File size: 8,189 Bytes
7efeab0 fcc06ed 7efeab0 fcc06ed d62b82e 7efeab0 fcc06ed 7efeab0 b4730c9 fcc06ed 7efeab0 9c7b0a3 7efeab0 fcbde97 2649612 52ecb98 7efeab0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 |
import gradio as gr
import pandas as pd
from functions import process_file_bm25 , process_file_bert , generate_plot , generate
#------------------------------------------------------
# Create the state object
# state = gr.State()
# state.df_bm25 = pd.DataFrame({"Products": [1, 2, 3], "column2": ["A", "B", "C"]})
# state.df_bert = pd.DataFrame({"Products": [1, 2, 3], "column2": ["A", "B", "C"]})
# state.df_topics_bert = pd.DataFrame({"Topic": [1, 2, 3], "column2": ["A", "B", "C"]})
# state.df_topics_bm25 = pd.DataFrame({"Topic": [1, 2, 3], "column2": ["A", "B", "C"]})
df_bm25=gr.State(value=pd.DataFrame({"Products": [1, 2, 3], "column2": ["A", "B", "C"]}))
df_topics_bm25 = gr.State(value=pd.DataFrame({"Topic": [1, 2, 3], "column2": ["A", "B", "C"]}))
with gr.Blocks() as bm25:
with gr.Row():
with gr.Column():
gr.Markdown(
"""
# Select a CSV/Excel file with column as 'products'
""")
inputfile = gr.File(file_types=['.csv','.xlsx'], label="Upload CSV/Excel file")
#german = gr.Textbox(label="German Text")
def confirmation():
doc='File uploaded! Press Cluster button'
return doc
def download_doc(doc):
return doc
def download_df():
df1=state.df
print(df1)
return df1
out = gr.Textbox()
mode=gr.Radio(["Automated clustering", "Manually choose parameters"], label="Type of algorithm", value="Automated clustering",info="Choose any mode u want")
inputfile.upload(confirmation,inputs=[],outputs=out)
with gr.Row():
with gr.Column():
min_cluster_size=gr.Slider(1, 100, value=5, step=1,label="min_cluster_size", info="Choose minimum No. of docs in a cluster. Lower the value ,higher the clusters created")
with gr.Column():
top_n_words=gr.Slider(1, 25, value=10, step=1,label="top_n_words", info="Choose no of key words for a cluster")
with gr.Column():
ngram=gr.Slider(1, 3, value=2, step=1,label="ngram", info="Choose no of n-grams words to be taken for clustering")
cluster_btn = gr.Button(value="Cluster")
#[ df,topics_info,barchart,topics_plot,heatmap,hierarchy]
tup=cluster_btn.click(process_file_bm25, inputs=[inputfile,mode,min_cluster_size,top_n_words,ngram],
outputs=[
gr.Dataframe(),
gr.File(label="Download CSV"),
gr.Dataframe(),
#'html',
gr.Plot(label="Barchart"),
gr.Plot(label="Topics Plot"),
gr.Plot(label="Heatmap"),
gr.Plot(label="Hierarchy"),
])
print('Tuple **************************' ,tup)
#[df1, df2, barchart_plot, topics_plot, heatmap_plot, hierarchy_plot] = tup
llm_btn = gr.Button(value="AI generation ")
llm_btn.click(download_df,inputs=[],outputs=gr.Dataframe(label="Output"))
with gr.Blocks() as bert:
with gr.Row():
with gr.Column():
gr.Markdown(
"""
# Select a CSV/Excel file with column as 'products'
""")
inputfile = gr.File(file_types=['.csv','.xlsx'], label="Upload CSV/Excel file")
#german = gr.Textbox(label="German Text")
def confirmation():
doc='File uploaded! Press Cluster button'
return doc
out = gr.Textbox()
mode=gr.Radio(["Automated clustering", "Manually choose parameters"], label="Type of algorithm", value="Automated clustering",info="Choose any mode u want")
inputfile.upload(confirmation,inputs=[],outputs=out)
with gr.Row():
with gr.Column():
min_cluster_size=gr.Slider(1, 100, value=5, step=1,label="min_cluster_size", info="Choose minimum No. of docs in a cluster. Lower the value ,higher the clusters created")
