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app.py

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+from pyabsa import available_checkpoints
+import warnings
+warnings.simplefilter("ignore")
+checkpoint_map = available_checkpoints('atepc', show_ckpts=False)
+from pyabsa import ATEPCCheckpointManager
+aspect_extractor = ATEPCCheckpointManager.get_aspect_extractor(checkpoint='english',
+                                   auto_device=True  # False means load model on CPU
+                                   )
+
+import gradio as gr
+import pandas as pd
+def inference(text):
+    result = aspect_extractor.extract_aspect(inference_source=[text],
+                                             pred_sentiment=True)
+
+    result = pd.DataFrame({
+        'aspect': result[0]['aspect'],
+        'sentiment': result[0]['sentiment']
+    })
+
+    return result
+
+if __name__ == '__main__':
+    iface = gr.Interface(
+        fn=inference,
+        inputs=["text"],
+        examples=[['The wine list is incredible and extensive and diverse , the food is all incredible and the staff was all very nice ,'
+                   ' good at their jobs and cultured .'],
+                  ['Though the menu includes some unorthodox offerings (a peanut butter roll, for instance), the classics are pure and '
+                   'great--we have never had better sushi anywhere, including Japan.'],
+                  ['Everything, from the soft bread, soggy salad, and 50 minute wait time, with an incredibly rude service to deliver'
+                   ' below average food .'],
+                  ['Even though it is running Snow Leopard, 2.4 GHz C2D is a bit of an antiquated CPU and thus the occasional spinning '
+                   'wheel would appear when running Office Mac applications such as Word or Excel .'],
+                  ['Good Work user'],
+                  ['camera is good, battery drains fast'],
+                  ],
+        outputs="dataframe",
+        description="Project by Devansh Mistry with PyABSA Library",
+        title='ASPECT BASED SEMANTICS ANALYTICS'
+    )
+
+    iface.launch()