Create app.py

app.py

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+"""
+FitByte — Personalized AI Fitness Coach
+========================================
+Tech Stack : Groq API (LLaMA 3.3 70B) | Gradio | Hugging Face Spaces
+Concepts   : System Prompt Engineering, Prompt Chaining, Dynamic Prompts,
+             BMI Calculation, Input Validation, Secure API Key Handling
+"""
+
+import os
+import gradio as gr
+from groq import Groq
+
+# ─────────────────────────────────────────────
+# API SETUP  (Colab → HF Spaces compatible)
+# ─────────────────────────────────────────────
+def get_api_key() -> str:
+    try:
+        from google.colab import userdata
+        return userdata.get("GROQ_API_KEY")
+    except Exception:
+        key = os.getenv("GROQ_API_KEY")
+        if not key:
+            raise EnvironmentError("GROQ_API_KEY not found in environment.")
+        return key
+
+try:
+    client = Groq(api_key=get_api_key())
+    API_READY = True
+except Exception as e:
+    API_READY = False
+    API_ERROR = str(e)
+
+
+# ─────────────────────────────────────────────
+# BMI CALCULATION
+# ─────────────────────────────────────────────
+def calculate_bmi(weight_kg: float, height_cm: float) -> tuple:
+    if height_cm <= 0 or weight_kg <= 0:
+        raise ValueError("Weight and height must be positive numbers.")
+    height_m = height_cm / 100
+    bmi = round(weight_kg / (height_m ** 2), 1)
+    if bmi < 18.5:
+        category = "Underweight"
+    elif bmi < 25.0:
+        category = "Normal weight"
+    elif bmi < 30.0:
+        category = "Overweight"
+    else:
+        category = "Obese"
+    return bmi, category
+
+
+# ─────────────────────────────────────────────
+# PROMPT ENGINEERING
+# ─────────────────────────────────────────────
+COACHING_MODES = {
+    "Motivational Coach 🔥": (
+        "You are an energetic, motivational fitness coach. "
+        "Use powerful, encouraging language. Include motivational quotes. "
+        "Make the user feel unstoppable. Use emojis sparingly but effectively."
+    ),
+    "Scientific Advisor 🔬": (
+        "You are a sports scientist and certified nutritionist. "
+        "Use precise, evidence-based language. Cite physiological principles "
+        "(e.g., progressive overload, TDEE, macronutrient ratios). "
+        "Be clinical but clear. No fluff."
+    ),
+    "Friendly Buddy 😊": (
+        "You are the user's supportive gym buddy. "
+        "Keep the tone casual, warm, and relatable. "
+        "Use simple language. Make fitness feel approachable and fun."
+    ),
+    "Strict Drill Sergeant 💪": (
+        "You are a no-nonsense military fitness trainer. "
+        "Be direct, demanding, and results-focused. "
+        "No excuses. Short, punchy sentences. Push the user hard."
+    ),
+}
+
+def build_system_prompt(mode: str) -> str:
+    base = COACHING_MODES.get(mode, COACHING_MODES["Motivational Coach 🔥"])
+    return (
+        f"{base}\n\n"
+        "Always structure your response with these clearly labeled sections:\n"
+        "1. **BMI Analysis** — Interpret their BMI and what it means for them.\n"
+        "2. **Weekly Workout Plan** — Plan with specific exercises, sets, reps.\n"
+        "3. **Daily Meal Plan** — Breakfast, Lunch, Dinner, Snacks with approximate calories.\n"
+        "4. **Key Tips** — 3 personalized tips based on their goal and fitness level.\n"
+        "5. **Motivational Closing** — End with an inspiring one-liner.\n\n"
+        "Use markdown formatting with bold headers. Be specific, not generic.\n\n"
+        "IMPORTANT: Build the meal plan strictly around the user's available foods and cuisine. "
+        "Do NOT suggest foods they cannot access or afford. Use locally available, budget-friendly alternatives."
