Create app.py

app.py

@@ -0,0 +1,116 @@
+import tensorflow as tf
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.text import Tokenizer
+from tensorflow.keras import backend as K
+import pandas as pd
+import numpy as np
+import re  
+import gradio as gr
+
+# Function to clean text
+def clean_text(text):
+    text = text.lower()
+    text = re.sub(r"[^a-zA-Zñḳḍāī\s]", "", text)
+    text = re.sub(r'(\n)(\S)', r'\1 \2', text)
+    return text
+
+# Load the dataset
+df = pd.read_csv('Roman-Urdu-Poetry.csv')
+df['Poetry'] = df['Poetry'].apply(clean_text)
+
+# Tokenization
+tokenizer = Tokenizer(num_words=5000, filters='')
+tokenizer.fit_on_texts(df['Poetry'])
+sequences = tokenizer.texts_to_sequences(df['Poetry'])
+
+max_sequence_len = max([len(seq) for seq in sequences])  
+max_sequence_len = min(max_sequence_len, 225) 
+padded_sequences = pad_sequences(sequences, maxlen=max_sequence_len, padding='pre')
+K.clear_session()
+
+input_sequences = []
+output_words = []
+
+for seq in padded_sequences:
+    for i in range(1, len(seq)):
+        input_sequences.append(seq[:i])
+        output_words.append(seq[i])
+
+input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_len, padding='pre')
+output_words = np.array(output_words)
+total_words = len(tokenizer.word_index) + 1
+
+
+# Load the trained model
+model = load_model('poetry_model.h5')
+
+# Function to generate poetry
+def generate_poem(seed_text, next_words, temperature):
+    for _ in range(next_words):
+        token_list = tokenizer.texts_to_sequences([seed_text])[0]
+        token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding='pre')
+
+        predictions = model.predict(token_list, verbose=0)[0]
+        
+        # Apply temperature scaling
+        predictions = np.log(predictions + 1e-10) / temperature
+        exp_preds = np.exp(predictions)
+        predictions = exp_preds / np.sum(exp_preds)
+
+        # Sample the next word
+        predicted_word_index = np.random.choice(len(predictions), p=predictions)
+        predicted_word = tokenizer.index_word.get(predicted_word_index, '')
+
+        if predicted_word:
+            seed_text += " " + predicted_word  
+
+    return seed_text
+
+# Custom CSS Styling
+custom_css = """
+body {
+    background-color: #121212;
+    color: white;
+    font-family: 'Arial', sans-serif;
+}
+.gradio-container {
+    max-width: 600px;
+    margin: auto;
+    text-align: center;
+}
+textarea, input, button {
+    font-size: 16px !important;
+}
+button {
+    background: #ff5c5c !important;
+    color: white !important;
+    padding: 12px 18px !important;
+    border-radius: 8px !important;
+    font-weight: bold;
+    border: none !important;
+    cursor: pointer;
+}
+button:hover {
+    background: #e74c3c !important;
+}
+"""
+
+# Gradio Interface
+with gr.Blocks(css=custom_css) as iface:
+    gr.Markdown("<h1 style='text-align: center;'>🎶 Verse Hub: Poetry Generator 🎶</h1>")
+    
+
+    seed_text = gr.Textbox(label="Verse Hub", placeholder="Start your poetry...", interactive=True)
+
+    with gr.Row():
+        words = gr.Slider(minimum=5, maximum=100, step=1, value=10, label="Number of Words", interactive=True)
+        temperature = gr.Slider(minimum=0.5, maximum=2.0, step=0.1, value=1.0, label="Temperature", interactive=True)
+
+    generate_button = gr.Button("✨ Generate Poetry 🎤")
+    output_text = gr.Textbox(label="Generated Poem", interactive=False, lines=6)
+
+    generate_button.click(fn=generate_poem, inputs=[seed_text, words, temperature], outputs=output_text)
+
+# Launch Gradio App
+iface.launch()