Update llm_integration.py

llm_integration.py

@@ -1,64 +1,57 @@
-import openai
-from openai import OpenAI
+# llm_integration.py
 import os
 
-client = OpenAI()
+# Try to set up an OpenAI client. If anything fails, keep _client=None so the app still runs.
+try:
+    from openai import OpenAI
+    _client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+    _model = os.getenv("OPENAI_MODEL", "gpt-4o-mini")  # change if you use Azure/OpenRouter
+except Exception as e:
+    _client = None
+    _model = None
 
-def generate_workout_with_llm(level: str, days_per_week: int, goal: str, equipment: str) -> str:
-    prompt = f"""Generate a {level} level weekly workout plan for someone who wants to {goal}. 
-    They will be working out {days_per_week} days a week and have access to {equipment}. 
-    Provide specific exercises, sets, and reps. Also, include a brief warm-up and cool-down suggestion.
-    Format the output as a Markdown list, with each day as a bold heading.
-    Example:
-    **Day 1 - Push**
-    - Exercise 1: Sets x Reps
-    - Exercise 2: Sets x Reps
-    ..."""
-    
-    response = client.chat.completions.create(
-        model="gemini-2.5-flash",
-        messages=[
-            {"role": "system", "content": "You are an expert fitness coach providing personalized workout plans."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=700,
-        temperature=0.7,
-    )
-    return response.choices[0].message.content
-
-def generate_nutrition_with_llm(weight_kg: float, target_kcal: int, liked_csv: str, avoid_csv: str, meals: int) -> str:
-    prompt = f"""Generate a personalized daily nutrition plan for someone weighing {weight_kg} kg, 
-    with a target of {target_kcal} kcal/day, spread across {meals} meals. 
-    They like these foods: {liked_csv if liked_csv else 'none specified'}. 
-    They want to avoid these foods: {avoid_csv if avoid_csv else 'none specified'}. 
-    Provide specific meal ideas for each meal, including approximate macros (protein, carbs, fat) for the day, 
-    and a general grocery list. Also, include a brief tip for healthy eating.
-    Format the output as a Markdown list, with each meal as a bold heading.
-    Example:
-    ### 🥗 Nutrition Plan
-    - **Target:** 2000 kcal/day
-    - **Macros (approx.):** Protein 150g, Carbs 200g, Fat 60g
-    **Meal 1: Breakfast**
-    - Scrambled eggs with spinach and whole-wheat toast.
-    ..."""
-    
-    response = client.chat.completions.create(
-        model="gemini-2.5-flash",
-        messages=[
-            {"role": "system", "content": "You are an expert nutritionist providing personalized meal plans and advice."},
-            {"role": "user", "content": prompt}
-        ],
-        max_tokens=800,
-        temperature=0.7,
-    )
-    return response.choices[0].message.content
+DISABLED_MSG = (
+    "[LLM disabled] Install 'openai' and set the OPENAI_API_KEY secret "
+    "in your Space settings to enable AI-generated plans."
+)
 
+def _chat(system: str, user: str, temperature: float = 0.4) -> str:
+    """Small helper that calls the model or returns a friendly fallback."""
+    if _client is None or _model is None:
+        return DISABLED_MSG
+    try:
+        resp = _client.chat.completions.create(
+            model=_model,
+            messages=[{"role": "system", "content": system},
+                      {"role": "user", "content": user}],
+            temperature=temperature,
+        )
+        return (resp.choices[0].message.content or "").strip()
+    except Exception as e:
+        # Keep the UI responsive even if the API fails
+        return f"[LLM error] {e}"
 
-# LLM-enhanced agent_workout
 def agent_workout_llm(level: str, days_per_week: int, goal: str, equipment: str) -> str:
-    return generate_workout_with_llm(level, days_per_week, goal, equipment)
-
-# LLM-enhanced agent_nutrition
-def agent_nutrition_llm(weight_kg: float, target_kcal: int, liked_csv: str, avoid_csv: str, meals: int) -> str:
-    return generate_nutrition_with_llm(weight_kg, target_kcal, liked_csv, avoid_csv, meals)
+    system = (
+        "You are a certified strength coach. Create concise, beginner-safe weekly training plans. "
+        "Prefer compound movements, progressive overload, clear sets x reps, and brief cues."
+    )
+    user = (
+        f"Design a {int(days_per_week)}-day plan for a {level} user. "
+        f"Goal: {goal}. Equipment: {equipment}. "
+        "Format with bullets, include sets x reps, rest times, and 1–2 safety tips."
+    )
+    return _chat(system, user, temperature=0.35)
 
+def agent_nutrition_llm(weight_kg: float, target_kcal: int,
+                        liked_csv: str, avoid_csv: str, meals: int) -> str:
+    system = (
+        "You are a registered dietitian. Provide practical beginner nutrition plans. "
+        "Respect preferences and allergies. Keep portions realistic."
+    )
+    user = (
+        f"Create a {int(meals)}-meal/day plan ≈{int(target_kcal)} kcal for a {float(weight_kg)} kg person. "
+        f"Preferred foods: {liked_csv or 'none'}. Avoid: {avoid_csv or 'none'}. "
+        "Include approximate macros per meal and a short grocery list."
+    )
+    return _chat(system, user, temperature=0.35)