+    )
+
+def build_user_prompt(
+    name, age, gender, body_condition,
+    weight, height, bmi, bmi_cat,
+    goal, fitness_level, dietary_pref,
+    cuisine_pref, food_context,
+    workout_days, workout_duration,
+    health_conditions
+) -> str:
+    conditions_str = health_conditions.strip() if health_conditions else "None reported"
+    food_str = food_context.strip() if food_context else "No specific constraints"
+    return (
+        f"Create a fully personalized fitness plan for the following individual:\n\n"
+        f"**Personal Details:**\n"
+        f"- Name: {name}\n"
+        f"- Age: {age} years | Gender: {gender}\n"
+        f"- Weight: {weight} kg | Height: {height} cm\n"
+        f"- BMI: {bmi} ({bmi_cat}) | Body Condition: {body_condition}\n\n"
+        f"**Goals & Preferences:**\n"
+        f"- Primary Goal: {goal}\n"
+        f"- Current Fitness Level: {fitness_level}\n"
+        f"- Dietary Type: {dietary_pref}\n"
+        f"- Cuisine / Food Region: {cuisine_pref}\n"
+        f"- Food Availability & Budget: {food_str}\n"
+        f"- Workout Days per Week: {workout_days}\n"
+        f"- Workout Duration per Day: {workout_duration} minutes\n"
+        f"- Health Conditions / Injuries: {conditions_str}\n\n"
+        f"Generate a realistic, safe, and highly personalized plan. "
+        f"Account for their BMI category, body condition, and health conditions "
+        f"when recommending exercises and diet."
+    )
+
+
+# ─────────────────────────────────────────────
+# GROQ API CALL
+# ─────────────────────────────────────────────
+def call_groq(system_prompt: str, user_prompt: str) -> str:
+    response = client.chat.completions.create(
+        model="llama-3.3-70b-versatile",
+        messages=[
+            {"role": "system", "content": system_prompt},
+            {"role": "user", "content": user_prompt}
+        ],
+        max_tokens=2048
+    )
+    return response.choices[0].message.content
+
+
+# ─────────────────────────────────────────────
+# MAIN ORCHESTRATION
+# ─────────────────────────────────────────────
+def generate_fitness_plan(
+    name, age, gender, body_condition,
+    weight, height,
+    goal, fitness_level, dietary_pref,
+    cuisine_pref, food_context,
+    workout_days, workout_duration,
+    health_conditions, coaching_mode
+):
+    if not name.strip():
+        return "❌ Error", "Please enter your name.", ""
+    if not (10 <= int(age) <= 100):
+        return "❌ Error", "Age must be between 10 and 100.", ""
+    if weight <= 0 or height <= 0:
+        return "❌ Error", "Weight and height must be positive values.", ""
+    if not API_READY:
+        return "❌ API Error", f"Groq API not configured: {API_ERROR}", ""
+
+    try:
+        bmi, bmi_cat = calculate_bmi(weight, height)
+        bmi_display = f"**BMI: {bmi}** — {bmi_cat}"
+
+        system_prompt = build_system_prompt(coaching_mode)
+        user_prompt = build_user_prompt(
+            name, age, gender, body_condition,
+            weight, height, bmi, bmi_cat,
+            goal, fitness_level, dietary_pref,
+            cuisine_pref, food_context,
+            int(workout_days), int(workout_duration),
+            health_conditions
+        )
+
+        plan = call_groq(system_prompt, user_prompt)
+
+        download_text = (
+            f"FITBYTE — PERSONALIZED FITNESS PLAN\n"
+            f"Name: {name}\n"
+            f"{'='*50}\n\n"
+            f"BMI: {bmi} ({bmi_cat})\n"
+            f"Goal: {goal} | Level: {fitness_level} | Mode: {coaching_mode}\n"
+            f"{'='*50}\n\n"
+            + plan
+        )
+
+        return bmi_display, plan, download_text
+
+    except ValueError as ve:
+        return "❌ Input Error", str(ve), ""
+    except Exception as e:
+        return "❌ Error", f"Something went wrong: {str(e)}", ""
+
+
+def save_plan(download_text: str):
+    if not download_text:
+        return None
+    filepath = "/tmp/fitbyte_plan.txt"
+    with open(filepath, "w") as f:
+        f.write(download_text)
+    return filepath
+
+
+# ─────────────────────────────────────────────
+# GRADIO UI
+# ─────────────────────────────────────────────
+CSS = """
+footer { display: none !important; }
+"""
+
+with gr.Blocks(theme=gr.themes.Soft(primary_hue="emerald"), css=CSS, title="FitByte") as demo:
+
+    gr.Markdown("""
+    # 🏋️ FitByte — Personalized AI Fitness Coach
