import os
import json
import gradio as gr
import google.generativeai as genai

GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")
genai.configure(api_key=GOOGLE_API_KEY)

# Set up the model
generation_config = {
    "temperature": 0.9,
    "top_p": 1,
    "top_k": 1,
    "max_output_tokens": 2048,
}


model = genai.GenerativeModel(
    model_name="gemini-pro",
    generation_config=generation_config,
)

task_description = " You need to classify each message you receive among the following categories: 'admiration','amusement','anger','annoyance','approval','caring','confusion','curiosity','desire','disappointment','disapproval','disgust','embarrassment','excitement','fear','gratitude','grief','joy','love','nervousness', 'optimism', 'pride', 'realization', 'relief', 'remorse', 'sadness', 'surprise', 'neutral'<div>Let the output be a softmax function output giving the probability of message belonging to each category.</div><div>The sum of the probabilities should be 1</div><div>The output must be in JSON format</div>"


def classify_msg(message):
    prompt_parts = [
        task_description,
        f"Message: {message}",
        "Category: ",
    ]

    response = model.generate_content(prompt_parts)

    json_response = json.loads(
        response.text[response.text.find("{") : response.text.rfind("}") + 1]
    )

    return gr.Label(json_response)

iface = gr.Interface(fn=classify_msg, inputs="text", outputs="text")
iface.launch()