Create main.py

main.py

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+import gradio as gr
+import joblib
+from gensim.models import Word2Vec
+import numpy as np
+
+# Load the models
+classifier = joblib.load("random_forest_model.pkl")
+word2vec_model = Word2Vec.load("word2vec_model.bin")
+label_encoder = joblib.load("label_encoder.pkl")
+
+def predict_comment(comment):
+    tokenized_comment = comment.split()
+    comment_vector = get_average_word2vec(tokenized_comment, word2vec_model, 100)
+    comment_vector = comment_vector.reshape(1, -1)
+    prediction = classifier.predict(comment_vector)
+    return "Based on Experience" if label_encoder.inverse_transform(prediction)[0] == 1 else "Not Based on Experience"
+
+def get_average_word2vec(comment, model, num_features):
+    feature_vec = np.zeros((num_features,), dtype="float32")
+    n_words = 0
+    for word in comment:
+        if word in model.wv.key_to_index:
+            n_words += 1
+            feature_vec = np.add(feature_vec, model.wv[word])
+    if n_words > 0:
+        feature_vec = np.divide(feature_vec, n_words)
+    return feature_vec
+
+# Gradio interface
+iface = gr.Interface(fn=predict_comment, inputs="text", outputs="text")
+iface.launch()