Update with main function

app.py

@@ -1,7 +1,92 @@
 import gradio as gr
 
-def greet(name):
-    return "Hello " + name + "!!"
+model_card = "microsoft/mdeberta-v3-base"
+finetuned_model = "MatteoFasulo/mdeberta-v3-base-subjectivity-sentiment-multilingual"
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+class CustomModel(PreTrainedModel):
+    config_class = DebertaV2Config
+
+    def __init__(self, config, sentiment_dim=3, num_labels=2, *args, **kwargs):
+        super().__init__(config, *args, **kwargs)
+        self.deberta = DebertaV2Model(config)
+        self.pooler = ContextPooler(config)
+        output_dim = self.pooler.output_dim
+        self.dropout = nn.Dropout(0.1)
+
+        self.classifier = nn.Linear(output_dim + sentiment_dim, num_labels)
+
+    def forward(self, input_ids, positive, neutral, negative, attention_mask=None, labels=None):
+        outputs = self.deberta(input_ids=input_ids, attention_mask=attention_mask)
+
+        encoder_layer = outputs[0]
+        pooled_output = self.pooler(encoder_layer)
+        
+        # Sentiment features as a single tensor
+        sentiment_features = torch.stack((positive, neutral, negative), dim=1)  # Shape: (batch_size, 3)
+        
+        # Combine CLS embedding with sentiment features
+        combined_features = torch.cat((pooled_output, sentiment_features), dim=1)
+        
+        # Classification head
+        logits = self.classifier(self.dropout(combined_features))
+        
+        return {'logits': logits}
+
+def load_tokenizer(model_name: str):
+    return AutoTokenizer.from_pretrained(model_name)
+
+# Load the pre-trained model
+def load_model(model_card: str, finetuned_model: str):
+    tokenizer = AutoTokenizer.from_pretrained(model_card)
+
+    config = DebertaV2Config.from_pretrained(
+        finetuned_model,
+        num_labels=2,
+        id2label={0: 'OBJ', 1: 'SUBJ'},
+        label2id={'OBJ': 0, 'SUBJ': 1},
+        output_attentions=False,
+        output_hidden_states=False
+    )
+
+    model = CustomModel(config=config, sentiment_dim=3, num_labels=2).from_pretrained(finetuned_model)
+
+    return model
+
+def get_sentiment_values(text: str):
+    pipe = pipeline("sentiment-analysis", model="cardiffnlp/twitter-xlm-roberta-base-sentiment", tokenizer="cardiffnlp/twitter-xlm-roberta-base-sentiment", top_k=None)
+    sentiments = pipe(text)[0]
+    return {k:v for k,v in [(list(sentiment.values())[0], list(sentiment.values())[1]) for sentiment in sentiments]}
+
+def predict_subjectivity(text):
+    sentiment_values = get_sentiment_values(text)
+
+    model = load_model(model_card, finetuned_model)
+    tokenizer = load_tokenizer(model_card)
+
+    inputs = tokenizer(text, padding=True, truncation=True, max_length=256, return_tensors='pt')
+
+    outputs = model(**inputs)
+    logits = outputs.get('logits')
+
+    predicted_class_idx = logits.argmax().item()
+    predicted_class = model.config.id2label[predicted_class_idx]
+
+    return predicted_class
+
+demo = gr.Interface(
+    fn=predict_subjectivity, 
+    inputs=gr.Textbox(
+        label='Input sentence',
+        placeholder='Enter a sentence from a news article',
+        info='Paste a sentence from a news article to determine if it is subjective or objective.'
+    ),
+    outputs=gr.Text(
+        label="Prediction",
+        info="Whether the sentence is subjective or objective."
+    ),
+    title='Subjectivity Detection',
+    description='Detect if a sentence is subjective or objective using a pre-trained model.'
+    theme='huggingface',
+)
+
+demo.launch()