Create app.py

app.py

@@ -0,0 +1,103 @@
+import os
+import gradio as gr
+from dotenv import load_dotenv
+from google import genai
+
+# Load environment variables
+load_dotenv()
+api_key = os.getenv("GEMINI_API_KEY")
+
+# Initialize Gemini client
+client = genai.Client(api_key=api_key)
+
+# Function to calculate daily calorie requirements
+def calculate_calorie_requirements(age, gender, weight, height, fitness_goal):
+    if gender == "Male":
+        bmr = 10 * weight + 6.25 * height - 5 * age + 5
+    else:
+        bmr = 10 * weight + 6.25 * height - 5 * age - 161
+
+    if fitness_goal == "Weight Loss":
+        return bmr * 1.2
+    elif fitness_goal == "Weight Gain":
+        return bmr * 1.5
+    else:
+        return bmr * 1.375
+
+# Generate personalized plan
+def generate_plan(name, age, gender, weight, height, fitness_goal, dietary_preference,
+                  food_allergies, local_cuisine, month, include_ayurveda):
+    bmi = round(weight / (height / 100) ** 2, 2)
+    health_status = "Underweight" if bmi < 18.5 else "Normal weight" if bmi <= 24.9 else "Overweight"
+    daily_calories = calculate_calorie_requirements(age, gender, weight, height, fitness_goal)
+
+    metrics = {
+        "name": name,
+        "age": age,
+        "gender": gender,
+        "bmi": bmi,
+        "health_status": health_status,
+        "fitness_goal": fitness_goal,
+        "dietary_preference": dietary_preference,
+        "food_allergies": food_allergies,
+        "daily_calories": int(daily_calories),
+        "local_cuisine": local_cuisine,
+        "month": month,
+    }
+
+    if include_ayurveda:
+        prompt = f"""
+        You are a health expert specializing in both modern medicine and Ayurveda.
+        Generate a personalized weekly diet and exercise plan for {name}, a {age}-year-old {gender}
+        with a BMI of {bmi} ({health_status}).
+        Goal: {fitness_goal}. Daily Calorie Requirement: {int(daily_calories)} kcal.
+        Dietary Preference: {dietary_preference}. Allergies: {food_allergies}.
+        Local Cuisine: {local_cuisine}. Month: {month}.
+        Include Ayurvedic insights and dosha-based recommendations.
+        """
+    else:
+        prompt = f"""
+        You are a health expert.
+        Generate a personalized weekly diet and exercise plan for {name}, a {age}-year-old {gender}
+        with a BMI of {bmi} ({health_status}).
+        Goal: {fitness_goal}. Daily Calorie Requirement: {int(daily_calories)} kcal.
+        Dietary Preference: {dietary_preference}. Allergies: {food_allergies}.
+        Local Cuisine: {local_cuisine}. Month: {month}.
+        """
+
+    try:
+        response = client.models.generate_content(
+            model="gemini-2.5-flash",
+            contents=prompt
+        )
+        return f"**Your BMI:** {bmi} ({health_status})\n\n" + response.text
+    except Exception as e:
+        return f"Error calling Gemini API: {e}"
+
+# Gradio UI
+iface = gr.Interface(
+    fn=generate_plan,
+    inputs=[
+        gr.Textbox(label="Name"),
+        gr.Number(label="Age", value=25),
+        gr.Radio(["Male", "Female", "Other"], label="Gender"),
+        gr.Number(label="Weight (kg)", value=70),
+        gr.Number(label="Height (cm)", value=170),
+        gr.Radio(["Weight Loss", "Weight Gain", "Maintenance"], label="Fitness Goal"),
+        gr.Dropdown(["Vegetarian", "Vegan", "Keto", "Halal", "None"], label="Dietary Preference"),
+        gr.Textbox(label="Food Allergies (if any)"),
+        gr.Textbox(label="Preferred Local Cuisine"),
+        gr.Dropdown(
+            ["January", "February", "March", "April", "May", "June",
+             "July", "August", "September", "October", "November", "December"],
+            label="Month"
+        ),
+        gr.Checkbox(label="Include Ayurvedic insights", value=True),
+    ],
+    outputs=gr.Markdown(label="Personalized Health Plan"),
+    title="AI-Based Personalized Weekly Diet and Exercise Planner (Gemini 2.5 Flash Pro)",
+    description="Uses Google Gemini AI to generate a custom health and fitness plan integrating Ayurvedic insights."
+)
+
+if __name__ == "__main__":
+    iface.launch()