Spaces:
Sleeping
Sleeping
| 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) | |