Update app.py

app.py

@@ -1,4 +1,3 @@
-!pip install tensorflow
 import gradio as gr
 import numpy as np
 from tensorflow.keras.models import load_model, save_model
@@ -8,22 +7,20 @@ from tensorflow.keras.utils import to_categorical
 import json
 
 # Load the pre-trained model and tokenizer
-model = load_model("code_language_cnn.keras")  # Replace with your model path
+model = load_model("code_language_cnn.keras")  
 with open("tokenizer.json", "r") as f:
-    tokenizer_data = f.read()  # Read the JSON file as a string
+    tokenizer_data = f.read()  
 tokenizer = tokenizer_from_json(tokenizer_data)
 
-max_sequence_length = 500  # Adjust based on your model
-languages = ["C", "C++", "JAVA", "Python"]  # Replace with your language labels
+max_sequence_length = 500  
+languages = ["C", "C++", "JAVA", "Python"]
 
-# Load or initialize feedback data
 try:
     with open("feedback.json", "r") as f:
         feedback_data = json.load(f)
 except FileNotFoundError:
     feedback_data = []
 
-# Define the prediction function
 def predict_language(code_snippet):
     seq = tokenizer.texts_to_sequences([code_snippet])
     padded_seq = pad_sequences(seq, maxlen=max_sequence_length, padding='post', truncating='post')
@@ -32,7 +29,6 @@ def predict_language(code_snippet):
     predicted_language = languages[np.argmax(predictions)]
     return predicted_language, confidence_scores
 
-# Feedback handling function
 def provide_feedback(code_snippet, predicted_language, feedback, correct_language=None):
     global feedback_data
 
@@ -48,13 +44,11 @@ def provide_feedback(code_snippet, predicted_language, feedback, correct_languag
     with open("feedback.json", "w") as f:
         json.dump(feedback_data, f, indent=4)
 
-    # If feedback is "Incorrect", retrain the model
     if feedback == "Incorrect":
         retrain_model(code_snippet, correct_language)
 
     return "Thank you for your feedback!"
 
-# Retraining the model based on feedback
 def retrain_model():
     global model
     # Prepare the feedback data (new training data)