+    > **Powered by Groq (LLaMA 3.3 70B)** | Built with Gradio | Deployed on Hugging Face Spaces
+    """)
+
+    with gr.Row():
+
+        with gr.Column(scale=1):
+            gr.Markdown("### 👤 Personal Info")
+            name = gr.Textbox(label="Full Name", placeholder="e.g. Rohit Sharma")
+            age = gr.Number(label="Age", value=21, minimum=10, maximum=100)
+            gender = gr.Radio(["Male", "Female", "Other"], label="Gender", value="Male")
+            body_condition = gr.Dropdown(
+                ["Not Sure", "Skinny", "Skinny-Fat", "Average", "Overweight/Fat", "Muscular"],
+                label="Current Body Condition", value="Average"
+            )
+
+            gr.Markdown("### 📏 Body Metrics")
+            weight = gr.Number(label="Weight (kg)", value=70)
+            height = gr.Number(label="Height (cm)", value=175)
+
+            gr.Markdown("### 🎯 Fitness Profile")
+            goal = gr.Dropdown(
+                ["Weight Loss", "Muscle Gain", "Maintain Weight", "Improve Stamina", "Flexibility & Mobility"],
+                label="Primary Goal", value="Muscle Gain"
+            )
+            fitness_level = gr.Dropdown(
+                ["Beginner (0–6 months)", "Intermediate (6 months–2 years)", "Advanced (2+ years)"],
+                label="Fitness Level", value="Beginner (0–6 months)"
+            )
+            dietary_pref = gr.Dropdown(
+                ["No Preference", "Vegetarian", "Eggetarian", "Vegan", "Non-Vegetarian", "Keto", "High Protein"],
+                label="Dietary Preference", value="No Preference"
+            )
+            workout_days = gr.Slider(minimum=2, maximum=7, step=1, value=4, label="Workout Days per Week")
+            workout_duration = gr.Slider(minimum=20, maximum=120, step=10, value=45, label="Workout Duration per Day (minutes)")
+
+            gr.Markdown("### 🍽️ Food & Cuisine")
+            cuisine_pref = gr.Dropdown(
+                ["No Preference", "Indian", "South Indian", "Middle Eastern", "East Asian", "Mediterranean", "Western"],
+                label="Cuisine / Region", value="No Preference"
+            )
+            food_context = gr.Textbox(
+                label="Food Availability & Budget (optional)",
+                placeholder="e.g. I eat chapati, dal, sabzi daily. No bread or pasta. Tight budget.",
+                lines=2
+            )
+
+            gr.Markdown("### ⚕️ Health")
+            health_conditions = gr.Textbox(
+                label="Health Conditions / Injuries (optional)",
+                placeholder="e.g. Lower back pain, knee injury..."
+            )
+
+            gr.Markdown("### 🤖 Coaching Style")
+            coaching_mode = gr.Radio(
+                list(COACHING_MODES.keys()),
+                label="Select Your Coach",
+                value="Motivational Coach 🔥"
+            )
+
+            generate_btn = gr.Button("⚡ Generate My Plan", variant="primary", size="lg")
+
+        with gr.Column(scale=2):
+            gr.Markdown("### 📊 Your BMI")
+            bmi_output = gr.Markdown(value="_Your BMI will appear here._")
+
+            gr.Markdown("### 📋 Your Personalized Plan")
+            plan_output = gr.Markdown(value="_Fill in your details and hit Generate._", height=600)
+
+            with gr.Row():
+                download_state = gr.State("")
+                download_btn = gr.Button("💾 Download My Plan", variant="secondary")
+                download_file = gr.File(label="Your Plan (TXT)", visible=False)
+
+    gr.Markdown("""
+    ---
+    **📌 Disclaimer:** AI-generated for educational purposes. Consult a certified trainer and nutritionist before starting any fitness program.
+    """)
+
+    generate_btn.click(
+        fn=generate_fitness_plan,
+        inputs=[
+            name, age, gender, body_condition,
+            weight, height,
+            goal, fitness_level, dietary_pref,
+            cuisine_pref, food_context,
+            workout_days, workout_duration,
+            health_conditions, coaching_mode
+        ],
+        outputs=[bmi_output, plan_output, download_state]
+    )
+
+    download_btn.click(
+        fn=save_plan,
+        inputs=[download_state],
+        outputs=[download_file]
+    ).then(
+        fn=lambda: gr.File(visible=True),
+        outputs=[download_file]
+    )
+
+
+if __name__ == "__main__":
+    demo.launch()