add app.py

app.py

@@ -0,0 +1,190 @@
+import gradio as gr
+from transformers import AutoConfig, AutoTokenizer
+from bert_graph import BertForMultipleChoice
+import ipdb
+import torch
+import copy
+from itertools import chain
+
+# "Comparison of mean diurnal measurements with latanoprost and timolol showed a statistical significant (P < 0.001) difference at 3, 6, and 12 months.",
+# "in patients with pigmentary glaucoma, 0.005% latanoprost taken once daily was well tolerated and more effective in reducing IOP than 0.5% timolol taken twice daily."
+
+def preprocess_function_exp(examples, tokenizer):
+
+        # Flatten out
+        pair_list = examples
+        # ipdb.set_trace()
+        pair_len = [len(item) for item in pair_list]
+
+        first_sentences = []
+        second_sentences = []
+        for line_list in pair_list:
+            for line in line_list:
+            # ipdb.set_trace()
+                sent_item = line.strip().split('\t')
+                first_sentences.append(sent_item[0].strip())
+                second_sentences.append(sent_item[1].strip())
+        
+        # Tokenize
+        tokenized_examples = tokenizer(
+            first_sentences,
+            second_sentences,
+            max_length=512,
+            padding=False,
+            truncation=True,
+        )
+        # Un-flatten
+        # tokenized_inputs = {k: [v[i : i + pair_len[0]] for i in range(0, len(v), pair_len[0])] for k, v in tokenized_examples.items()}
+        tokenized_inputs = {}
+        for k, v in tokenized_examples.items():
+            flatten_list = []
+            head_idx = 0
+            tail_idx = 0
+            for pair_idx in pair_len:
+                tail_idx = head_idx + pair_idx
+                flatten_list.append(v[head_idx: tail_idx])
+                head_idx = copy.copy(tail_idx)
+            tokenized_inputs[k] = flatten_list
+
+        # tokenized_inputs["pair_len"] = pair_len
+        return tokenized_inputs
+
+def DCForMultipleChoice(features, tokenizer):
+
+        batch_size = len(features)
+        argument_len = 4
+        
+        flattened_features = [
+            [{k: v[0][i] for k, v in features.items()} for i in range(4)]
+        ]
+        
+        flattened_features = list(chain(*flattened_features))
+
+
+        batch = tokenizer.pad(
+            flattened_features,
+            padding=True,
+            max_length=512,
+            return_tensors="pt",
+        )
+
+
+        batch = {k: v.view(1, argument_len, -1) for k, v in batch.items()}
+
+        return batch
+
+def post_process_diag(predictions):
+    
+    num_sentences = int(len(predictions)**0.5)
+    predictions_mtx = predictions.reshape(num_sentences, num_sentences)
+
+    for i in range(num_sentences):
+        for j in range(num_sentences):
+            if i == j:
+                predictions_mtx[i, j] = 0
+
+    return predictions_mtx.view(-1)
+
+def max_vote(logits1, logits2, pred1, pred2):
+
+    pred1 = post_process_diag(pred1)
+    pred2 = post_process_diag(pred2)
+    pred_res = []
+    confidence_res = []
+    for i in range(len(logits1)):
+
+        soft_logits1 = torch.nn.functional.softmax(logits1[i]) # [[j] for j in range(logits1.shape[1])]
+        soft_logits2 = torch.nn.functional.softmax(logits2[i])
+
+        # two class
+        # torch.topk(soft_logits1, n=2)
+        values_1, _ = soft_logits1.topk(k=2)
+        values_2, _ = soft_logits2.topk(k=2)
+        # import ipdb
+        # ipdb.set_trace()
+        # if (values_1[0] - values_2[0]) > (values_1[1] - values_2[1]):
+        #     pred_res.append(int(pred1[i].detach().cpu().numpy()))
+        # else:
+        #     pred_res.append(int(pred2[i].detach().cpu().numpy()))
+        if (values_1[0] - values_1[1]) >= (values_2[0] - values_2[1]):
+            pred_res.append(int(pred1[i].detach().cpu().numpy()))
+            confidence_res.append(float((values_1[0] - values_1[1]).detach().cpu().numpy()))
+        else:
+            pred_res.append(int(pred2[i].detach().cpu().numpy()))
+            confidence_res.append(float((values_2[0] - values_2[1]).detach().cpu().numpy()))
+
+    return pred_res, confidence_res
+
+def model_infer(input_a, input_b):
+
+    config = AutoConfig.from_pretrained('michiyasunaga/BioLinkBERT-base')
+    config.win_size = 13
+    config.model_mode = 'bert_mtl_1d'
+    config.dataset_domain = 'absRCT'
+    config.voter_branch = 'dual'
+    config.destroy = False
+
