Update app.py

app.py

@@ -1,141 +1,78 @@
-import gradio as gr
+# app.py - very simple Flask emotion detector
+# English (joblib) + Sinhala & Tamil (transformers)
+
+from flask import Flask, render_template, request
 import joblib
-import logging
 from huggingface_hub import hf_hub_download
-from transformers import pipeline, logging as hf_logging
-
-# ────────────────────────────────────────────────
-# 1. Logging Setup (Spaces compatible)
-# ────────────────────────────────────────────────
-logging.basicConfig(level=logging.INFO)
-hf_logging.set_verbosity_error()  # reduce transformer noise
-
-logging.info("🚀 Starting application...")
-
-# ────────────────────────────────────────────────
-# 2. Load Models (with caching)
-# ────────────────────────────────────────────────
-@gr.cache()
-def load_models():
-    try:
-        logging.info("Loading English model...")
-        en_vectorizer = joblib.load(
-            hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "tfidf_vectorizer.joblib")
-        )
-        en_classifier = joblib.load(
-            hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "logreg_model.joblib")
-        )
-        en_label_encoder = joblib.load(
-            hf_hub_download("E-motionAssistant/Englsih_Trained_Model_LR", "label_encoder.joblib")
-        )
-
-        logging.info("Loading Sinhala model...")
-        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")
-        )
-
-        logging.info("Loading Tamil transformer model...")
-        tamil_pipe = pipeline(
-            "text-classification",
-            model="E-motionAssistant/Tamil_Emotion_Recognition_Model"
-        )
-
-        logging.info("✅ Models loaded successfully!")
-        return en_vectorizer, en_classifier, en_label_encoder, \
-               si_vectorizer, si_classifier, si_label_encoder, \
-               tamil_pipe
-
-    except Exception as e:
-        logging.error(f"❌ Model loading failed: {e}")
-        raise e
-
-
-# Load once
-(en_vectorizer, en_classifier, en_label_encoder,
- si_vectorizer, si_classifier, si_label_encoder,
- tamil_pipe) = load_models()
-
-
-# ────────────────────────────────────────────────
-# 3. Prediction Function
-# ────────────────────────────────────────────────
-def predict_emotion(text, language):
-    if not text.strip():
-        return "⚠️ Please enter some text."
-
-    logging.info(f"Input: {text} | Language: {language}")
-
-    try:
-        if language == "English":
-            X = en_vectorizer.transform([text])
-            pred = en_classifier.predict(X)[0]
-            result = en_label_encoder.inverse_transform([pred])[0]
-
-        elif language == "Sinhala":
-            X = si_vectorizer.transform([text])
-            pred = si_classifier.predict(X)[0]
-            result = si_label_encoder.inverse_transform([pred])[0]
-
-        elif language == "Tamil":
-            res = tamil_pipe(text)[0]
-            result = f"{res['label']} (Confidence: {res['score']:.3f})"
-
-        logging.info(f"Prediction: {result}")
-        return result
-
-    except Exception as e:
-        logging.error(f"Prediction error: {e}")
-        return f"❌ Error: {str(e)}"
-
-
-# ────────────────────────────────────────────────
-# 4. Gradio UI
-# ────────────────────────────────────────────────
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🌍 Multi-Language Emotion Detector")
-    gr.Markdown("Select a language and enter your text to analyze the emotion.")
-
-    with gr.Row():
-        with gr.Column():
-            input_text = gr.Textbox(
-                label="Input Text",
-                placeholder="Type here...",
-                lines=3
-            )
-
-            lang_dropdown = gr.Dropdown(
-                choices=["English", "Sinhala", "Tamil"],
-                value="English",
-                label="Select Language"
-            )
-
-            submit_btn = gr.Button("Analyze Emotion", variant="primary")
-
-        with gr.Column():
-            output_label = gr.Label(label="Predicted Emotion")
-
-    submit_btn.click(
-        fn=predict_emotion,
-        inputs=[input_text, lang_dropdown],
-        outputs=output_label
-    )
-
-    gr.Examples(
-        examples=[
-            ["I am very happy today!", "English"],
-            ["මම අද ගොඩක් සතුටින්", "Sinhala"],
-            ["நான் இன்று மிகவும் மகிழ்ச்சியாக இருக்கிறேன்", "Tamil"]
-        ],
-        inputs=[input_text, lang_dropdown]
+from transformers import pipeline
+
+app = Flask(__name__)
+
+print("Loading English model...")
+vectorizer = joblib.load(hf_hub_download(
+    "E-motionAssistant/Englsih_Trained_Model_LR",
+    "tfidf_vectorizer.joblib"
+))
+classifier = joblib.load(hf_hub_download(
+    "E-motionAssistant/Englsih_Trained_Model_LR",
+    "logreg_model.joblib"
+))
+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")
+
+print("Loading Tamil model...")
+tamil_pipe = pipeline("text-classification",
+                      model="E-motionAssistant/Tamil_Emotion_Recognition_Model")
+
+print("All models loaded successfully!")
+
+@app.route("/", methods=["GET", "POST"])
+def home():
+    english_result = ""
+    sinhala_result = ""
+    tamil_result   = ""
+
+    if request.method == "POST":
+        action = request.form.get("action")
+
+        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]
+                english_result = f"Emotion: {emotion}"
+            else:
+                english_result = "Please write something"
+
+        elif action == "predict_sinhala":
+            text = request.form.get("sinhala_text", "").strip()
+            if text:
+                res = sinhala_pipe(text)[0]
+                sinhala_result = f"හැඟීම: {res['label']} ({res['score']:.3f})"
+            else:
+                sinhala_result = "කරුණාකර යමක් ලියන්න"
+
+        elif action == "predict_tamil":
+            text = request.form.get("tamil_text", "").strip()
+            if text:
+                res = tamil_pipe(text)[0]
+                tamil_result = f"உணர்வு: {res['label']} ({res['score']:.3f})"
+            else:
+                tamil_result = "கருணையுடன் உரையை உள்ளிடவும்"
+
+    return render_template(
+        "index.html",
+        english_result=english_result,
+        sinhala_result=sinhala_result,
+        tamil_result=tamil_result
     )
 
-# ────────────────────────────────────────────────
-# 5. Launch (IMPORTANT for Spaces)
-# ────────────────────────────────────────────────
-demo.launch()
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7860, debug=False)