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peace4ever
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•
8a0fb54
1
Parent(s):
a0ce698
Update app.py
Browse files
app.py
CHANGED
@@ -1,9 +1,5 @@
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from transformers import pipeline
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import os
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# Initialize Flask app
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app = Flask(__name__)
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# Load pre-trained sentiment analysis pipeline
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model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"
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@@ -22,28 +18,23 @@ def analyze_sentiment(text):
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"entailment": "Negative", # Map based on your fine-tuned model's labels
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"contradiction": "Neutral",
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"neutral": "Positive",
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# Add more mappings if needed
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}
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sentiment = sentiment_map.get(label.lower(), "Unknown")
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return
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@app.route('/analyze', methods=['POST'])
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def analyze():
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"""
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This endpoint receives text data and returns the sentiment analysis result.
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"""
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data = request.json
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if 'text' not in data:
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return jsonify({"error": "No text provided"}), 400
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text = data['text']
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result = analyze_sentiment(text)
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return jsonify(result)
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@app.route('/')
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def home():
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return "Welcome to the Sentiment Analysis API!"
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if __name__ == "__main__":
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port = int(os.environ.get("PORT", 7860))
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app.run(host="0.0.0.0", port=port)
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import streamlit as st
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from transformers import pipeline
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# Load pre-trained sentiment analysis pipeline
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model_name = "peace4ever/roberta-large-finetuned-mongolian_v4"
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"entailment": "Negative", # Map based on your fine-tuned model's labels
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"contradiction": "Neutral",
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"neutral": "Positive",
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}
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sentiment = sentiment_map.get(label.lower(), "Unknown")
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return sentiment, label, probability
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# Streamlit app layout
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st.title("Mongolian Sentiment Analysis")
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st.write("Enter some text to analyze its sentiment.")
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user_input = st.text_area("Text input")
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if st.button("Analyze"):
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if user_input:
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sentiment, label, probability = analyze_sentiment(user_input)
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st.write(f"**Sentiment:** {sentiment}")
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st.write(f"**Label:** {label}")
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st.write(f"**Probability:** {probability:.2f}")
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else:
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st.write("Please enter some text to analyze.")
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