with gr.Column():
top_n_words=gr.Slider(1, 25, value=10, step=1,label="top_n_words", info="Choose no of key words for a cluster")
with gr.Column():
ngram=gr.Slider(1, 3, value=2, step=1,label="ngram", info="Choose no of n-grams words to be taken for clustering")
cluster_btn = gr.Button(value="Cluster")
#[ df,topics_info,barchart,topics_plot,heatmap,hierarchy]
tup=cluster_btn.click(process_file_bert, inputs=[inputfile,mode,min_cluster_size],
outputs=[
gr.Dataframe(),
gr.Dataframe(),
gr.Plot(label="Barchart"),
gr.Plot(label="Topics Plot"),
gr.Plot(label="Heatmap"),
gr.Plot(label="Hierarchy")
])
#___________________________________________
additional_inputs=[
gr.Textbox(
label="System Prompt",
max_lines=1,
interactive=True,
),
gr.Slider(
label="Temperature",
value=0.9,
minimum=0.0,
maximum=1.0,
step=0.05,
interactive=True,
info="Higher values produce more diverse outputs",
),
gr.Slider(
label="Max new tokens",
value=256,
minimum=0,
maximum=4096,
step=64,
interactive=True,
info="The maximum numbers of new tokens",
),
gr.Slider(
label="Top-p (nucleus sampling)",
value=0.90,
minimum=0.0,
maximum=1,
step=0.05,
interactive=True,
info="Higher values sample more low-probability tokens",
),
gr.Slider(
label="Repetition penalty",
value=1.2,
minimum=1.0,
maximum=2.0,
step=0.05,
interactive=True,
info="Penalize repeated tokens",
)
]
examples=[["I'm planning a vacation to Japan. Can you suggest a one-week itinerary including must-visit places and local cuisines to try?", None, None, None, None, None, ],
["Can you write a short story about a time-traveling detective who solves historical mysteries?", None, None, None, None, None,],
["I'm trying to learn French. Can you provide some common phrases that would be useful for a beginner, along with their pronunciations?", None, None, None, None, None,],
["I have chicken, rice, and bell peppers in my kitchen. Can you suggest an easy recipe I can make with these ingredients?", None, None, None, None, None,],
["Can you explain how the QuickSort algorithm works and provide a Python implementation?", None, None, None, None, None,],
["What are some unique features of Rust that make it stand out compared to other systems programming languages like C++?", None, None, None, None, None,],
]
chat_interface=gr.ChatInterface(
fn=generate,
chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
additional_inputs=additional_inputs,
title="Mixtral 46.7B",
examples=examples,
concurrency_limit=20,
)
#______________________________________________________
# Create a Gradio interface
#df=pd.DataFrame(columns=['Topic'])
df=df_topics_bm25.value
print(df)
excel_analysis_bm25 = gr.Interface(
fn=generate_plot, # Placeholder function, will be defined later
inputs=[
gr.Dropdown(df['Topic'].unique().tolist(), label="Select Topic Number", type="index"),
gr.Dropdown(list(df.columns[~df.columns.isin(['Topic'])]), label="Select X Axis", type="index"),
gr.Dropdown(list(df.columns[~df.columns.isin(['Topic'])]), label="Select Y Axis", type="index"),
gr.Radio(["scatter", "bar", "line", "box", "wordcloud", "pie"], label="Select Chart Type"),
gr.Dropdown(["count", "count_distinct", "sum", "average"], label="Select Aggregation Function")
],
outputs=gr.Plot(label="Visualization")
)
demo = gr.TabbedInterface([bm25,chat_interface,excel_analysis_bm25,
bert], ["TFIDF-BM25 Clustering", "TFIDF-BM25-Topics AI","TFIDF-BM25-Topic analysis","keyBERT"])
demo.launch(share=True,debug=True) |