Update app.py

app.py

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