect(row[0]))) | |
| cwriter.writerow(row) | |
| return "out.csv" | |
| def annotate_csv_deep(f): | |
| labels = DLmodel.config.id2label | |
| with open(f.name) as csvfile: | |
| creader = csv.reader(csvfile, delimiter=',', quotechar='"') | |
| with open('out.csv', 'w', newline='') as csvout: | |
| cwriter = csv.writer(csvout, delimiter=',', | |
| quotechar='"', quoting=csv.QUOTE_MINIMAL) | |
| for row in creader: | |
| inputs = tokenizer(row, return_tensors="pt") | |
| outputs = DLmodel(**inputs) | |
| probs = outputs.logits.softmax(dim=-1).detach().cpu().flatten().numpy().tolist() | |
| idx = probs.index(max(probs)) | |
| row.append(labels[idx]) | |
| row.append("{:.0%}".format(probs[idx]) ) | |
| cwriter.writerow(row) | |
| return "out.csv" | |
| def detect_gibberish_deep(line,f): | |
| if line: | |
| inputs = tokenizer(line, return_tensors="pt") | |
| labels = DLmodel.config.id2label | |
| outputs = DLmodel(**inputs) | |
| probs = outputs.logits.softmax(dim=-1).detach().cpu().flatten().numpy().tolist() | |
| output=dict(zip(labels.values(), probs)) | |
| readable_output="" | |
| for k,v in output.items(): | |
| readable_output+=k+" : "+ "{:.0%}".format(v) + "\n" | |
| return readable_output, None, output | |
| if f: | |
| return None, annotate_csv_deep(f),None | |
| def detect_gibberish_abstract(model, line,f): | |
| if model == "Deep Learning Model": | |
| return detect_gibberish_deep(line,f) | |
| else: | |
| return detect_gibberish(line, f) | |
| inputLine=gr.inputs.Textbox(lines=1, placeholder="Input text here, if both text and file have values, only the text input will be processed.", default="", label="Text", optional=False) | |
| inputFile=gr.inputs.File(file_count="single", type="file", label="File to Annotate", optional=True) | |
| choices = ["Deep Learning Model", "Markov Chain"] | |
| inputModel=gr.inputs.Dropdown(choices) | |
| outputLine=gr.outputs.Textbox(type="auto", label=None) | |
| outputFile=gr.outputs.File( label="Annotated CSV") | |
| label = gr.outputs.Label(num_top_classes=4) | |
| examples=[ | |
| ["Deep Learning Model","quetzalcoatl","demo_blank.csv"], | |
| ["Deep Learning Model","aasdf","demo_blank.csv"], | |
| ["Deep Learning Model","Covfefe","demo_blank.csv"], | |
| ["Markov Chain","quetzalcoatl","demo_blank.csv"], | |
| ["Markov Chain","aasdf","demo_blank.csv"], | |
| ["Markov Chain","Covfefe","demo_blank.csv"], | |
| ["Deep Learning Model","","demo_bad.txt"], | |
| ["Deep Learning Model","","demo_mixed.txt"], | |
| ["Markov Chain","","demo_bad.txt"], | |
| ["Markov Chain","","demo_mixed.txt"], | |
| ] | |
| #iface = gr.Interface(fn=[detect_gibberish], inputs=["text",inputFile], outputs=["text",outputFile],examples=examples, allow_flagging='never') | |
| #iface.launch() | |
| iface = gr.Interface(fn=[detect_gibberish_abstract], inputs=[inputModel,inputLine,inputFile], outputs=["text",outputFile,label],examples=examples, allow_flagging='never') | |
| iface.launch() |