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import gradio as gr |
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import json |
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from datasets import load_dataset |
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from sentence_transformers import SentenceTransformer, InputExample, util |
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import pandas as pd |
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def Main(Modelo, Umbral, File): |
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error = "" |
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modelResult = "" |
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try: |
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data_test = ConvertJsonToList(File.name) |
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modelResult = TestModel('jfarray/Model_'+ Modelo +'_50_Epochs',data_test, Umbral) |
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except Exception as e: |
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error = e |
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return [error, modelResult] |
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def ConvertJsonToList(fileName): |
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subject_fileDataset = [] |
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with open(fileName, encoding='utf-8') as fh: |
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fileContent = fh.read().replace('}\n{','}\n,{') |
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subject_fileDataset = json.loads('[' + fileContent+ ']') |
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samples = [] |
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for i in range (0,len(subject_fileDataset)): |
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hashed_id = subject_fileDataset[i]['hashed_id'] |
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mark = subject_fileDataset[i]['nota'] |
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responseStudent = subject_fileDataset[i]['respuesta'] |
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responseTeacher = "" |
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for j in range(0,len(subject_fileDataset[i]['metadata']['minipreguntas'])): |
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responseTeacher = responseTeacher + subject_fileDataset[i]['metadata']['minipreguntas'][j]['minirespuesta'] |
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ie = InputExample(guid= hashed_id, texts=[responseTeacher, responseStudent], label=mark) |
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samples.append(ie) |
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return samples |
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def TestModel(checkpoint, data, Umbral): |
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local_model_path = checkpoint |
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model = SentenceTransformer(local_model_path) |
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df = pd.DataFrame(columns=["Hashed_id", "Nota", "Similitud Semántica", "Supera Umbral"]) |
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sentences1 = [] |
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sentences2 = [] |
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hashed_ids = [] |
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marks = [] |
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scores = [] |
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thresholds = [] |
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for i in range (0,len(data)): |
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sentences1.append(data[i].texts[0]) |
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sentences2.append(data[i].texts[1]) |
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embeddings1 = model.encode(sentences1, convert_to_tensor=True) |
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embeddings2 = model.encode(sentences2, convert_to_tensor=True) |
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cosine_scores = util.cos_sim(embeddings1, embeddings2) |
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for i in range(len(sentences1)): |
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hashed_ids.append(data[i].guid) |
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marks.append(data[i].label) |
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scores.append(round(cosine_scores[i][i].item(),3)) |
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umbralDiferencia = data[i].label * (Umbral/100) |
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if ( abs(round(data[i].label,3) - round(cosine_scores[i][i].item(),3)) >= umbralDiferencia ): |
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thresholds.append("SI") |
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else: |
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thresholds.append("") |
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df['Hashed_id'] = hashed_ids |
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df['Nota'] = marks |
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df['Similitud Semántica'] = scores |
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df['Supera Umbral'] = thresholds |
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return df |
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Modelos = gr.inputs.Dropdown(["dccuchile_bert-base-spanish-wwm-uncased" |
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, "bert-base-multilingual-uncased" |
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, "all-distilroberta-v1" |
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, "paraphrase-multilingual-mpnet-base-v2" |
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, "paraphrase-multilingual-MiniLM-L12-v2" |
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, "distiluse-base-multilingual-cased-v1"]) |
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Umbral = gr.inputs.Slider(minimum=0, maximum=100, step=None, default=15, label=None) |
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FileInput = gr.inputs.File(file_count="single", type="file", label="Fichero Json") |
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LabelOutput = gr.outputs.Label(num_top_classes=None, type="auto", label="") |
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DataFrameOutput = gr.outputs.Dataframe(headers=["Hashed_id", "Nota", "Similitud Semántica", "Supera Umbral"] |
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, max_rows=20, max_cols=4, overflow_row_behaviour="paginate", type="pandas", label="Resultado") |
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iface = gr.Interface(fn=Main |
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, inputs=[Modelos, Umbral, FileInput] |
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, outputs=[LabelOutput, DataFrameOutput] |
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, examples=[["dccuchile_bert-base-spanish-wwm-uncased", 10, "TestFile.json"]] |
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, title = "Similitud Semántica de textos en Español de tamaño medio (200-250 palabras)" |
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) |
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iface.launch(share = False,enable_queue=True, show_error =True) |
