app.py

#!/usr/bin/env python
# coding: utf-8

# In[10]:


import pandas as pd
import os
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers.optimization import  Adafactor 
import time
import warnings
import random
warnings.filterwarnings('ignore')

import re

def strip_html(text):
    return re.sub('<[^<]+?>', '', text)


# In[5]:


train_columns = ['round_amount', 'round_date', 'stage', 'investee',
       'investee_description', 'investee_country', 'investee_region',
       'investee_subregion', 'investee_vertical', 'investee_industry',
       'investor_list', 'previous_investors', 'prior_funding']
train = pd.read_csv("train.csv")


# In[6]:


train.publication_timestamp = pd.to_datetime(train.publication_timestamp)


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input_text = train[train_columns].to_dict(orient='records')
train_df = train[['title']].rename(columns={'title':'target_text'})
train_df['input_text'] = input_text
train_df['prefix'] = 'tia'
train_df.input_text = train_df.input_text.astype(str)


# In[8]:


if torch.cuda.is_available():
    dev = torch.device("cuda:0") 
    print("Running on the GPU")
else:
    dev = torch.device("cpu")
    print("Running on the CPU")


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tokenizer = T5Tokenizer.from_pretrained('google/t5-v1_1-base')
model = T5ForConditionalGeneration.from_pretrained('t5-v1_1-base_tia/', local_files_only=True)
#moving the model to device(GPU/CPU)
model.to(dev)


# In[12]:


vi_table = train[['investee_industry', 'investee_vertical']].drop_duplicates()


# In[13]:


def update_industry(value):
    verticals = list(vi_table[vi_table['investee_industry'] == value]['investee_vertical'].values)
    return verticals[0]

def update_vertical(value):
    industries = list(vi_table[vi_table['investee_vertical'] == value]['investee_industry'].values)
    return industries[0]


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update_industry('Green')


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update_vertical('Clean tech')


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import gradio as gr


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num_return_sequences = 5


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def generate_headline(stage, investee_country, investee_subregion, investee_region, 
                      investee_vertical, investee_industry, 
                      round_amount, investee, investee_description, investor_list, previous_investors,
                      other_values):

    full_df = other_values.set_index("key").T
    
    full_df['stage'] = stage
    full_df['investee_country'] = investee_country
    full_df['investee_subregion'] = investee_subregion
    full_df['investee_region'] = investee_region
    full_df['investee_vertical'] = investee_vertical
    full_df['investee_industry'] = investee_industry
    full_df['round_amount'] = str(float(round_amount))
    full_df['investee'] = investee
    full_df['investee_description'] = investee_description
    full_df['investor_list'] = investor_list
    full_df['previous_investors'] = previous_investors
    
    random_set =full_df[['round_amount', 'round_date', 'stage', 'investee',
       'investee_description', 'investee_country', 'investee_region',
       'investee_subregion', 'investee_vertical', 'investee_industry',
       'investor_list', 'previous_investors', 'prior_funding']].to_json(orient="records")
#    print(random_set)
    
    input_ids = tokenizer.encode(f"tia: {{{random_set}}}", return_tensors="pt")  # Batch size 1
    input_ids=input_ids.to(dev)
    outputs = model.generate(input_ids)
    # text_output = tokenizer.decode(outputs[0]) # Single output
    text_outputs = model.generate(inputs=input_ids, do_sample=True, 
                                  num_beams=2, 
                                  num_return_sequences=num_return_sequences,
                                 repetition_penalty=5.0)
    outputs = [strip_html(tokenizer.decode(o)) for o in text_outputs]
    return "\n".join(outputs)


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other_columns = ['round_date', 'prior_funding']


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train.sample(1)[other_columns].T.reset_index().values


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print(train.query("investee == 'NOSH'")['title'].head(1).T)
train.query("investee == 'NOSH'")[train_columns].head(1).T


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fake_data = {
   "round_amount":1000000.0,
   "round_date":"2018-09-26",
   "stage":"Pre-series A",
   "investee":"NOSH",
   "investee_description":"NOSH makes and delivers ready-to-eat meals in Hong Kong.",
   "investee_country":"Hong Kong",
   "investee_region":"Asia",
   "investee_subregion":"Eastern Asia",
   "investee_vertical":"Food tech",
   "investee_industry":"Restaurants & Food",
   "investor_list":["Alibaba Entrepreneurs Fund (阿里巴巴创业者基金)"],
   "previous_investors":"",
   "prior_funding":1000000.0
}


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pd.DataFrame([fake_data]).T


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demo = gr.Blocks()

random_sample = train[train_columns].sample(1)
random_sample = pd.DataFrame([fake_data])

stage = gr.Dropdown(label="stage", choices=list(train[train_columns].stage.unique()))
investee_country = gr.Dropdown(label="investee_country", choices=list(train[train_columns].investee_country.unique()),
                              value=random_sample.investee_country.values[0])
investee_subregion = gr.Dropdown(label="investee_subregion", choices=list(train[train_columns].investee_subregion.unique()),
                              value=random_sample.investee_subregion.values[0])
investee_region = gr.Dropdown(label="investee_region", choices=list(train[train_columns].investee_region.unique()),
                             value=random_sample.investee_region.values[0])
investee_vertical = gr.Dropdown(label="investee_vertical", choices=list(train[train_columns].investee_vertical.unique()),
                            value=random_sample.investee_vertical.values[0])
investee_industry = gr.Dropdown(label="investee_industry", choices=list(train[train_columns].investee_industry.unique()),
                            value=random_sample.investee_industry.values[0])

if pd.isnull(random_sample.round_amount.values[0]):
    rand_amount = 0
else:
    rand_amount = random_sample.round_amount.values[0]

round_amount = gr.Slider(label="round_amount", minimum=100000, maximum=200000000, 
                         value=rand_amount, 
                         step=100000)

investee = gr.Textbox(label="investee", value=random_sample.investee.values[0])
investee_description = gr.Textbox(label="investee_description", 
           value=random_sample.investee_description.values[0])
investor_list = gr.Textbox(label="investor_list", 
           value=random_sample.investor_list.values[0])
previous_investors = gr.Textbox(label="previous_investors", 
           value=random_sample.previous_investors.values[0])  
other_values = gr.Dataframe(
    headers=['key', 'value'],
    value=[['round_date', random_sample.round_date.values[0]],
   ['prior_funding', random_sample.prior_funding.values[0]]]
)
out = gr.Textbox(max_lines=num_return_sequences)

with demo:
    gr.Markdown("Enter funding data to generate news headline.")
   
    inputs=[stage, investee_country, investee_subregion, investee_region, 
                      investee_vertical, investee_industry, 
                      round_amount, investee, investee_description, investor_list, previous_investors,
                      other_values]
    
    investee_industry.change(fn=update_industry, inputs=investee_industry, outputs=investee_vertical)
    investee_vertical.change(fn=update_vertical, inputs=investee_vertical, outputs=investee_industry)
    gr.Interface(fn=generate_headline, inputs=inputs, outputs=out, live=True)
    description="Enter funding data to generate news headline.",
    live=True
    
demo.launch()