#!/usr/bin/env python
# coding: utf-8

# In[13]:


# Loading the libaries.
import pandas as pd
import numpy as np
import tensorflow as tf
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.sequence import pad_sequences

# Loading the new CSV file that includes BERT embeddings into a pandas dataframe.
file_path = 'bert_embeddings_tweets.csv'
data = pd.read_csv(file_path)

# Converting the "embeddings" column to a numeric float format to numpy array
embeddings = np.array([np.fromstring(x.strip('[]'), dtype=float, sep=' ') for x in data['embeddings']])
# Normalizing the embeddings column by subtracting the mean of the embeddings column from each value and then dividing by the standard deviation of the embeddings column.
embeddings = (embeddings - np.mean(embeddings, axis=0)) / np.std(embeddings, axis=0) 
# Converting the "choose_one" column to a numeric with "Relevant" as 0 and "Not Relevant" as 1.
labels = np.array([0 if x == "Relevant" else 1 for x in data['choose_one']])

# Spliting the data into training, validation, and test sets
x_train, x_val_test, y_train, y_val_test = train_test_split(embeddings, labels, test_size=0.2)
x_val, x_test, y_val, y_test = train_test_split(x_val_test, y_val_test, test_size=0.5)

# Defining a sequential Keras model with one input layer, one hidden layer with 8 units and relu activation function
model = tf.keras.Sequential([
    tf.keras.layers.Dense(8, activation='relu', input_dim=embeddings.shape[1]),
    tf.keras.layers.Dropout(0.5), # adding Dropout layer to the model to prevent overfitting by randomly dropping out a fraction during training 
    tf.keras.layers.Dense(1, activation='sigmoid')#adding single dense layer with 1 neuron and sigmoid activation function which is useful for binary classification output.
])
# Compiling the model
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])

# Training the model for 10 epochs with applying early stopping.
early_stop = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5) #using an early stopping callback to stop the model training if the validation loss doesn't improve for 5 epochs.
history = model.fit(x_train, y_train, validation_data=(x_val, y_val), epochs=10, batch_size=8, callbacks=[early_stop])#training the model using an epoch 10 and batch size of 8, and adding the early stopping callback.

# Evaluating the model
test_loss, test_acc = model.evaluate(x_test, y_test)
train_loss, train_acc = model.evaluate(x_train, y_train)
val_loss, val_acc = model.evaluate(x_val_test, y_val_test)
print("Test Loss:", test_loss)
print("Test Accuracy:", test_acc)
print("Training Accuracy:", train_acc)
print("Validation Accuracy:", val_acc)


# In[40]:


# TRY THE MODEL

from tensorflow.keras.preprocessing.sequence import pad_sequences

# Preprocess the input sentence
input_sentence = input("Write a sentence: ")
input_sentence = tokenizer.encode_plus(
    input_sentence,
    add_special_tokens=True,
    max_length=768,
    padding="longest",
    truncation=True,
    return_attention_mask=True,
    return_tensors="tf",
)

# Pad the input sequence
input_ids = pad_sequences(
    input_sentence["input_ids"],
    maxlen=768,
    dtype="float32",
    value=0,
    truncating="post",
    padding="post",
)

# Make the prediction
prediction = model.predict(input_ids)[0][0]

# Convert the prediction to a label
label = "Relevant" if prediction == 0 else "Not Relevant"

print("Input Sentence:", input_sentence)
print("Prediction:", label)



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