import gradio as gr
import requests
from huggingface_hub import InferenceClient


# Step 1 from Semantic Search
from sentence_transformers import SentenceTransformer
import torch


# # Step 2 from Semantic Search
# with open("water_cycle.txt", "r", encoding="utf-8") as file:
#   # Read the entire contents of the file and store it in a variable
#   water_cycle_text = file.read()

#     # Print the text below
# print(water_cycle_text)



SPOONACULAR_API_KEY = "71259036cfb3405aa5d49c1220a988c5"

def get_recipes(ingredient):
    url = "https://api.spoonacular.com/recipes/complexSearch"
    params = {
        "query": ingredient,
        "number": 3,
        "apiKey": SPOONACULAR_API_KEY
    }
    res = requests.get(url, params=params)
    data = res.json()
    # return [r["title"] for r in data["results"]]
    return data

# iface = gr.Interface(
#     fn=get_recipes,
#     inputs="text",
#     outputs="text",
#     title="Spoonacular Recipe Finder"
# )

# iface.launch()

"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")


def respond(
    message,
    history: list[tuple[str, str]],
    system_message,
    max_tokens,
    temperature,
    top_p,
):
    messages = [{"role": "system", "content": system_message}]

    for val in history:
        if val[0]:
            messages.append({"role": "user", "content": val[0]})
        if val[1]:
            messages.append({"role": "assistant", "content": val[1]})

    messages.append({"role": "user", "content": message})

    response = ""

    for message in client.chat_completion(
        messages,
        max_tokens=max_tokens,
        stream=True,
        temperature=temperature,
        top_p=top_p,
    ):
        token = message.choices[0].delta.content

        response += token
        yield response


"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
    respond,
    additional_inputs=[
        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
        gr.Slider(
            minimum=0.1,
            maximum=1.0,
            value=0.95,
            step=0.05,
            label="Top-p (nucleus sampling)",
        ),
    ],
)


# if __name__ == "__main__":
demo.launch()