Duplicate from ThankGod/text-classification

Co-authored-by: Egbe <[email protected]>

.github/workflows/main.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://ThankGod:[email protected]/spaces/ThankGod/text-classification main

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

README.md

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+---
+title: Text Classification/ Sentiment analysis
+emoji: 📸
+colorFrom: yellow
+colorTo: pink
+sdk: gradio
+sdk_version: 3.1.7
+app_file: app.py
+pinned: false
+duplicated_from: ThankGod/text-classification
+---
+
+[Try Demo Text classification Here](https://huggingface.co/spaces/ThankGod/text-classification)
+
+## Credits
+- Hugging face 🤗 for hosting this demo.
+- Hugging face transformer model for text classification transformer model
+- Gradio for the beautiful visualization dashboards.
+
+## References
+- https://gradio.app/
+- https://huggingface.co/

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
+#Instructuction = "Write an imaginary review about a product or service you might be interested in."
+title="Text Sentiment Analysis"
+description = """Write a Good or Bad review about an imaginary product or service,\
+   see how the machine learning model is able to predict your sentiments"""
+article = """
+            - Click submit button to test sentiment analysis prediction
+            - Click clear button to refresh text
+           """
+
+gr.Interface(classify_sentiments,
+            'text',
+            'label',
+            title = title,
+            description = description,
+            #Instruction = Instructuction,
+            article = article,
+            allow_flagging = "never",
+            live = False,
+            examples=["This has to be the best Introductory course in machine learning",
+            "I consider this training an absolute waste of time."]
+             ).launch()
+

file

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+more lines

requirements.txt

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+scipy
+gradio
+numpy
+transformers
+torch 

text-classification

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+Subproject commit e187cc1fff0bfec15df56f125f944607154499a9