Upload 3 files

README.md

@@ -1,12 +1,13 @@
 ---
-title: CSC502 MIMIC4RAG
-emoji: 🐨
-colorFrom: yellow
-colorTo: yellow
+title: CSC502 MLTC
+emoji: 🚀
+colorFrom: pink
+colorTo: green
 sdk: gradio
-sdk_version: 5.3.0
+sdk_version: 4.29.0
 app_file: app.py
 pinned: false
+license: apache-2.0
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,87 @@
+import gradio as gr
+import torch
+from torch import nn
+from transformers import BertTokenizer, BertModel
+
+# Define the BertClassifier class
+class BertClassifier(nn.Module):
+    def __init__(self, bert: BertModel, num_classes: int):
+        super().__init__()
+        self.bert = bert
+        self.classifier = nn.Linear(bert.config.hidden_size, num_classes)
+        self.criterion = nn.BCELoss()
+
+    def forward(self, input_ids, attention_mask=None, token_type_ids=None, position_ids=None, head_mask=None, labels=None):
+        outputs = self.bert(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            token_type_ids=token_type_ids,
+            position_ids=position_ids,
+            head_mask=head_mask
+        )
+        cls_output = outputs.pooler_output
+        cls_output = self.classifier(cls_output)
+        cls_output = torch.sigmoid(cls_output)
+
+        loss = 0
+        if labels is not None:
+            loss = self.criterion(cls_output, labels)
+        return loss, cls_output
+
+# Load the tokenizer and model
+tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+bert_model = BertModel.from_pretrained('bert-base-uncased')
+model = BertClassifier(bert_model, num_classes=7)
+
+# Load the model weights from the .pkl file
+model.load_state_dict(torch.load('bert_classifier_mltc.pkl', map_location=torch.device('cpu')))
+model.eval()
+
+# Define prediction function
+def predict(text):
+    tokens = tokenizer.encode(text, add_special_tokens=True, max_length=512, truncation=True)
+    input_ids = torch.tensor([tokens])
+    mask = (input_ids != tokenizer.pad_token_id).float()
+    
+    with torch.no_grad():
+        _, outputs = model(input_ids, attention_mask=mask)
+    
+    # Assuming outputs[0] contains the probability scores for each class
+    confidence_scores = outputs[0].tolist()
+    
+    # Convert to a dictionary mapping labels to confidence scores
+    labels = ['toxic', 'severe_toxic', 'obscene', 'threat', 'insult', 'identity_hate', 'clean']
+    prediction = {label: score for label, score in zip(labels, confidence_scores)}
+    
+    return prediction
+
+# Add example texts
+examples = [
+    ["This is a positive and friendly comment."],
+    ["I'm not happy with your service."],
+    ["You are a great person!"],
+    ["How can one defame someone who thinks the Fort Hood Shooting is justified by God's law?"],
+    ["A Bisexual, like a homosexual or a heterosexual, is not defined by sexual activity. (Much like a 15 year old boy who is attracted to a girl sexually but has never had sex is still straight). A person who is actually sexually attracted/aroused by the same sex as well as the opposite sex is bisexual."],
+    ["I'LL SAY NIGGER IF I WANT TO BRO"],
+    ["Do you need a verifiable source for that statement?  Check your own user page you fat, miserable balding fuck."],
+    ["Fuck you bitch! I will go to your house and kill you with my gun as well! Prepare to die!"],
+    ["you're hot, i will rape you ;)"],
+    ["WOULDN'T BE THE FIRST TIME BITCH. FUCK YOU I'LL FIND OUT WHERE YOU LIVE, SODOMIZE YOUR WIFE AND THEN BURN YOUR HOUSE DOWN. FUCK YOU YOU FUCKING QUEER."],
+    ["What a motherfucking piece of crap those fuckheads for blocking us!"],
+    ["Get a life you animal fucker. Shut up you fucking nigger. Fuck off and shit your pants full of all the shit you can fill them with. 144.131.176.126"],
+    ["HOPE YOUR HEAD GETS CUT OFF AND SOMEONE WIPS THERE ASS WITH IT AND THEN STABS YOU IN YOUR HEART"],
+    ["you people are pretty overzealous with this whole free thing. get a fucking life, you fucking niggers !!!23 16!!!"],
+    ["Stupid peace of shit stop deleting my stuff asshole go die and fall in a hole go to hell!"],
+    ["Bye! Don't look, come or think of comming back! Tosser."]
+]
+# Create Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(lines=10, placeholder="Enter a comment here..."),
+    outputs=gr.Label(num_top_classes=7),
+    examples=examples,
+    title="Toxic Comment Classification",
+    description="Classify comments into toxic and non-toxic categories using BERT and GNN model.",
+)
+
+iface.launch()

requirements.txt

@@ -0,0 +1,7 @@
+scikit-learn>=0.24
+scikit-multilearn
+tensorflow>=2.11.0
+torch>=1.9.0
+transformers
+gradio
+huggingface_hub>=0.13.0