Create app.py

app.py

@@ -0,0 +1,121 @@
+import os
+import torch
+import time
+from torch.utils.data import DataLoader, RandomSampler, SequentialSampler
+from transformers import (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer, squad_convert_examples_to_features)
+from transformers.data.processors.squad import SquadResult, SquadV2Processor, SquadExample
+from transformers.data.metrics.squad_metrics import compute_predictions_logits
+
+model_name_or_path = "ktrapeznikov/albert-xlarge-v2-squad-v2"
+
+output_dir = ""
+
+# Config
+n_best_size = 1
+max_answer_length = 30
+do_lower_case = True
+null_score_diff_threshold = 0.0
+
+def to_list(tensor):
+    return tensor.detach().cpu().tolist()
+
+# Setup model
+config_class, model_class, tokenizer_class = (AlbertConfig, AlbertForQuestionAnswering, AlbertTokenizer)
+config = config_class.from_pretrained(model_name_or_path)
+tokenizer = tokenizer_class.from_pretrained(model_name_or_path, do_lower_case=True)
+model = model_class.from_pretrained(model_name_or_path, config=config)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+processor = SquadV2Processor()
+
+def run_prediction(question, context_text):
+    """Setup function to compute predictions"""
+    examples = []
+    question_texts = [question]
+    for i, question_text in enumerate(question_texts):
+        example = SquadExample(
+            qas_id=str(i),
+            question_text=question_text,
+            context_text=context_text,
+            answer_text=None,
+            start_position_character=None,
+            title="Predict",
+            is_impossible=False,
+            answers=None,
+        )
+
+        examples.append(example)
+
+    features, dataset = squad_convert_examples_to_features(
+        examples=examples,
+        tokenizer=tokenizer,
+        max_seq_length=384,
+        doc_stride=128,
+        max_query_length=64,
+        is_training=False,
+        return_dataset="pt",
+        threads=1,
+    )
+
+    eval_sampler = SequentialSampler(dataset)
+    eval_dataloader = DataLoader(dataset, sampler=eval_sampler, batch_size=10)
+
+    all_results = []
+
+    for batch in eval_dataloader:
+        model.eval()
+        batch = tuple(t.to(device) for t in batch)
+
+        with torch.no_grad():
+            inputs = {
+                "input_ids": batch[0],
+                "attention_mask": batch[1],
+                "token_type_ids": batch[2],
+            }
+
+            example_indices = batch[3]
+
+            outputs = model(**inputs)
+
+            for i, example_index in enumerate(example_indices):
+                eval_feature = features[example_index.item()]
+                unique_id = int(eval_feature.unique_id)
+
+                output = [to_list(output[i]) for output in outputs]
+
+                start_logits, end_logits = output
+                result = SquadResult(unique_id, start_logits, end_logits)
+                all_results.append(result)
+
+    predictions = compute_predictions_logits(
+        examples,
+        features,
+        all_results,
+        n_best_size,
+        max_answer_length,
+        do_lower_case,
+        False,
+        False,
+        False,
+        False,  # verbose_logging
+        True,  # version_2_with_negative
+        null_score_diff_threshold,
+        tokenizer,
+    )
+    answer = "empty"
+    for key in predictions.keys():
+      answer=predictions[key]
+      break
+    return answer 
+    
+    
+context = "4/5/2022 · In connection with the closing, Helix Acquisition Corp changed its name to MoonLake Immunotherapeutics (“MoonLake” or the “Company”). Beginning April 6, 2022, MoonLake’s shares will trade on the Nasdaq Stock Market..."
+questions = ["Helix Acquisition Corp change its name to"]
+title = 'Question Answering demo with Albert QA transformer and gradio'
+
+# Run method
+
+  
+  
+gr.Interface(run_prediction,inputs=[gr.inputs.Textbox(lines=7, default=context, label="Context"), gr.inputs.Textbox(lines=2, default=question, label="Question")],
+             outputs=[gr.outputs.Textbox(type="auto",label="Answer")],title = title,theme = "peach").launch()