Update app.py

app.py

@@ -1,76 +1,49 @@
-import json
-import random
-
-# Predefined attributes for empathy training
-emotions = ["lonely", "confused", "sadness", "frustrated", "scared"]
-intents = ["seek_reassurance", "express_confusion", "seek_support", "vent", "request_help"]
-difficulties = ["easy", "moderate", "hard"]
-tags_pool = [
-    ["memory", "forgetting", "support"],
-    ["family", "burden", "dementia"],
-    ["confusion", "orientation", "safety"],
-    ["fear", "loneliness", "aging"],
-    ["support", "validation", "love"]
-]
-
-# English and French empathetic templates
-en_templates = [
-    ("I feel like {emotion}.", "You're not alone. You're understood."),
-    ("I'm always {emotion}, it's scary.", "That sounds tough. I'm here to support you."),
-    ("Sometimes I just feel so {emotion}.", "You're valued. Let's work through this together."),
-    ("Why do I keep forgetting things?", "It's okay. Memory issues happen, and you’re not alone."),
-    ("I don’t know where I am.", "Let’s take a deep breath. I can help guide you.")
-]
-
-fr_templates = [
-    ("Je me sens {emotion}.", "Tu n'es pas seul. Je suis là pour toi."),
-    ("Je suis toujours {emotion}, c'est effrayant.", "C'est dur. Je te comprends."),
-    ("Parfois je me sens si {emotion}.", "Tu comptes beaucoup. Ensemble, on va y arriver."),
-    ("Pourquoi j'oublie tout le temps ?", "Ce n'est pas grave. Ça arrive à beaucoup de gens."),
-    ("Je ne sais pas où je suis.", "Respire un peu. Je vais t'aider.")
-]
-
-# Generate a single sample
-def create_entry(lang):
-    templates = en_templates if lang == "en" else fr_templates
-    input_template, response = random.choice(templates)
-    emotion = random.choice(emotions)
-    intent = random.choice(intents)
-    tags = random.choice(tags_pool)
-    difficulty = random.choice(difficulties)
-    input_text = input_template.format(emotion=emotion)
-
-    return {
-        "input": input_text,
-        "response": response,
-        "emotion": emotion,
-        "intent": intent,
-        "tags": tags,
-        "care_mode": True,
-        "language": lang,
-        "difficulty": difficulty,
-        "is_dementia_related": True
-    }
-
-# Generate 100 English and 70 French samples
-en_data = [create_entry("en") for _ in range(100)]
-fr_data = [create_entry("fr") for _ in range(70)]
-all_data = en_data + fr_data
-random.shuffle(all_data)
-
-# Smart split (70% train, 20% validation, 10% test)
-train = all_data[:int(0.7 * len(all_data))]
-validation = all_data[int(0.7 * len(all_data)):int(0.9 * len(all_data))]
-test = all_data[int(0.9 * len(all_data)):]
-
-# Write to JSON
-with open("dementia_train_split.json", "w", encoding="utf-8") as f:
-    json.dump(train, f, indent=2, ensure_ascii=False)
-
-with open("dementia_validation_split.json", "w", encoding="utf-8") as f:
-    json.dump(validation, f, indent=2, ensure_ascii=False)
-
-with open("dementia_test_multilang.json", "w", encoding="utf-8") as f:
-    json.dump(test, f, indent=2, ensure_ascii=False)
-
-print("✅ Dataset splits created (train/validation/test)")
+from fastapi import FastAPI
+from pydantic import BaseModel
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+import torch
+import uvicorn
+
+app = FastAPI()
+
+MODEL_NAME = "obx0x3/empathy-dementia"
+tokenizer = T5Tokenizer.from_pretrained(MODEL_NAME)
+model = T5ForConditionalGeneration.from_pretrained(MODEL_NAME)
+
+class PromptRequest(BaseModel):
+    message: str
+    lang: str = None  # Optional
+
+def detect_language(text: str):
+    fr_keywords = ["je", "tu", "c’est", "j’ai", "où", "suis", "pas", "peux"]
+    return "fr" if any(word in text.lower() for word in fr_keywords) else "en"
+
+def prefix_message(message: str, lang: str) -> str:
+    if lang == "fr":
+        return f"émotion: {message}"
+    elif any(q in message.lower() for q in ["why", "how", "what", "when", "where", "?"]):
+        return f"chat: {message}"
+    elif any(e in message.lower() for e in ["feel", "i’m", "i am", "sad", "scared", "lonely", "happy", "forgot"]):
+        return f"emotion: {message}"
+    else:
+        return f"chat: {message}"
+
+@app.get("/")
+def root():
+    return {"message": "✅ Empathy model running!"}
+
+@app.post("/generate")
+async def generate_response(payload: PromptRequest):
+    lang = payload.lang or detect_language(payload.message)
+    input_text = prefix_message(payload.message, lang)
+
+    inputs = tokenizer.encode(input_text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model.generate(inputs, max_length=128, num_beams=4, early_stopping=True)
+
+    result = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return {"reply": result.strip(), "language": lang}
+
+if __name__ == "__main__":
+    # For local testing: uvicorn app:app --reload --port 7860
+    uvicorn.run(app, host="0.0.0.0", port=7860)