Update interfaces/relation_e.py

interfaces/relation_e.py

@@ -1,99 +1,99 @@
-from utca.core import RenameAttribute
-from utca.implementation.predictors import TokenSearcherPredictor, TokenSearcherPredictorConfig
-from utca.implementation.tasks import TokenSearcherNER, TokenSearcherNERPostprocessor, TokenSearcherRelationExtraction, TokenSearcherRelationExtractionPostprocessor
-from typing import Dict, Union
-import gradio as gr
-
-text = """
-Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal \"Nature Neuroscience\".
- His research focuses on a rare genetic mutation, found in less than 0.01% of the population, that appears to prevent the development of Alzheimer's disease.
- Collaborating with researchers at the University of California, San Francisco, the team is now working to understand the mechanism by which this mutation confers its protective effect.
- Funded by the National Institutes of Health, their research could potentially open new avenues for Alzheimer's treatment.
-"""
-predictor = TokenSearcherPredictor(
-    TokenSearcherPredictorConfig(
-        device="cuda:0",
-        model="knowledgator/UTC-DeBERTa-large-v2"
-    )
-)
-
-pipe = (
-    TokenSearcherNER( # TokenSearcherNER task produces classified entities that will be at the "output" key.
-        predictor=predictor,
-        postprocess=TokenSearcherNERPostprocessor(
-            threshold=0.5 # Entity threshold
-        )
-    ) 
-    | RenameAttribute("output", "entities") # Rename output entities from TokenSearcherNER task to use them as inputs in TokenSearcherRelationExtraction
-    | TokenSearcherRelationExtraction( # TokenSearcherRelationExtraction is used for relation extraction.
-        predictor=predictor,
-        postprocess=TokenSearcherRelationExtractionPostprocessor(
-            threshold=0.5 # Relation threshold
-        )
-    )
-)
-
-
-def process(
-    relation: str, text, distance_threshold: str, pairs_filter: str, labels: str
-) -> Dict[str, Union[str, int, float]]:
-    pairs_filter = [tuple(pair.strip() for pair in pair.split("->")) for pair in pairs_filter.split(",")]
-
-    if len(distance_threshold) < 1 or not distance_threshold or not distance_threshold.strip().isdigit():
-        r = pipe.run({
-            "text": text, 
-            "labels": [label.strip() for label in labels.split(",")],
-            "relations": [{
-                "relation": relation,
-                "pairs_filter": pairs_filter
-                }]
-                })
-    elif int(distance_threshold.strip()):
-        r = pipe.run({
-            "text": text, 
-            "labels": [label.strip() for label in labels.split(",")],
-            "relations": [{
-                "relation": relation,
-                "pairs_filter": pairs_filter,
-                "distance_threshold": int(distance_threshold.replace(" ", ""))
-                }]
-                })
-
-    return r["output"]
-
-relation_e_examples = [
-    [
-    "worked at",
-    text,
-    "None",
-    "scientist -> university, scientist -> other",
-    "scientist, university, city, research, journal"]
-    ]
-
-with gr.Blocks(title="Open Information Extracting") as relation_e_interface:
-    relation = gr.Textbox(label="Relation", placeholder="Enter relation you want to extract here")
-    input_text = gr.Textbox(label="Text input", placeholder="Enter your text here")
-    labels = gr.Textbox(label="Labels", placeholder="Enter your labels here (comma separated)", scale=2)
-    pairs_filter = gr.Textbox(label="Pairs Filter", placeholder="It specifies possible members of relations by their entity labels. Write as: source -> target,..")
-    distance_threshold =  gr.Textbox(label="Distance Threshold", placeholder="It specifies the max distance in characters between spans in the text")
-    output = gr.Textbox(label="Predicted Relation")
-    submit_btn = gr.Button("Submit")
-    examples = gr.Examples(
-    relation_e_examples,
-    fn=process,
-    inputs=[relation, input_text, distance_threshold, pairs_filter, labels],
-    outputs=output,
-    cache_examples=True
-    )
-    theme=gr.themes.Base()
-
-    input_text.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
-    labels.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
-    pairs_filter.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
-    submit_btn.click(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
-    distance_threshold.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
-
-
-if __name__ == "__main__":
-    
+from utca.core import RenameAttribute
+from utca.implementation.predictors import TokenSearcherPredictor, TokenSearcherPredictorConfig
+from utca.implementation.tasks import TokenSearcherNER, TokenSearcherNERPostprocessor, TokenSearcherRelationExtraction, TokenSearcherRelationExtractionPostprocessor
+from typing import Dict, Union
+import gradio as gr
+
+text = """
+Dr. Paul Hammond, a renowned neurologist at Johns Hopkins University, has recently published a paper in the prestigious journal \"Nature Neuroscience\".
