add application file

app.py

@@ -0,0 +1,79 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+import numpy as np
+from corpy.morphodita import Tokenizer
+
+import transformers
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+model_checkpoint = 'ufal/robeczech-base'
+device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
+transformers.logging.set_verbosity(transformers.logging.ERROR)
+
+def classify_sentence(sent:str):
+    toksentence = tokenizer(sent,truncation=True,return_tensors="pt")
+    model.eval()
+    with torch.no_grad():
+        toksentence.to(device)
+        output = model(**toksentence)
+    
+    return F.softmax(output.logits,dim=1).argmax(dim=1)
+    
+def classify_text(text:str):
+    tokenizer_morphodita = Tokenizer("czech")
+
+    all = []
+    for sentence in tokenizer_morphodita.tokenize(text, sents=True):
+        all.append(sentence)
+
+    sentences = np.array([' '.join(x) for x in all])
+    annotations = np.array(list(map(classify_sentence,sentences)))
+    
+    return annotations
+    
+def classify_text_wrapper(text:str):
+    result = classify_text(text)
+    n = len(result)
+    non_biased = np.where(result==0)[0].shape[0]
+    biased = np.where(result==1)[0].shape[0]
+
+    return {'Non-biased':non_biased/n,'Biased':biased/n}
+    
+    
+def interpret_bias(text:str):
+    result = classify_text(text)
+    
+    tokenizer_morphodita = Tokenizer("czech")
+
+    interpretation = []
+    all = []
+    for sentence in tokenizer_morphodita.tokenize(text, sents=True):
+        all.append(sentence)
+    
+    sentences = np.array([' '.join(x) for x in all])
+    
+    for idx,sentence in enumerate(sentences):
+        score = 0
+        #non biased
+        if result[idx] == 0:
+            score = -1
+        #biased
+        if result[idx] == 1:
+            score = 1
+        interpretation.append((sentence, score))
+        
+    return interpretation
+    
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+model = AutoModelForSequenceClassification.from_pretrained("sagittariusA/media_bias_classifier_cs")
+model.eval()
+
+label = gr.outputs.Label(num_top_classes=2)
+inputs = gr.inputs.Textbox(placeholder=None, default="", label=None)
+app = gr.Interface(fn=classify_text_wrapper,title='Bias classifier',theme='default',
+                    inputs="textbox",layout='unaligned', outputs=label, capture_session=True
+                    ,interpretation=interpret_bias)
+
+app.launch(inbrowser=True)
+