Delete app (1).py

app (1).py

@@ -1,81 +0,0 @@
-import pandas as pd
-from sklearn.model_selection import train_test_split
-from sklearn.feature_extraction.text import CountVectorizer
-from sklearn.neighbors import KNeighborsClassifier
-from sklearn.linear_model import LogisticRegression
-import joblib
-import gradio as gr
-
-# Load the dataset
-data_df = pd.read_csv('homework01_text_data_group13.csv')
-
-# Separate features and labels
-X = data_df['reviews']
-y = data_df['class']
-
-# Split the data into train and test sets
-X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.33, random_state=42)
-
-# Create bag-of-words representations
-vectorizer = CountVectorizer()
-X_train_counts = vectorizer.fit_transform(X_train)
-X_test_counts = vectorizer.transform(X_test)
-
-# Train the KNN model
-knn_model = KNeighborsClassifier(n_neighbors=2, metric='euclidean')
-knn_model.fit(X_train_counts, y_train)
-
-# Train the Logistic Regression model
-logistic_model = LogisticRegression(penalty='l2', C=1, random_state=0)
-logistic_model.fit(X_train_counts, y_train)
-
-# Save the trained models
-joblib.dump(knn_model, 'best_knn_model.pkl')
-joblib.dump(logistic_model, 'best_logistic_regression_model.pkl')
-
-def predict_knn(review_text, model=knn_model):
-    X_test = vectorizer.transform([review_text])
-    y_pred = model.predict(X_test)
-    y_pred_proba = model.predict_proba(X_test)[0]
-    return {'Positive': y_pred_proba[1], 'Negative': y_pred_proba[0]}
-
-def predict_logistic(review_text, model=logistic_model):
-    X_test = vectorizer.transform([review_text])
-    y_pred = model.predict(X_test)
-    y_pred_proba = model.predict_proba(X_test)[0]
-    return {'Positive': y_pred_proba[1], 'Negative': y_pred_proba[0]}
-
-models = ["KNN", "Logistic Regression"]
-
-def predict(review_text, model):
-    if model == "KNN":
-        output = predict_knn(review_text)
-    else:
-        output = predict_logistic(review_text)
-    
-    if output['Positive'] > output['Negative']:
-        sentiment = "Positive Feedback"
-    else:
-        sentiment = "Negative Feedback"
-    
-    return sentiment, output
-
-demo = gr.Interface(
-    fn=predict,
-    inputs=[
-        gr.Textbox(lines=2, placeholder="Enter your review comment...", label="Review Comment"),
-        gr.Dropdown(choices=models, label="Select Model")
-    ],
-    outputs=[
-        gr.Textbox(label="Predicted Sentiment Class"),
-        gr.Label(num_top_classes=2, label="Predicted Probability")
-    ],
-    examples=[
-        ["This Food is interesting, I need a second Plate", "KNN"],
-        ["This Food is interesting, I need a second Plate", "Logistic Regression"],
-        ["The food was terrible, and the service was worse.", "KNN"],
-        ["The food was terrible, and the service was worse.", "Logistic Regression"]
-    ]
-)
-
-demo.launch()