Update app.py

app.py

@@ -1,5 +1,5 @@
 # app.py - very simple Flask emotion detector
-# English (joblib) + Sinhala & Tamil (transformers)
+# English (joblib) + Sinhala (joblib) + Tamil (transformers)
 
 from flask import Flask, render_template, request
 import joblib
@@ -8,64 +8,96 @@ from transformers import pipeline
 
 app = Flask(__name__)
 
+# ────────────────────────────────────────────────
+# English model (already joblib)
+# ────────────────────────────────────────────────
 print("Loading English model...")
-vectorizer = joblib.load(hf_hub_download(
+en_vectorizer = joblib.load(hf_hub_download(
     "E-motionAssistant/Englsih_Trained_Model_LR",
     "tfidf_vectorizer.joblib"
 ))
-classifier = joblib.load(hf_hub_download(
+en_classifier = joblib.load(hf_hub_download(
     "E-motionAssistant/Englsih_Trained_Model_LR",
     "logreg_model.joblib"
 ))
-label_encoder = joblib.load(hf_hub_download(
+en_label_encoder = joblib.load(hf_hub_download(
     "E-motionAssistant/Englsih_Trained_Model_LR",
     "label_encoder.joblib"
 ))
 
-print("Loading Sinhala model...")
-sinhala_pipe = pipeline("text-classification",
-                        model="E-motionAssistant/Sinhala_Text_Emotion_Model_Retrained")
+# ────────────────────────────────────────────────
+# Sinhala model – now using joblib (LR + TF-IDF)
+# Replace REPO_SINHALA with your actual Sinhala LR model repository
+# ────────────────────────────────────────────────
+print("Loading Sinhala LR model...")
+REPO_SINHALA = "E-motionAssistant/Sinhala_Text_Emotion_Model_LR"   # ← CHANGE THIS to your real repo name
 
+si_vectorizer = joblib.load(hf_hub_download(
+    "E-motionAssistant/Sinhala_Text_Emotion_Model_LR",
+    "tfidf_vectorizer.joblib"
+))
+si_classifier = joblib.load(hf_hub_download(
+    "E-motionAssistant/Sinhala_Text_Emotion_Model_LR",
+    "logreg_model.joblib"
+))
+si_label_encoder = joblib.load(hf_hub_download(
+    "E-motionAssistant/Sinhala_Text_Emotion_Model_LR",
+    "label_encoder.joblib"
+))
+
+# ────────────────────────────────────────────────
+# Tamil model (still transformers)
+# ────────────────────────────────────────────────
 print("Loading Tamil model...")
-tamil_pipe = pipeline("text-classification",
-                      model="E-motionAssistant/Tamil_Emotion_Recognition_Model")
+tamil_pipe = pipeline(
+    "text-classification",
+    model="E-motionAssistant/Tamil_Emotion_Recognition_Model"
+)
 
 print("All models loaded successfully!")
 
+# ────────────────────────────────────────────────
+# Routes
+# ────────────────────────────────────────────────
 @app.route("/", methods=["GET", "POST"])
 def home():
     english_result = ""
     sinhala_result = ""
-    tamil_result   = ""
+    tamil_result = ""
 
     if request.method == "POST":
         action = request.form.get("action")
 
+        # English – joblib
         if action == "predict_english":
             text = request.form.get("english_text", "").strip()
             if text:
-                X = vectorizer.transform([text])
-                pred = classifier.predict(X)[0]
-                emotion = label_encoder.inverse_transform([pred])[0]
+                X = en_vectorizer.transform([text])
+                pred = en_classifier.predict(X)[0]
+                emotion = en_label_encoder.inverse_transform([pred])[0]
                 english_result = f"Emotion: {emotion}"
             else:
                 english_result = "Please write something"
 
+        # Sinhala – now joblib (same pattern as English)
         elif action == "predict_sinhala":
             text = request.form.get("sinhala_text", "").strip()
             if text:
-                res = sinhala_pipe(text)[0]
-                sinhala_result = f"Emotion: {res['label']})"
+                X = si_vectorizer.transform([text])
+                pred = si_classifier.predict(X)[0]
+                emotion = si_label_encoder.inverse_transform([pred])[0]
+                sinhala_result = f"හැඟීම: {emotion}"
             else:
-                sinhala_result = "Please write something"
+                sinhala_result = "කරුණාකර යමක් ලියන්න"
 
+        # Tamil – still transformers / pipeline
         elif action == "predict_tamil":
             text = request.form.get("tamil_text", "").strip()
             if text:
                 res = tamil_pipe(text)[0]
-                tamil_result = f"Emotion: {res['label']})"
+                tamil_result = f"உணர்வு: {res['label']} ({res['score']:.3f})"
             else:
-                tamil_result = "Please write something"
+                tamil_result = "கருணையுடன் உரையை உள்ளிடவும்"
 
     return render_template(
         "index.html",
@@ -74,5 +106,6 @@ def home():
         tamil_result=tamil_result
     )
 
+
 if __name__ == "__main__":
     app.run(host="0.0.0.0", port=7860, debug=False)