Delete app_old.py

app_old.py

@@ -1,105 +0,0 @@
-import gradio as gr
-import torch
-import numpy as np
-
-import pandas as pd
-from tqdm import tqdm
-
-import matplotlib.pyplot as plt
-
-from transformers import AutoTokenizer, AutoConfig, AutoModel, AutoModelForSequenceClassification
-
-
-description_sentence = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotion in a sentence."
-description_dataset = "<h3>Demo EmotioNL</h3>\nThis demo allows you to analyse the emotions in a dataset.\nThe data should be in tsv-format with two named columns: the first column (id) should contain the sentence IDs, and the second column (text) should contain the actual texts. Optionally, there is a third column named 'date', which specifies the date associated with the text (e.g., tweet date). This column is necessary when the options 'emotion distribution over time' and 'peaks' are selected."
-
-inference_modelpath = "model/checkpoint-128"
-
-def inference_sentence(text):
-    tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
-    model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
-    for text in tqdm([text]):
-        inputs = tokenizer(text, return_tensors="pt")
-    with torch.no_grad(): # run model
-        logits = model(**inputs).logits
-        predicted_class_id = logits.argmax().item()
-    output = model.config.id2label[predicted_class_id]
-    return output
-
-def frequencies(preds):
-	preds_dict = {"neutral": 0, "anger": 0, "fear": 0, "joy": 0, "love": 0, "sadness": 0}
-	for pred in preds:
-		preds_dict[pred] = preds_dict[pred] + 1
-	bars = list(preds_dict.keys())
-	height = list(preds_dict.values())
-
-	x_pos = np.arange(len(bars))
-	plt.bar(x_pos, height, color=['lightgrey', 'firebrick', 'rebeccapurple', 'orange', 'palevioletred', 'cornflowerblue'])
-	plt.xticks(x_pos, bars)
-	return plt
-    
-def inference_dataset(file_object, option_list):
-    tokenizer = AutoTokenizer.from_pretrained(inference_modelpath)
-    model = AutoModelForSequenceClassification.from_pretrained(inference_modelpath)
-    data_path = open(file_object.name, 'r')
-    df = pd.read_csv(data_path, delimiter='\t', header=0, names=['id', 'text'])
-    ids = df["id"].tolist()
-    texts = df["text"].tolist()
-    preds = []
-    for text in tqdm(texts): # progressbar
-        inputs = tokenizer(text, return_tensors="pt")
-        with torch.no_grad(): # run model
-            logits = model(**inputs).logits
-        predicted_class_id = logits.argmax().item()
-        prediction = model.config.id2label[predicted_class_id]
-        preds.append(prediction)
-    predictions_content = list(zip(ids, texts, preds))
-    # write predictions to file
-    output = "output.txt"
-    f = open(output, 'w')
-    f.write("id\ttext\tprediction\n")
-    for line in predictions_content:
-        f.write(str(line[0]) + '\t' + str(line[1]) + '\t' + str(line[2]) + '\n')
-    output1 = output
-    output2 = output3 = output4 = output5 = "This option was not selected."
-    if "emotion frequencies" in option_list:
-        output2 = frequencies(preds)
-    else:
-        output2 = None
-    if "emotion distribution over time" in option_list:
-        output3 = "This option was selected."
-    if "peaks" in option_list:
-        output4 = "This option was selected."
-    if "topics" in option_list:
-        output5 = "This option was selected."
-    return [output1, output2, output3, output4, output5]
-
-iface_sentence = gr.Interface(
-            fn=inference_sentence,
-            description = description_sentence,
-            inputs = gr.Textbox(
-                    label="Enter a sentence",
-                    lines=1),
-            outputs="text")
-
-inputs = [gr.File(
-            label="Upload a dataset"),
-          gr.CheckboxGroup(
-            ["emotion frequencies", "emotion distribution over time", "peaks", "topics"],
-            label = "Select options")]
-
-outputs = [gr.File(),
-           gr.Plot(label="Emotion frequencies"),
-           gr.Textbox(label="Emotion distribution over time"),
-           gr.Textbox(label="Peaks"),
-           gr.Textbox(label="Topics")]
-
-iface_dataset = gr.Interface(
-            fn = inference_dataset,
-            description = description_dataset,
-            inputs=inputs,
-            outputs = outputs)
-
-iface = gr.TabbedInterface([iface_sentence, iface_dataset], ["Sentence", "Dataset"])
-
-iface.queue().launch()