Update app.py

app.py

@@ -1,91 +1,74 @@
-import gradio as gr
+from fastapi import FastAPI
+from pydantic import BaseModel
 import joblib
+import logging
 from huggingface_hub import hf_hub_download
 from transformers import pipeline
 
-# ────────────────────────────────────────────────
-# 1. Load Models & Components
-# ────────────────────────────────────────────────
-print("Loading Models...")
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
-# English (LR)
-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"))
+app = FastAPI()
+models = None
 
-# Sinhala (LR)
-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"))
+def load_models():
+    global models
+    if models is not None:
+        return
+    logging.info("Loading models...")
 
-# Tamil (Transformers)
-tamil_pipe = pipeline("text-classification", model="E-motionAssistant/Tamil_Emotion_Recognition_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"))
 
-print("All models loaded successfully!")
+    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"))
 
-# ────────────────────────────────────────────────
-# 2. Prediction Functions
-# ────────────────────────────────────────────────
+    tamil_pipe = pipeline("text-classification", model="E-motionAssistant/Tamil_Emotion_Recognition_Model", device=-1)
+
+    models = (en_vectorizer, en_classifier, en_label_encoder,
+              si_vectorizer, si_classifier, si_label_encoder, tamil_pipe)
+    logging.info("✅ All models loaded.")
+
+@app.on_event("startup")
+def startup_event():
+    load_models()
+
+class PredictRequest(BaseModel):
+    text: str
+    language: str  # "English", "Sinhala", or "Tamil"
+
+@app.get("/")
+def root():
+    return {"status": "ok", "message": "Emotion Detector API is running"}
+
+@app.post("/predict")
+def predict(req: PredictRequest):
+    if not req.text.strip():
+        return {"error": "Text cannot be empty"}
+
+    en_vec, en_clf, en_le, si_vec, si_clf, si_le, tamil_pipe = models
 
-def predict_emotion(text, language):
-    if not text.strip():
-        return "Please enter some text."
-    
     try:
-        if language == "English":
-            X = en_vectorizer.transform([text])
-            pred = en_classifier.predict(X)[0]
-            return en_label_encoder.inverse_transform([pred])[0]
-            
-        elif language == "Sinhala":
-            X = si_vectorizer.transform([text])
-            pred = si_classifier.predict(X)[0]
-            return si_label_encoder.inverse_transform([pred])[0]
-            
-        elif language == "Tamil":
-            res = tamil_pipe(text)[0]
-            return f"{res['label']} (Confidence: {res['score']:.3f})"
-            
+        if req.language == "English":
+            X = en_vec.transform([req.text])
+            pred = en_clf.predict(X)[0]
+            emotion = en_le.inverse_transform([pred])[0]
+            return {"emotion": emotion, "language": "English"}
+
+        elif req.language == "Sinhala":
+            X = si_vec.transform([req.text])
+            pred = si_clf.predict(X)[0]
+            emotion = si_le.inverse_transform([pred])[0]
+            return {"emotion": emotion, "language": "Sinhala"}
+
+        elif req.language == "Tamil":
+            res = tamil_pipe(req.text)[0]
+            return {"emotion": res["label"], "confidence": round(res["score"], 3), "language": "Tamil"}
+
+        else:
+            return {"error": f"Unsupported language: {req.language}"}
+
     except Exception as e:
-        return f"Error: {str(e)}"
-
-# ────────────────────────────────────────────────
-# 3. Gradio Interface
-# ────────────────────────────────────────────────
-
-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")
-
-    # This defines the API logic automatically
-    submit_btn.click(
-        fn=predict_emotion, 
-        inputs=[input_text, lang_dropdown], 
-        outputs=output_label,
-        api_name="predict" # This creates the /predict API endpoint
-    )
-
-    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]
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+        logging.error(f"Prediction error: {e}")
+        return {"error": str(e)}