updated the changes

app.py

@@ -1,7 +1,112 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import gradio as gr
+import pandas as pd
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+from huggingface_hub import hf_hub_download
+import numpy as np
+import tensorflow as tf
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import torch
-## calling the gpt model
+
+# Paths to the uploaded models
+repo_id = "himanishprak23/lstm_rnn"
+lstm_filename = "model_lstm_4.keras"  
+rnn_filename = "model_rnn_1.keras"
+lstm_model_path = hf_hub_download(repo_id=repo_id, filename=lstm_filename)
+rnn_model_path = hf_hub_download(repo_id=repo_id, filename=rnn_filename)
+
+data_text_path = "/Users/himanishprakash/NLP-Application/code/data_preprocess/df_commentary.csv"
+
+# Load the LSTM model
+lstm_model = load_model(lstm_model_path)
+
+# Load the RNN model
+rnn_model = load_model(rnn_model_path)
+
+# Load the data for tokenizer fitting
+data_text = pd.read_csv(data_text_path)
+
+# Check the embedding layer's input dimension for LSTM
+embedding_layer = lstm_model.layers[0]
+vocab_size = embedding_layer.input_dim
+
+# Initialize and fit the tokenizer with limited vocabulary size
+tokenizer = Tokenizer(num_words=vocab_size)
+tokenizer.fit_on_texts(data_text['Modified_Commentary'])
+
+# Define the maximum sequence length (adjust based on your model training)
+max_sequence_length = 153
+
+# Define the text generation function for LSTM
+def generate_with_lstm(commentary_text, num_words):
+    # Tokenize the input text
+    input_sequence = tokenizer.texts_to_sequences([commentary_text])
+    input_sequence = pad_sequences(input_sequence, maxlen=max_sequence_length)
+    
+    # Convert to tensor
+    input_tensor = tf.convert_to_tensor(input_sequence)
+    
+    # Generate the next words
+    generated_sequence = []
+    for _ in range(num_words):
+        # Get model predictions
+        output = lstm_model.predict(input_tensor)
+        
+        # Get the index of the most probable next word
+        next_word_index = np.argmax(output[0], axis=-1)
+        
+        # Add the predicted word to the sequence
+        generated_sequence.append(next_word_index)
+        
+        # Append the predicted word to the input sequence
+        input_sequence = np.append(input_sequence[0][1:], next_word_index).reshape(1, -1)
+        input_tensor = tf.convert_to_tensor(input_sequence)
+    
+    # Convert indices back to words
+    reverse_word_index = {value: key for key, value in tokenizer.word_index.items() if value < vocab_size}
+    generated_words = [reverse_word_index.get(i, '') for i in generated_sequence]
+    
+    # Combine the input text with the generated words
+    generated_text = commentary_text + ' ' + ' '.join(generated_words)
+    
+    return generated_text
+
+# Define the text generation function for RNN
+def generate_with_rnn(commentary_text, num_words):
+    # Tokenize the input text
+    input_sequence = tokenizer.texts_to_sequences([commentary_text])
+    input_sequence = pad_sequences(input_sequence, maxlen=max_sequence_length)
+    
+    # Convert to tensor
+    input_tensor = tf.convert_to_tensor(input_sequence)
+    
+    # Generate the next words
+    generated_sequence = []
+    for _ in range(num_words):
+        # Get model predictions
+        output = rnn_model.predict(input_tensor)
+        
+        # Get the index of the most probable next word
+        next_word_index = np.argmax(output[0], axis=-1)
+        
+        # Add the predicted word to the sequence
+        generated_sequence.append(next_word_index)
+        
+        # Append the predicted word to the input sequence
+        input_sequence = np.append(input_sequence[0][1:], next_word_index).reshape(1, -1)
+        input_tensor = tf.convert_to_tensor(input_sequence)
+    
+    # Convert indices back to words
+    reverse_word_index = {value: key for key, value in tokenizer.word_index.items() if value < vocab_size}
+    generated_words = [reverse_word_index.get(i, '') for i in generated_sequence]
+    
+    # Combine the input text with the generated words
+    generated_text = commentary_text + ' ' + ' '.join(generated_words)
+    
+    return generated_text
+
+# Load GPT-2 models and tokenizers
 trained_tokenizer = GPT2Tokenizer.from_pretrained("Kumarkishalaya/GPT-2-next-word-prediction")
 trained_model = GPT2LMHeadModel.from_pretrained("Kumarkishalaya/GPT-2-next-word-prediction")
 untrained_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
@@ -15,9 +120,9 @@ untrained_model.to(device)
 trained_tokenizer.pad_token = trained_tokenizer.eos_token
 untrained_tokenizer.pad_token = untrained_tokenizer.eos_token
 
-
-def generate(commentary_text, max_length, temperature):
-# Generate text using the finetuned model
+# Define the text generation function for GPT-2
+def generate_with_gpt2(commentary_text, max_length, temperature):
+    # Generate text using the finetuned model
     inputs = trained_tokenizer(commentary_text, return_tensors="pt", padding=True)
     input_ids = inputs.input_ids.to(device)
     attention_mask = inputs.attention_mask.to(device)
@@ -49,22 +154,32 @@ def generate(commentary_text, max_length, temperature):
     
     return trained_text, untrained_text
 
-# Create Gradio interface
-iface = gr.Interface(
-    fn=generate, 
+# Define the combined function for Gradio interface
+def generate_with_all_models(commentary_text, num_words, max_length, temperature):
+    lstm_output = generate_with_lstm(commentary_text, num_words)
+    rnn_output = generate_with_rnn(commentary_text, num_words)
+    gpt2_finetuned_output, gpt2_base_output = generate_with_gpt2(commentary_text, max_length, temperature)
+    return lstm_output, rnn_output, gpt2_finetuned_output, gpt2_base_output
+
+# Create the Gradio interface
+commentrymodel = gr.Interface(
+    fn=generate_with_all_models,
     inputs=[
-        gr.Textbox(lines=2, placeholder="Enter your prompt here...", label="Prompt"),
-        gr.Slider(minimum=10, maximum=100, value=50, step=1,label="Max Length"),        
-        gr.Slider(minimum=0.01, maximum=1.99, value=0.7, label="Temperature")
-    ], 
+        gr.Textbox(lines=2, placeholder="Enter commentary text here...", label="Prompt"),
+        gr.Slider(minimum=1, maximum=50, step=1, value=10, label="Number of words to predict (LSTM/RNN)"),
+        gr.Slider(minimum=10, maximum=100, value=50, step=1, label="Max Length (GPT-2)"),        
+        gr.Slider(minimum=0.01, maximum=1.99, value=0.7, label="Temperature (GPT-2)")
+    ],
     outputs=[
-        gr.Textbox(label="commentary generation from finetuned GPT2 Model"), 
-        gr.Textbox(label="commentary generation from base GPT2 Model")
+        gr.Textbox(label="LSTM Model Output"),
+        gr.Textbox(label="RNN Model Output"),
+        gr.Textbox(label="GPT-2 Finetuned Model Output"), 
+        gr.Textbox(label="GPT-2 Base Model Output")
     ],
-    title="GPT-2 Text Generation",
-    description="start writing a cricket commentary and GPT-2 will continue it using both a finetuned and base model."
+    title="Text Generation with LSTM, RNN, and GPT-2 Models",
+    description="Start writing a cricket commentary and the models will continue it. Compare outputs from LSTM, RNN, and GPT-2 (finetuned and base) models."
 )
 
 # Launch the app
 if __name__ == "__main__":
-    iface.launch()
+    commentrymodel.launch()