+ His research focuses on a rare genetic mutation, found in less than 0.01% of the population, that appears to prevent the development of Alzheimer's disease.
+ Collaborating with researchers at the University of California, San Francisco, the team is now working to understand the mechanism by which this mutation confers its protective effect.
+ Funded by the National Institutes of Health, their research could potentially open new avenues for Alzheimer's treatment.
+"""
+predictor = TokenSearcherPredictor(
+    TokenSearcherPredictorConfig(
+        device="cpu",
+        model="knowledgator/UTC-DeBERTa-large-v2"
+    )
+)
+
+pipe = (
+    TokenSearcherNER( # TokenSearcherNER task produces classified entities that will be at the "output" key.
+        predictor=predictor,
+        postprocess=TokenSearcherNERPostprocessor(
+            threshold=0.5 # Entity threshold
+        )
+    ) 
+    | RenameAttribute("output", "entities") # Rename output entities from TokenSearcherNER task to use them as inputs in TokenSearcherRelationExtraction
+    | TokenSearcherRelationExtraction( # TokenSearcherRelationExtraction is used for relation extraction.
+        predictor=predictor,
+        postprocess=TokenSearcherRelationExtractionPostprocessor(
+            threshold=0.5 # Relation threshold
+        )
+    )
+)
+
+
+def process(
+    relation: str, text, distance_threshold: str, pairs_filter: str, labels: str
+) -> Dict[str, Union[str, int, float]]:
+    pairs_filter = [tuple(pair.strip() for pair in pair.split("->")) for pair in pairs_filter.split(",")]
+
+    if len(distance_threshold) < 1 or not distance_threshold or not distance_threshold.strip().isdigit():
+        r = pipe.run({
+            "text": text, 
+            "labels": [label.strip() for label in labels.split(",")],
+            "relations": [{
+                "relation": relation,
+                "pairs_filter": pairs_filter
+                }]
+                })
+    elif int(distance_threshold.strip()):
+        r = pipe.run({
+            "text": text, 
+            "labels": [label.strip() for label in labels.split(",")],
+            "relations": [{
+                "relation": relation,
+                "pairs_filter": pairs_filter,
+                "distance_threshold": int(distance_threshold.replace(" ", ""))
+                }]
+                })
+
+    return r["output"]
+
+relation_e_examples = [
+    [
+    "worked at",
+    text,
+    "None",
+    "scientist -> university, scientist -> other",
+    "scientist, university, city, research, journal"]
+    ]
+
+with gr.Blocks(title="Open Information Extracting") as relation_e_interface:
+    relation = gr.Textbox(label="Relation", placeholder="Enter relation you want to extract here")
+    input_text = gr.Textbox(label="Text input", placeholder="Enter your text here")
+    labels = gr.Textbox(label="Labels", placeholder="Enter your labels here (comma separated)", scale=2)
+    pairs_filter = gr.Textbox(label="Pairs Filter", placeholder="It specifies possible members of relations by their entity labels. Write as: source -> target,..")
+    distance_threshold =  gr.Textbox(label="Distance Threshold", placeholder="It specifies the max distance in characters between spans in the text")
+    output = gr.Textbox(label="Predicted Relation")
+    submit_btn = gr.Button("Submit")
+    examples = gr.Examples(
+    relation_e_examples,
+    fn=process,
+    inputs=[relation, input_text, distance_threshold, pairs_filter, labels],
+    outputs=output,
+    cache_examples=True
+    )
+    theme=gr.themes.Base()
+
+    input_text.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
+    labels.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
+    pairs_filter.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
+    submit_btn.click(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
+    distance_threshold.submit(fn=process, inputs=[relation, input_text, distance_threshold, pairs_filter, labels], outputs=output)
+
+
+if __name__ == "__main__":
+    
     relation_e_interface.launch()