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from fastapi import FastAPI
import asyncio
import gradio as gr
import re
import string
import nltk
nltk.download('punkt')
nltk.download('wordnet')
nltk.download('omw-1.4')
from nltk.stem import WordNetLemmatizer
import pickle
# Function to remove URLs from text
def remove_urls(text):
return re.sub(r'http[s]?://\S+', '', text)
# Function to remove punctuations from text
def remove_punctuation(text):
regular_punct = string.punctuation
return str(re.sub(r'['+regular_punct+']', '', str(text)))
# Function to convert the text into lower case
def lower_case(text):
return text.lower()
# Function to lemmatize text
def lemmatize(text):
wordnet_lemmatizer = WordNetLemmatizer()
tokens = nltk.word_tokenize(text)
lemma_txt = ''
for w in tokens:
lemma_txt = lemma_txt + wordnet_lemmatizer.lemmatize(w) + ' '
return lemma_txt
def load_model():
# Define the file path where the trained model is saved
model_file_path = "logistic_regression_model.pkl"
# Load the saved Logistic Regression model from the file
with open(model_file_path, 'rb') as file:
loaded_model = pickle.load(file)
return loaded_model
def load_tfidf():
# Define the file path where the TF-IDF vectorizer is saved
vectorizer_file_path = "tfidf_vectorizer.pkl"
# Load the saved TF-IDF vectorizer from the file
with open(vectorizer_file_path, 'rb') as file:
loaded_vectorizer = pickle.load(file)
return loaded_vectorizer
def preprocess(input_text):
# Preprocess the input text
input_text = remove_urls(input_text)
input_text = remove_punctuation(input_text)
input_text = lower_case(input_text)
input_text = lemmatize(input_text)
# Apply TF-IDF vectorization
input_text = [input_text]
tfidf = load_tfidf()
input_text = tfidf.transform(input_text)
return input_text
app = FastAPI()
@app.get('/')
async def welcome():
return "Welcome to our Sentiment Analysis API"
@app.post('/predict_sentiment')
async def predict_sentiment(input_text):
loaded_model = load_model()
predicted_sentiment = loaded_model.predict(preprocess(input_text))
if predicted_sentiment == 0:
sentiment = "Sentiment: Negative"
else:
sentiment = "Sentiment: Positive"
return sentiment
async def predict(input):
sentiment = await predict_sentiment(input)
return sentiment
# Create Gradio interface
iface = gr.Interface(fn=predict, inputs="text", outputs="text", title="Movie Review Sentiment Analysis API", description="Enter a review to know its sentiment...")
iface.launch(share=True)
asyncio.run(predict()) |