Update app.py

app.py

@@ -1,87 +1,40 @@
 import gradio as gr
 import torch
-from torch import nn
-from transformers import BertTokenizer, BertModel
+from transformers import RagTokenizer, RagRetriever, RagSequenceForGeneration
 
-# 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()
+# Load the tokenizer, retriever, and model
+tokenizer = RagTokenizer.from_pretrained("facebook/rag-sequence-nq")
+retriever = RagRetriever.from_pretrained("facebook/rag-sequence-nq", use_dummy_dataset=True)
+model = RagSequenceForGeneration.from_pretrained("facebook/rag-sequence-nq", retriever=retriever)
 
 # 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()
+def predict(input_text):
+    # Tokenize input
+    input_ids = tokenizer([input_text], return_tensors="pt").input_ids
     
-    with torch.no_grad():
-        _, outputs = model(input_ids, attention_mask=mask)
+    # Generate response
+    outputs = model.generate(input_ids)
+    response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
     
-    # 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
+    return response
 
 # 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."]
+    ["Patient admitted with a history of heart failure and requires detailed follow-up on cardiovascular treatment."],
+    ["What are the complications of diabetes mellitus that need to be monitored in this patient?"],
+    ["Describe the appropriate treatment for acute respiratory distress syndrome in a critical care setting."],
+    ["Explain the signs and symptoms that indicate a neurological emergency in a stroke patient."],
+    ["What are the best practices for managing an infectious disease outbreak in a hospital setting?"]
 ]
+
 # 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),
+    inputs=gr.Textbox(lines=10, placeholder="Enter your medical question or clinical notes here..."),
+    outputs="text",
     examples=examples,
-    title="Toxic Comment Classification",
-    description="Classify comments into toxic and non-toxic categories using BERT and GNN model.",
+    title="MIMIC-IV RAG Implementation",
+    description="Use RAG (Retrieval-Augmented Generation) to generate responses or provide additional information based on clinical notes and medical questions. This model helps in generating relevant information based on existing medical literature.",
 )
 
 iface.launch()