Initial app setup

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+
+# # L1: NLP tasks with a simple interface 🗞️
+
+# Load your HF API key and relevant Python libraries.
+
+# In[1]:
+
+
+import os
+import io
+from IPython.display import Image, display, HTML
+from PIL import Image
+import base64 
+
+# removed dotenv and hf key requirements to see how HF Spaces handles it
+
+# In[2]:
+
+
+# Helper function
+import requests, json
+
+#Summarization endpoint
+from transformers import pipeline
+
+get_completion = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
+
+def summarize(input):
+    output = get_completion(input)
+    return output[0]['summary_text']
+
+
+
+# ## Building a Named Entity Recognition app
+
+# We are using this [Inference Endpoint](https://huggingface.co/inference-endpoints) for `dslim/bert-base-NER`, a 108M parameter fine-tuned BART model on the NER task.
+
+# ### How about running it locally?
+# 
+# ```py
+# from transformers import pipeline
+# 
+# get_completion = pipeline("ner", model="dslim/bert-base-NER")
+# 
+# def ner(input):
+#     output = get_completion(input)
+#     return {"text": input, "entities": output}
+#     
+# ```
+
+# In[8]:
+
+from transformers import pipeline
+get_completion = pipeline("ner", model="dslim/bert-base-NER")
+
+
+# In[12]:
+
+
+def ner(input):
+    output = get_completion(input)
+    return {"text": input, "entities": output}
+
+gr.close_all()
+demo = gr.Interface(fn=ner,
+                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+                    outputs=[gr.HighlightedText(label="Text with entities")],
+                    title="NER with dslim/bert-base-NER",
+                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+                    allow_flagging="never",
+                    #Here we introduce a new tag, examples, easy to use examples for your application
+                    examples=["My name is Andrew and I live in California", "My name is Poli and work at HuggingFace"])
+demo.launch()
+
+
+# ### Adding a helper function to merge tokens
+
+# In[17]:
+
+
+def merge_tokens(tokens):
+    merged_tokens = []
+    for token in tokens:
+        if merged_tokens and token['entity'].startswith('I-') and merged_tokens[-1]['entity'].endswith(token['entity'][2:]):
+            # If current token continues the entity of the last one, merge them
+            last_token = merged_tokens[-1]
+            last_token['word'] += token['word'].replace('##', '')
+            last_token['end'] = token['end']
+            last_token['score'] = (last_token['score'] + token['score']) / 2
+        else:
+            # Otherwise, add the token to the list
+            merged_tokens.append(token)
+
+    return merged_tokens
+
+def ner(input):
+    output = get_completion(input)
+    merged_tokens = merge_tokens(output)
+    return {"text": input, "entities": merged_tokens}
+
+gr.close_all()
+demo = gr.Interface(fn=ner,
+                    inputs=[gr.Textbox(label="Text to find entities", lines=2)],
+                    outputs=[gr.HighlightedText(label="Text with entities")],
+                    title="NER with dslim/bert-base-NER",
+                    description="Find entities using the `dslim/bert-base-NER` model under the hood!",
+                    allow_flagging="never",
+                    examples=["My name is Andrew, I'm building DeeplearningAI and I live in California", "My name is Poli, I live in Vienna and work at HuggingFace"])
+
+demo.launch()
+
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+# In[15]:
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+gr.close_all()
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requirements.txt

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+transformers
+IPython
+torch