Update app.py

app.py

@@ -2,12 +2,40 @@ import gradio as gr
 from network import SpanNet
 from huggingface_hub import Repository
 
+git clone https://huggingface.co/spaces/nehalelkaref/plain_span
 def greet(name):
     return "Hello " + name + "!!"
+    
+def extract_spannet_scores(path,input_sentence, pos_col, task_col):
 
-# args= dict({'transformer_model_name':'nehalelkaref/plain_span',
-#             'device':'cuda'})
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-model = SpanNet(transformer_model_name= 'nehalelkaref/plain_span',device='cuda')
+    sent = input_sentence.split()
+    length = [len(sent)]
+   
+    all_scores = []
+    
+    model = SpanNet.load_model(model_path=path, device='cuda').cuda()
+    scores = []
+    model.eval()
+        
+    out_dict = model(sentences=[sent], output_span_scores=True)
+    scores.extend([[t.tolist() for t in o[:l]] for o, l in zip(out_dict['span_scores'], length)])
+    all_scores.append(scores)
+    return all_scores
 
-# iface.launch()
+def pool_span_scores(score_dicts, sent_lens):
+    TAGS = ['B', 'I', 'O']
+    pooled_scores = [[np.argmax([sum([sd[sent_id][token_id][score_id] for sd in score_dicts])
+                                    for score_id in range(len(score_dicts[0][sent_id][token_id]))])
+                                    for token_id in range(sent_lens[sent_id])]
+                        for sent_id in range(len(sent_lens))]
+                        
+    r = [[TAGS[ps] for ps in sent_ps]for sent_ps in pooled_scores]
+    return r 
+
+input_text = gr.textbox(label="Text 1")
+
+iface = gr.Interface(fn=greet, inputs=input_text, outputs="text")
+iface.
+model_path = 'models/span_model'
+scores = extract_spannet_scores(model_path,input_text, pos_col=1, task_col=2)
+iface.launch()