import gradio as gr
from huggingface_hub import InferenceClient
import requests
from sentence_transformers import SentenceTransformer
import torch


SPOONACULAR_API_KEY = "71259036cfb3405aa5d49c1220a988c5"
recipe_id_map = {}

client=InferenceClient("mistralai/Mistral-7B-Instruct-v0.2")

# # Search recipes

global recipe_id_map
url = "https://api.spoonacular.com/recipes/complexSearch"
params = {
    # "query": ingredient,
    # "cuisine": cuisine,
    # "diet": dietary,
    "number": 3,
    "apiKey": SPOONACULAR_API_KEY
}
res = requests.get(url, params=params)
data = res.json()


#     if "results" not in data or not data["results"]:
#         recipe_id_map = {}
#         return gr.update(choices=[], visible=True, label="No recipes found"), gr.update(value="No recipes found.")

#     recipe_id_map = {r["title"]: r["id"] for r in data["results"]}
#     return gr.update(choices=list(recipe_id_map.keys()), visible=True), gr.update(value="Select a recipe from the dropdown.")

# # Get recipe details
# def get_recipe_details(selected_title):
#     if not selected_title or selected_title not in recipe_id_map:
#         return "Please select a valid recipe."

#     recipe_id = recipe_id_map[selected_title]
#     url = f"https://api.spoonacular.com/recipes/{recipe_id}/information"
#     params = {"apiKey": SPOONACULAR_API_KEY}
#     res = requests.get(url, params=params)
#     data = res.json()

#     title = data.get("title", "Unknown Title")
#     time = data.get("readyInMinutes", "N/A")
#     instructions = data.get("instructions") or "No instructions available."
#     ingredients_list = data.get("extendedIngredients", [])
#     ingredients = "\n".join([f"- {item.get('original')}" for item in ingredients_list])

#     return f"### 🍽️ {title}\n**⏱️ Cook Time:** {time} minutes\n\n**📋 Instructions:**\n{instructions}"
#     gr.Markdown("💬 Go to the next tab to ask our chatbot your questions on the recipe!")
# # Handle chatbot questions
# def ask_recipe_bot(message, history):
#     # Try to find a recipe ID from previous dropdown results
#     if not recipe_id_map:
#         return "Please use the dropdown tab first to search for a recipe."

#     # Use the first recipe ID from the map
#     recipe_id = list(recipe_id_map.values())[0]
#     url = f"https://api.spoonacular.com/recipes/{recipe_id}/nutritionWidget.json"
#     params = {"apiKey": SPOONACULAR_API_KEY}
#     res = requests.get(url, params=params)
    
#     if res.status_code != 200:
#         return "Sorry, I couldn't retrieve nutrition info."

#     data = res.json()
#     calories = data.get("calories", "N/A")
#     carbs = data.get("carbs", "N/A")
#     protein = data.get("protein", "N/A")
#     fat = data.get("fat", "N/A")

#     if "calorie" in message.lower():
#         return f"This recipe has {calories}."
#     elif "protein" in message.lower():
#         return f"It contains {protein}."
#     elif "carb" in message.lower():
#         return f"It has {carbs}."
#     elif "fat" in message.lower():
#         return f"The fat content is {fat}."
#     elif "scale" in message.lower() or "double" in message.lower():
#         return "You can scale ingredients by multiplying each quantity. For example, to double the recipe, multiply every amount by 2."
#     elif "substitute" in message.lower():
#         return "Let me know the ingredient you'd like to substitute, and I’ll try to help!"
#     else:
#         return "You can ask about calories, protein, carbs, fat, substitutes, or scaling tips."

def respond(message, history, ingredient, cuisine, diet):

    context = search_recipes(ingredient, cuisine, diet)
    messages = [
        {
            "role": "system",
            "content": f"You give recipes from {context}."
        }
    ]
    if history:
        messages.extend(history)
    messages.append({"role": "user", "content": message})
    stream = client.chat_completion(
        messages,
        max_tokens=300,
        temperature=1.2,
        stream=True,
    )
    for message in stream:
        token = message.choices[0].delta.content
        if token is not None:
            response += token
            yield response 


# # Gradio layout
with gr.Blocks() as demo:
    gr.Markdown("## 🧠🍴 The BiteBot")

    with gr.Tabs():
        # with gr.Tab("Search Recipes"):
            with gr.Row():
                ingredient = gr.Textbox(label="Preferred Ingredient")
                cuisine = gr.Textbox(label="Preferred Cuisine")
                diet = gr.Textbox(label="Dietary Restrictions")

            # search_button = gr.Button("Search Recipes")
            # recipe_dropdown = gr.Dropdown(label="Select a recipe", visible=False)
            # recipe_output = gr.Markdown()

            # search_button.click(
            #     fn=search_recipes,
            #     inputs=[ingredient, cuisine, diet],
            #     outputs=[recipe_dropdown, recipe_output]
            # )

            # recipe_dropdown.change(
            #     fn=get_recipe_details,
            #     inputs=recipe_dropdown,
            #     outputs=recipe_output
            # )

        # with gr.Tab("Ask BiteBot"):
            chatbot = gr.ChatInterface(fn=respond, additional_inputs = [ingredient, cuisine, diet], type="messages")
            gr.Markdown("💬 Ask about calories, macros, scaling, or substitutions. (Run a recipe search first!)")

demo.launch()