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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"]})
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=state.df_topics_bm25
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) |