+    model = BertForMultipleChoice.from_pretrained(
+                'michiyasunaga/BioLinkBERT-base',
+                config=config,
+            )
+    p_sum = torch.load('D:/Code/Antidote/ari_model/best.pth', map_location=torch.device('cpu'))
+    model.load_state_dict(p_sum)
+    tokenizer = AutoTokenizer.from_pretrained('michiyasunaga/BioLinkBERT-base')
+
+
+    examples = [[input_a+'\t'+input_a, input_a+'\t'+input_b, input_b+'\t'+input_a, input_b+'\t'+input_b]]
+    tokenized_inputs = preprocess_function_exp(examples, tokenizer)
+    tokenized_inputs = DCForMultipleChoice(tokenized_inputs, tokenizer)
+    # ipdb.set_trace()
+    outputs = model(**tokenized_inputs)
+    predictions, scores = max_vote(outputs.logits[0], outputs.logits[1], outputs.logits[0].argmax(dim=-1), outputs.logits[1].argmax(dim=-1))
+
+    prediction_a_b = predictions[1]
+    prediction_b_a = predictions[2]
+
+    label_space = {0: 'not relates', 1: 'supports', 2: 'attack'}
+    label_a_b = label_space[prediction_a_b]
+    label_b_a = label_space[prediction_b_a]
+
+    return 'Head Argument {} Tail Argument'.format(label_a_b, label_b_a)
+    # ipdb.set_trace()
+
+
+with gr.Blocks() as demo:
+    #设置输入组件
+    arg_1 = gr.Textbox(label="Head Argument")
+    arg_2 = gr.Textbox(label="Tail Argument")
+
+    gr.Examples([\
+    "Compared with baseline measurements, both latanoprost and timolol caused a significant (P < 0.001) reduction of IOP at each hour of diurnal curve throughout the duration of therapy.",\
+        "Reduction of IOP was 6.0 +/- 4.5 and 5.9 +/- 4.6 with latanoprost and 4.8 +/- 3.0 and 4.6 +/- 3.1 with timolol after 6 and 12 months, respectively.",\
+        "Comparison of mean diurnal measurements with latanoprost and timolol showed a statistical significant (P < 0.001) difference at 3, 6, and 12 months.",\
+        "Mean C was found to be significantly enhanced (+30%) only in the latanoprost-treated group compared with the baseline (P = 0.017).",\
+        "Mean conjunctival hyperemia was graded at 0.3 in latanoprost-treated eyes and 0.2 in timolol-treated eyes.",\
+        "A remarkable change in iris color was observed in both eyes of 1 of the 18 patients treated with latanoprost and none of the 18 patients who received timolol.",\
+        "In the timolol group, heart rate was significantly reduced from 72 +/- 9 at baseline to 67 +/- 10 beats per minute at 12 months.",\
+        "in patients with pigmentary glaucoma, 0.005% latanoprost taken once daily was well tolerated and more effective in reducing IOP than 0.5% timolol taken twice daily.",\
+        "further studies may need to confirm these data on a larger sample and to evaluate the side effect of increased iris pigmentation on long-term follow-up,",\
+        ], arg_1)
+    gr.Examples([\
+    "Compared with baseline measurements, both latanoprost and timolol caused a significant (P < 0.001) reduction of IOP at each hour of diurnal curve throughout the duration of therapy.",\
+        "Reduction of IOP was 6.0 +/- 4.5 and 5.9 +/- 4.6 with latanoprost and 4.8 +/- 3.0 and 4.6 +/- 3.1 with timolol after 6 and 12 months, respectively.",\
+        "Comparison of mean diurnal measurements with latanoprost and timolol showed a statistical significant (P < 0.001) difference at 3, 6, and 12 months.",\
+        "Mean C was found to be significantly enhanced (+30%) only in the latanoprost-treated group compared with the baseline (P = 0.017).",\
+        "Mean conjunctival hyperemia was graded at 0.3 in latanoprost-treated eyes and 0.2 in timolol-treated eyes.",\
+        "A remarkable change in iris color was observed in both eyes of 1 of the 18 patients treated with latanoprost and none of the 18 patients who received timolol.",\
+        "In the timolol group, heart rate was significantly reduced from 72 +/- 9 at baseline to 67 +/- 10 beats per minute at 12 months.",\
+        "in patients with pigmentary glaucoma, 0.005% latanoprost taken once daily was well tolerated and more effective in reducing IOP than 0.5% timolol taken twice daily.",\
+        "further studies may need to confirm these data on a larger sample and to evaluate the side effect of increased iris pigmentation on long-term follow-up,",\
+        ], arg_2)
+    # 设置输出组件
+    output = gr.Textbox(label="Output Box")
+    #设置按钮
+    greet_btn = gr.Button("Run")
+    #设置按钮点击事件
+    greet_btn.click(fn=model_infer, inputs=[arg_1, arg_2], outputs=output)
+
+
+
+demo.launch()