text-classification -v1

Makefile

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+install:
+	pip install --upgrade pip &&\
+		pip install -r requirements.txt
+
+test:
+	python -m pytest -vvv --cov=hello --cov=greeting \
+		--cov=smath --cov=web tests
+	python -m pytest --nbval notebook.ipynb	#tests our jupyter notebook
+	#python -m pytest -v tests/test_web.py #if you just want to test web
+
+debug:
+	python -m pytest -vv --pdb	#Debugger is invoked
+
+one-test:
+	python -m pytest -vv tests/test_greeting.py::test_my_name4
+
+debugthree:
+	#not working the way I expect
+	python -m pytest -vv --pdb --maxfail=4  # drop to PDB for first three failures
+
+format:
+	black *.py
+
+lint:
+	pylint --disable=R,C *.py
+
+all: install lint test format

app.py

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+from transformers import AutoModelForSequenceClassification
+from transformers import AutoTokenizer, AutoConfig
+import numpy as np
+from scipy.special import softmax
+import gradio as gr
+
+# Preprocess text (username and link placeholders)
+def preprocess(text):
+    new_text = []
+    for t in text.split(" "):
+        t = '@user' if t.startswith('@') and len(t) > 1 else t
+        t = 'http' if t.startswith('http') else t
+        new_text.append(t)
+    return " ".join(new_text)
+
+# load model
+MODEL = f"cardiffnlp/twitter-roberta-base-sentiment-latest"
+model = AutoModelForSequenceClassification.from_pretrained(MODEL)
+#model.save_pretrained(MODEL)
+
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL)
+config = AutoConfig.from_pretrained(MODEL)
+
+# create classifier function
+def classify_sentiments(text):
+  text = preprocess(text)
+  encoded_input = tokenizer(text, return_tensors='pt')
+  output = model(**encoded_input)
+  scores = output[0][0].detach().numpy()
+  scores = softmax(scores)
+
+  # Print labels and scores
+  probs = {}
+  ranking = np.argsort(scores)
+  ranking = ranking[::-1]
+
+  for i in range(len(scores)):
+    l = config.id2label[ranking[i]]
+    s = scores[ranking[i]]
+    probs[l] = np.round(float(s), 4)
+  return probs
+
+
+#build the Gradio app
+gr.Interface(classify_sentiments, 'text', 'label').launch()
+

requirements.txt

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+scipy
+gradio
+numpy