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# -*- coding: utf-8 -*-
"""FA20-BCS-OO1 final app.ipynb
Automatically generated by Colab
"""
# !pip install emoji gradio
import joblib, pickle, pandas as pd, numpy as np
import gradio as gr
from TweetNormalizer import normalizeTweet
import seaborn as sns
import matplotlib.pyplot as plt
from transformers import pipeline
# seek007/taskA-DeBERTa-bweet-1.2.5
# seek007/taskA-DeBERTa-large-1.0.0
# seek007/taskA-DeBERTa-bweet-1.1.0
pipe= pipeline(model="seek007/taskA-DeBERTa-large-1.0.0",tokenizer='seek007/taskA-DeBERTa-large-1.0.0')
# pipe = joblib.load('/content/drive/MyDrive/FYPpkl models/pipeA-wTok-0.0.1.pkl')
import numpy as np
def predict(text=None , fil=None):
# Preprocess the text
preprocessed_text = normalizeTweet(text)
sentiment =None
df=None
fig=None
if fil:
if fil.name.endswith('.csv'):
df = pd.read_csv(fil.name)
elif fil.name.endswith('.xlsx') or fil.name.endswith('.xls'):
df = pd.read_excel(fil.name)
else:
raise ValueError("Unsupported file type. Please upload a CSV or Excel file.")
# df= df.sample(20)
lst = list(df.tweet)
m =[normalizeTweet(i) for i in lst]
# m = [truncate_string(i) for i in m]
d = pd.DataFrame(pipe.predict(m))
df['label'] = d['label']
# print(df.sample(5))
df.drop('sarcastic', axis=1, inplace=True)
# print(df.sample(5))
mapping = {
'LABEL_0': 'non_sarcastic',
'LABEL_1': 'sarcastic'
}
# df['label']=df['label'].map(mapping)
sarcastic_count = np.sum(df.label =='sarcastic')
non_sarcastic_count = np.sum(df.label =='non_sarcastic')
labels = ['Sarcastic', 'Non-Sarcastic']
sizes = [sarcastic_count, non_sarcastic_count]
colors = ['gold', 'lightblue']
explode = (0.1, 0) # explode 1st slice
sns.set_style("whitegrid")
fig, ax = plt.subplots()
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%', shadow=True, startangle=140) #, colors=colors
ax.axis('equal') # Equal aspect ratio ensures that pie is drawn as a circle.
plt.title('Sarcastic vs Non-Sarcastic Tweets')
# fig = plt.figure() #figsize=(8, 6)
# sns.countplot(x='label', data=df, palette='viridis')
# plt.title('Result: Count Plot') # Add a title to the plot
# plt.xlabel('label') # Add label for the x-axis
# plt.ylabel('Count')
# Perform sentiment prediction
if text !="" or fil !=None:
prediction = pipe.predict([preprocessed_text])[0]
print(prediction)
# sentiment = {p['label']: p['score'] for p in prediction}
# sentiment['']
# print(sentiment)
sentiment = "Sarcastic" if (prediction['label'] == 'LABEL_1' or prediction['label'] =='sarcastic') else "Non Sarcastic"
if fil == None:
df= pd.DataFrame([{'tweet':text, 'label':sentiment}])
else:
return "Either enter text or upload .csv or .xlsx file.!" , df, fig
return sentiment, df, fig
file_path =gr.File(label="Upload a File")
output = gr.Label(num_top_classes=2, label="Predicted Labels")
demo = gr.Interface(fn=predict, inputs=[gr.Text(label="Input"),file_path], outputs=[output, gr.DataFrame(headers =['Tweets', 'Labels'], wrap=True), gr.Plot(label="Sarcasm Predictor")], title="Sarcasm Predictor")
# demo.launch(debug=True)
file_path =gr.File(label="Upload a File")
label = gr.Label(num_top_classes=3, label="Top 3 Labels")
classification = gr.Interface(classify, inputs=[gr.Text(label="Input"),file_path], outputs= [label, gr.DataFrame(headers =['Tweets', 'Label', "Score"], wrap=True), gr.Plot(label="Sarcasm classifier")], title="Sarcasm Classifier")
# classification.launch(debug=True)
from transformers import pipeline
pipe2 = pipeline(model="seek007/taskB-bertweet-base-trainer-1.0.0", tokenizer="seek007/taskB-bertweet-base-trainer-1.0.0")
def classifyB(text=None , fil=None):
# Preprocess the text
preprocessed_text = normalizeTweet(text)
sentiment =None
df=None
fig=None
labels = ['sarcasm', 'irony','Staire', 'understatement','overstatement', 'rhetorical question']
if fil:
if fil.name.endswith('.csv'):
df = pd.read_csv(fil.name)
elif fil.name.endswith('.xlsx') or fil.name.endswith('.xls'):
df = pd.read_excel(fil.name)
else:
raise ValueError("Unsupported file type. Please upload a CSV or Excel file.")
lst = list(df.tweet)
m =[normalizeTweet(i) for i in lst]
# m = [truncate_string(i) for i in m]
d = pipe2(m)
structured_data = []
# Iterate over the list of dictionaries and convert each to a structured dictionary
for item in d:
labels = item['label']
scores = item['score']
structured_data.append({ "label": labels, "score": scores})
# Convert the list of dictionaries to a DataFrame
df1 = pd.DataFrame(structured_data)
df = pd.concat([df, df1], axis=1)
# df["labels"] = d['labels']
# print("df: ",df.head())
# return df.head()
fig = plt.figure() #figsize=(8, 6)
sns.countplot(x='label', data=df, palette='viridis')
plt.title('Result: Count Plot') # Add a title to the plot
plt.xlabel('label') # Add label for the x-axis
plt.ylabel('Count')
# Perform sentiment prediction
if text !=None or fil !=None:
prediction = pipe2([preprocessed_text])[0]
print(prediction["label"])
labels = prediction['label']
scores = prediction['score']
# Combine labels and scores, and sort by score in descending order
# Extract top 3 labels and their scores
sentiment = labels
return sentiment, df, fig
file_path =gr.File(label="Upload a File")
label = gr.Label( label="Labels")
classificationB = gr.Interface(classifyB, inputs=[gr.Text(label="Input"),file_path], outputs= [label, gr.DataFrame(headers =['Tweets', 'Label', "Score"], wrap=True), gr.Plot(label="Sarcasm classifier")], title="Sarcasm Classifier",theme= 'dark')
main = gr.TabbedInterface([demo, classificationB],['Analysizer', 'Classifier'], title="Sarcasm Predictor: An Optimized Sentiment Analysis system" )
main.launch(share=True) |