Added all files

app.py

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+import joblib
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+from sklearn.feature_extraction.text import CountVectorizer
+import gradio as gr
+
+count_vectorizer = joblib.load("count_vectorizer.joblib")
+best_logistic_model = joblib.load("logistic_regression_model.joblib")
+best_knn_model = joblib.load("knn_model.joblib")
+
+knn_test_accuracy =0.635
+logistic_test_accuracy = 0.735
+
+def preprocess_text(text):
+    tokens = word_tokenize(text)
+    tokens = [word for word in tokens if word.isalnum()]
+    tokens = [word.lower() for word in tokens]
+    stop_words = set(stopwords.words("english"))
+    tokens = [word for word in tokens if word not in stop_words]
+    lemmatizer = WordNetLemmatizer()
+    tokens = [lemmatizer.lemmatize(word) for word in tokens]
+    preprocessed_text = " ".join(tokens)
+    transformed_text = count_vectorizer.transform([preprocessed_text])
+    return transformed_text
+
+def predict_sentiment(text, model_name):
+    preprocessed_text = preprocess_text(text)
+    if model_name == "Logistic Regression":
+        prediction = best_logistic_model.predict(preprocessed_text)[0]
+        accuracy = logistic_test_accuracy
+
+    elif model_name == "K-nearest Neighbors":
+        prediction = best_knn_model.predict(preprocessed_text)[0]
+        accuracy = knn_test_accuracy
+    else:
+        return "Invalid model selection", None
+    if prediction==1:
+      sentiment="Positive"
+      pos_accuracy=accuracy
+      neg_accuracy=1-pos_accuracy
+    else:
+      sentiment="Negative"
+      neg_accuracy=accuracy
+      pos_accuracy=1-neg_accuracy
+    accuracy_dict = {"Positive": pos_accuracy, "Negative": neg_accuracy}
+    
+    return accuracy_dict
+examples = [["Point your finger at any item on the menu, order it and you won't be disappointed","K-nearest Neighbors"], ["Similarly, the delivery man did not say a word of apology when our food was 45 minutes late.","Logistic Regression"],["I vomited in the bathroom mid lunch.","K-nearest Neighbors"]]
+
+
+interface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=[gr.Textbox(lines=7, label="Input Text"),
+            gr.Dropdown(["Logistic Regression", "K-nearest Neighbors"], label="Select Model")],
+    outputs=gr.Label(dict),
+    title="Sentiment Prediction",
+    description="Select a model and enter a text to predict the sentiment (positive or negative) and show the accuracy score.",
+    examples=examples
+)
+interface.launch()

count_vectorizer.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:09392663edcf224e458e08526e637a68c1ade848b35ca2df8fed9df264d8fee4
+size 19497

knn_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:60aa9ce605f7c6ededde4406460c7393ed9efcbde01575b287d69ba8a554ed44
+size 62820

logistic_regression_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:068209aa656a54ddfdefa54733c7c3c5a3b6b2355db93ad309b39b5aff7ba241
+size 13583

requirements.txt

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+pandas
+numpy
+seaborn
+wordcloud
+nltk
+scikit-learn==1.2.2
+joblib
+gradio