Update app.py

app.py

@@ -1,47 +1,38 @@
-# Import the required libraries
-import nltk
-import spacy
-import tensorflow as tf
-import numpy as np
-from tensorflow.keras.layers import Input, Dense, LSTM, Embedding, Dropout
-from tensorflow.keras.models import Model
-from tensorflow.keras.optimizers import Adam
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-from tensorflow.keras.preprocessing.text import Tokenizer
-
-# Load the language model
-nlp = spacy.load('en_core_web_sm')
-
-# Define the neural network architecture
-num_words = 10000
-embedding_dim = 128
-lstm_units = 128
-dropout_rate = 0.2
-num_classes = 10
-
-input_text = Input(shape=(None,))
-embedding_layer = Embedding(input_dim=num_words, output_dim=embedding_dim)(input_text)
-lstm_layer = LSTM(units=lstm_units)(embedding_layer)
-dropout_layer = Dropout(rate=dropout_rate)(lstm_layer)
-output_layer = Dense(units=num_classes, activation='softmax')(dropout_layer)
-
-# Compile the model
-learning_rate = 0.001
-model = Model(inputs=input_text, outputs=output_layer)
-model.compile(loss='categorical_crossentropy', optimizer=Adam(lr=learning_rate), metrics=['accuracy'])
-
-# Train the model
-batch_size = 32
-num_epochs = 10
-model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=batch_size, epochs=num_epochs)
-
-# Define the function for providing feedback and corrections
-def provide_feedback(code):
-    # Use NLP to analyze code syntax and structure
-    doc = nlp(code)
-    # Use machine learning to classify code errors and suggest corrections
-    # ...
-    # Use deep learning to generate new code that fixes errors
-    # ...
-    # Return the corrected code and feedback to the user
-    return corrected_code, feedback_message
+import openai
+
+# Set up the OpenAI API credentials
+openai.api_key = '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'
+
+# Define a function that takes a user's input code as a prompt and uses the OpenAI API to generate a corrected version of the code
+def correct_code(prompt):
+    # Set up the OpenAI API parameters
+    model_engine = "text-davinci-002"
+    temperature = 0.7
+    max_tokens = 100
+
+    # Generate the corrected code using the OpenAI API
+    response = openai.Completion.create(
+        engine=model_engine,
+        prompt=prompt,
+        temperature=temperature,
+        max_tokens=max_tokens
+    )
+
+    # Extract the corrected code from the API response
+    corrected_code = response.choices[0].text.strip()
+
+    return corrected_code
+
+def main():
+    while True:
+        # Prompt the user for input code
+        prompt = input("Enter code to correct: ")
+
+        # Generate a corrected version of the code using the OpenAI API
+        corrected_code = correct_code(prompt)
+
+        # Print the corrected code
+        print(corrected_code)
+
+if __name__ == '__main__':
+    main()