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  1. DockerFile +16 -0
  2. README.md +11 -11
  3. app.py +16 -0
  4. model.py +20 -0
  5. requirements.txt +4 -0
  6. static/style.css +7 -0
  7. templates/index.html +25 -0
DockerFile ADDED
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+ # Read the doc: https://huggingface.co/docs/hub/spaces-sdks-docker
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+ # you will also find guides on how best to write your Dockerfile
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+
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+ FROM python:3.9
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+
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+ RUN useradd -m -u 1000 user
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+ USER user
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+ ENV PATH="/home/user/.local/bin:$PATH"
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+
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+ WORKDIR /app
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+
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+ COPY --chown=user ./requirements.txt requirements.txt
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+ RUN pip install --no-cache-dir --upgrade -r requirements.txt
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+
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+ COPY --chown=user . /app
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+ CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]
README.md CHANGED
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- ---
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- title: MoodMeter
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- emoji: 📈
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- colorFrom: purple
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- colorTo: gray
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- sdk: docker
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- pinned: false
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- short_description: check your mood/emotions
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- ---
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-
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- Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
 
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+ ---
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+ title: MoodMeter
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+ emoji: 📈
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+ colorFrom: purple
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+ colorTo: gray
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+ sdk: docker
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+ pinned: false
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+ short_description: check your mood/emotions
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+ ---
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+
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+ Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
app.py ADDED
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+ # app.py
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+ from flask import Flask, render_template, request
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+ from model import get_sentiment # Import your model function
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+
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+ app = Flask(__name__)
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+
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+ @app.route('/', methods=['GET', 'POST'])
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+ def index():
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+ if request.method == 'POST':
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+ user_input = request.form['user_input']
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+ sentiment = get_sentiment(user_input) # Get sentiment from model
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+ return render_template('index.html', input_text=user_input, result=sentiment)
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+ return render_template('index.html') # Initial page load
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+
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+ if __name__ == '__main__':
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+ app.run(debug=True)
model.py ADDED
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+ # model.py
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+ import torch
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+ from transformers import DistilBertTokenizer, DistilBertForSequenceClassification
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+
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+ tokenizer = DistilBertTokenizer.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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+ model = DistilBertForSequenceClassification.from_pretrained("distilbert-base-uncased-finetuned-sst-2-english")
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+
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+ def get_sentiment(text):
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+ inputs = tokenizer(text, return_tensors="pt")
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+ with torch.no_grad():
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+ logits = model(**inputs).logits
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+ predicted_class_id = logits.argmax().item()
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+ return model.config.id2label[predicted_class_id]
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+
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+ if __name__ == '__main__':
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+ test_text = "This movie was NOt great!"
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+ sentiment = get_sentiment(test_text)
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+ print(f"Sentiment: {sentiment}")
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+
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+
requirements.txt ADDED
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+ torch
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+ pandas
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+ flask
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+ transformers
static/style.css ADDED
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+ /* static/style.css */
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+ body {
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+ font-family: sans-serif;
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+ }
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+ h1 {
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+ color: #333;
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+ }
templates/index.html ADDED
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+ <!DOCTYPE html>
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+ <html>
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+ <head>
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+ <title>Sentiment Analysis</title>
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+ <link
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+ rel="stylesheet"
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+ type="text/css"
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+ href="{{ url_for('static', filename='style.css') }}"
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+ />
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+ </head>
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+ <body>
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+ <h1>Sentiment Analysis</h1>
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+ <form method="POST">
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+ <label for="user_input">Enter 1 text:</label><br />
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+ <textarea id="user_input" name="user_input" rows="4" cols="50">
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+ {{ input_text or '' }}</textarea
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+ ><br /><br />
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+ <input type="submit" value="Analyze" />
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+ </form>
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+ {% if result %}
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+ <h2>Result:</h2>
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+ <p>{{ result }}</p>
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+ {% endif %}
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+ </body>
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+ </html>