import gradio as gr
import base64
from groq import Groq
from PIL import Image
import io
import os

# Initialize Groq client
client = Groq(api_key=os.getenv("GROQ_API_KEY"))

# Convert image → base64
def encode_image(image):
    buffered = io.BytesIO()
    image.save(buffered, format="JPEG")
    return base64.b64encode(buffered.getvalue()).decode()


# 🔍 STEP 1: Detect ingredients
def analyze_ingredients(image):
    if image is None:
        return "Please upload an image."

    base64_image = encode_image(image)

    try:
        completion = client.chat.completions.create(
            model="meta-llama/llama-4-scout-17b-16e-instruct",
            messages=[
                {
                    "role": "user",
                    "content": [
                        {
                            "type": "text",
                            "text": """
Analyze this image carefully.

If the image contains food:
→ List all ingredients clearly (comma separated).

If the image does NOT contain food:
→ Respond ONLY with: NO_FOOD_DETECTED
"""
                        },
                        {
                            "type": "image_url",
                            "image_url": {
                                "url": f"data:image/jpeg;base64,{base64_image}"
                            }
                        }
                    ],
                }
            ],
            temperature=0.3,
        )

        result = completion.choices[0].message.content.strip()

        return result

    except Exception as e:
        return f"Error: {str(e)}"


# 🍲 STEP 2: Generate recipe
def generate_recipe(ingredients_text):
    if not ingredients_text:
        return "No ingredients detected."

    # 🚨 HARD BLOCK for non-food
    if "NO_FOOD_DETECTED" in ingredients_text:
        return "❌ No food detected in the image. Please upload a valid food image."

    # Extra safety filter
    bad_keywords = ["diagram", "cnn", "architecture", "network", "chart"]
    if any(word in ingredients_text.lower() for word in bad_keywords):
        return "❌ Invalid input. This does not look like food ingredients."

    try:
        completion = client.chat.completions.create(
            model="llama-3.1-8b-instant",
            messages=[
                {
                    "role": "user",
                    "content": f"""
You are a strict cooking assistant.

Only generate a recipe if the input contains real food ingredients.

Ingredients:
{ingredients_text}

If this is NOT food → reply exactly:
INVALID_FOOD_INPUT

Otherwise provide:
- Dish name
- Ingredients
- Step-by-step instructions
"""
                }
            ],
            temperature=0.7,
        )

        result = completion.choices[0].message.content.strip()

        # 🚨 Final safety check
        if "INVALID_FOOD_INPUT" in result:
            return "❌ Could not generate recipe. Invalid food input."

        return result

    except Exception as e:
        return f"Error: {str(e)}"


# 🎨 UI
with gr.Blocks(title="AI Cooking Assistant") as app:
    gr.Markdown("# 🍳 AI Cooking Assistant")
    gr.Markdown("Upload food image → Detect ingredients → Generate recipe")

    image_input = gr.Image(type="pil", label="Upload Ingredients Image")

    detect_btn = gr.Button("🔍 Detect Ingredients")
    ingredients_output = gr.Textbox(label="Detected Ingredients")

    recipe_btn = gr.Button("🍲 Generate Recipe")
    recipe_output = gr.Textbox(label="Recipe", lines=12)

    detect_btn.click(analyze_ingredients, inputs=image_input, outputs=ingredients_output)
    recipe_btn.click(generate_recipe, inputs=ingredients_output, outputs=recipe_output)

app.launch()