Create app

app.py

@@ -1,3 +1,58 @@
 import gradio as gr
+from transformers import BartForSequenceClassification, BartTokenizer
+import torch.nn.functional as F
+import torch
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+from transformers import Pipeline
 
-gr.load("models/facebook/bart-large-mnli").launch()
+te_tokenizer = BartTokenizer.from_pretrained('facebook/bart-large-mnli')
+te_model = BartForSequenceClassification.from_pretrained('facebook/bart-large-mnli')
+qa_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-base")
+qa_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-base", device_map="auto")
+
+def predict(context, intent):
+    input_text = "In one word, what is the opposite of: " + intent + "?"
+    input_ids = qa_tokenizer(input_text, return_tensors="pt")
+    encoded_input = qa_tokenizer(input_ids, return_tensors="pt")
+    opposite_output = qa_tokenizer.decode(qa_model.generate(encoded_input)[0])
+    input_text = "In one word, what is the following describing: " + context
+    input_ids = qa_tokenizer(input_text, return_tensors="pt")
+    encoded_input = qa_tokenizer(input_ids, return_tensors="pt")
+    object_output = qa_tokenizer.decode(qa_model.generate(encoded_input)[0])
+    batch = ['I think the ' + object_output + ' are long.', 'I think the ' + object_output + ' are ' + opposite_output, 'I think the ' + object_output + ' are the perfect']
+    outputs = []
+    for i, hypothesis in enumerate(batch):
+        input_ids = te_tokenizer.encode(context, hypothesis, return_tensors='pt')
+        # -> [contradiction, neutral, entailment]
+        logits = te_model(input_ids)[0][0]
+
+        if (i == 2):
+            # -> [contradiction, entailment]
+            probs = logits[[0,2]].softmax(dim=0)
+        else:
+            probs = logits.softmax(dim=0)
+        outputs.append(probs)
+        
+    # -> [entailment, contradiction]
+    outputs[2] = outputs[2].flip(dims=[0])
+    # -> [entailment, neutral, contradiction]
+    outputs[0] = outputs[0].flip(dims=[0])
+    pn_tensor = (outputs[0] + outputs[1]).softmax(dim=0)
+    pn_tensor[1] = pn_tensor[1] * outputs[2][0]
+    pn_tensor[2] = pn_tensor[2] * outputs[2][1]
+    pn_tensor[0] = pn_tensor[0] * outputs[2][1]
+
+    pn_tensor = F.normalize(pn_tensor, p=1, dim=0)
+
+    pn_tensor = pn_tensor.softmax(dim=0)
+    return {"entailment": pn_tensor[0].item(), "neutral": pn_tensor[1].item(), "contradiction": pn_tensor[2].item()}
+
+gradio_app = gr.Interface(
+    predict,
+    inputs=gr.Text(label="Input sentence"),
+    outputs=[gr.Label(num_top_classes=3)],
+    title="Hot Dog? Or Not?",
+)
+
+if __name__ == "__main__":
+    gradio_app.launch()