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app.py

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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")
+iface.launch(share=True)
+
+asyncio.run(predict())

requirements.txt

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+fastapi==0.110.0
+gradio==4.23.0
+nltk==3.8.1