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app.py

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+import gradio as gr
+import numpy as np
+from tensorflow.keras.models import load_model
+import re 
+#importing skearn and joblib modules
+import joblib
+import sklearn
+
+# setting the joblib 
+sklearn.externals.joblib = joblib# Load the models
+
+lr_model = joblib.load("logistic_model.pkl")
+svm_model = joblib.load("svm_model.pkl")
+nn_model = load_model("dl_model.h5")
+mnb_model = joblib.load("mnb_model.pkl")
+
+TAG_RE = re.compile(r'<[^>]+>')
+
+def remove_tags(text):
+    return TAG_RE.sub('', text)
+
+# Load other necessary files like vectorizers or scalers
+tfidf_vectorizer = joblib.load("tfidf_vectorizer.pkl")
+
+def preprocess_text(sen):
+    # Removing html tags
+    sentence = remove_tags(sen)
+
+    # Remove punctuations and numbers
+    sentence = re.sub('[^a-zA-Z]', ' ', sentence)
+
+    # Single character removal
+    sentence = re.sub(r"\s+[a-zA-Z]\s+", ' ', sentence)
+
+    # Removing multiple spaces
+    sentence = re.sub(r'\s+', ' ', sentence)
+
+    return sentence
+
+# Function to predict sentiment using Logistic Regression
+# Function to predict sentiment using Logistic Regression
+def predict_lr(text):
+    preprocessed_text = preprocess_text(text)
+    vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(lr_model.predict(dense_vectorized_text)[0])
+    return prediction
+
+
+# Function to predict sentiment using SVM
+def predict_svm(text):
+    preprocessed_text = preprocess_text(text)
+    vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(svm_model.predict(dense_vectorized_text)[0])
+    return prediction
+
+# Function to predict sentiment using Neural Network
+def predict_nn(text):
+    preprocessed_text = preprocess_text(text)
+    vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+
+    prediction_probs = nn_model.predict(dense_vectorized_text)[0]
+    prediction = int(np.argmax(prediction_probs))
+    return prediction
+
+# Function to predict sentiment using Multinomial Naive Bayes
+def predict_mnb(text):
+    preprocessed_text = preprocess_text(text)
+    vectorized_text = tfidf_vectorizer.transform([preprocessed_text])
+    dense_vectorized_text = vectorized_text.toarray()  # Convert to dense array
+    prediction = int(mnb_model.predict(dense_vectorized_text)[0])
+    return prediction
+
+# Set the function based on the selected model
+def sentiment_prediction(text, model):
+    if model == "Logistic Regression":
+        prediction = predict_lr(text)
+    elif model == "SVM":
+        prediction = predict_svm(text)
+    elif model == "Neural Network":
+        prediction = predict_nn(text)
+    elif model == "Multinomial Naive Bayes":
+        prediction = predict_mnb(text)
+    threshold = 0.5
+
+    # Logic for determining positive or negative sentiment based on the model's prediction
+    if prediction >= threshold:
+        return "Positive"
+    else:
+        return "Negative"
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=sentiment_prediction,
+    inputs=[gr.Textbox(type="text", label="Enter Text"), gr.Dropdown(["Logistic Regression", "SVM", "Neural Network", "Multinomial Naive Bayes"], label="Select Model")],
+    outputs=gr.Label(),
+    live=True,
+    title="Sentiment Analysis with Model Selection",
+    description="Enter a text and choose a model for sentiment prediction.",
+)
+
+# Launch the Gradio interface
+iface.launch()
+
+
+iface.launch()

dl_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:25b02d3dddb9af940fb51c0b2496a1e5d06ce76041fe0ea337aeba17705bd189
+size 795072

logistic_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5453e622e1018c776ce9f829efe6a8d705f635920c8d07acc67d932c0b9f7834
+size 19642619

mnb_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5453e622e1018c776ce9f829efe6a8d705f635920c8d07acc67d932c0b9f7834
+size 19642619

requirements.txt

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+gradio 
+scikit-learn==1.2.2
+numpy
+nltk
+tensorflow==2.15.0
+joblib==1.3.2

svm_model.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:5453e622e1018c776ce9f829efe6a8d705f635920c8d07acc67d932c0b9f7834
+size 19642619

tfidf_vectorizer.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1189be4fec99140b4e536076a439f6d71a8dde51ca8071676e78bda39fa01331
+size 1122210