Update app.py

app.py

@@ -1,7 +1,70 @@
-import gradio as gr
+import torch
+import numpy as np
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-def greet(name):
-    return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+def getScores(ids, scores, pad_token_id):
+    """get sequence scores from model.generate output"""
+    scores = torch.stack(scores, dim=1)
+    log_probs = torch.log_softmax(scores, dim=2)
+    # remove start token
+    ids = ids[:,1:]
+    # gather needed probs
+    x = ids.unsqueeze(-1).expand(log_probs.shape)
+    needed_logits = torch.gather(log_probs, 2, x)
+    final_logits = needed_logits[:, :, 0]
+    padded_mask = (ids == pad_token_id)
+    final_logits[padded_mask] = 0
+    final_scores = final_logits.sum(dim=-1)
+    return final_scores.cpu().detach().numpy()
+
+def topkSample(input, model, tokenizer, 
+                num_samples=5,
+                num_beams=1,
+                max_output_length=30):
+    tokenized = tokenizer(input, return_tensors="pt")
+    out = model.generate(**tokenized,
+                        do_sample=True,
+                        num_return_sequences = num_samples,
+                        num_beams = num_beams,
+                        eos_token_id = tokenizer.eos_token_id,
+                        pad_token_id = tokenizer.pad_token_id,
+                        output_scores = True,
+                        return_dict_in_generate=True,
+                        max_length=max_output_length,)
+    out_tokens = out.sequences
+    out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True)
+    out_scores = getScores(out_tokens, out.scores, tokenizer.pad_token_id)
+    
+    pair_list = [(x[0], x[1]) for x in zip(out_str, out_scores)]
+    sorted_pair_list = sorted(pair_list, key=lambda x:x[1], reverse=True)
+    return sorted_pair_list
+
+def greedyPredict(input, model, tokenizer):
+    input_ids = tokenizer([input], return_tensors="pt").input_ids
+    out_tokens = model.generate(input_ids)
+    out_str = tokenizer.batch_decode(out_tokens, skip_special_tokens=True)
+    return out_str[0]
+    
+def predict_tail(entity, relation):
+    global model, tokenizer
+    input = entity + "| " + relation
+    out = topkSample(input, model, tokenizer, num_samples=5)
+    out_dict = {}
+    for k, v in out:
+        out_dict[k] = np.exp(v).item()
+    return out_dict
+
+    
+tokenizer = AutoTokenizer.from_pretrained("apoorvumang/kgt5-wikikg90mv2")
+model = AutoModelForSeq2SeqLM.from_pretrained("apoorvumang/kgt5-base-wikikg90mv2")
+
+
+
+ent_input = gradio.inputs.Textbox(lines=1, default="World War II")
+rel_input = gradio.inputs.Textbox(lines=1, default="followed by")
+output = gradio.outputs.Label()
+
+
+iface = gr.Interface(fn=predict_tail, inputs=[ent_input, rel_input], outputs=output)
+iface.launch()