import gradio as gr
import pandas as pd
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import openpyxl

file_name = "cluster_fin_iter1.xlsx"
ques_1 = "what category is having the highest number of issues?"
ques_2 = "what category is having the highest number of issues ?"

def greet(name, intensity):
    return "Hello, " + name + "!" * int(intensity)
def get_data():
    df = pd.read_excel(file_name)

    return df

def echo(message, history):
    #TBD - Integerate with Llama 3. the below logic is just for the demo
    message = message.lower()
    if(message == "what category is having the highest number of issues?"):
        merge_data = get_merge_data()
        man_category = merge_data.max(axis=0) #TBD with the model
        return "category 23: PR guide and contributor notes"
    elif(message == "what category is having the highest number of issues?"):
        merge_data = get_merge_data()
        man_category = merge_data.max(axis=0)
        return "category 23: PR guide and contributor notes"
    elif(message == "what is category 23?"):
        res = "category 23: PR Guides and contributor notes"
    elif(message == "what all categories?" or message == "categories?"):
        labels = get_labels()
        res = str(labels['New_label'])
    elif(message=="total categories"):
        labels = get_labels()
        res = "Total categories " + str(labels["cluster"].count())
    elif (message == "least affected category?"):
        merge_data = get_merge_data()
        min_category = merge_data.min(axis = 0)
        return "Least affected category is " + min_category["New_label"]

    else:

        res = "Can you repeat that again"
    message = res
    return message
def get_merge_data():
    df = get_data()
    bar_df = df['cluster'].value_counts().to_frame('count').rename_axis('cluster').reset_index()
    label_df = pd.read_excel("cluster_label_final.xlsx")
    df_merge_label = pd.merge(bar_df, label_df, "left", on="cluster")
    return df_merge_label
def get_labels():
    label_df = pd.read_excel("cluster_label_final.xlsx")
    return label_df
def bar_plot_fn():
    new_df = []
    df = get_data()
    bar_df = df['cluster'].value_counts().to_frame('count').rename_axis('cluster').reset_index()
    label_df = pd.read_excel("cluster_label_final.xlsx")
    df_merge_label = pd.merge(bar_df,label_df,"left",on="cluster")


    print(bar_df)
    fig = plt.figure()
    return gr.BarPlot(
        value=df_merge_label,
        x='cluster',
        y='count',
        title="Automated Ticketing system",
        tooltip=["cluster","New_label", "count"],
        y_lim=[10, 4000],
    )
    # __________________
with gr.Blocks() as bar_plot:
    with gr.Row():
         with gr.Column():
            plot = gr.BarPlot()
    with gr.Row():
         with gr.Column():
             gr.ChatInterface(
                 fn=echo,
                 title="AI Agent For ATS",
                 multimodal= False)

    bar_plot.load(fn=bar_plot_fn, outputs=plot)

bar_plot.launch(share=True)