Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import pickle
+from tensorflow.keras.preprocessing.sequence import pad_sequences
+
+# === Load Classifier ===
+classifier_model = tf.keras.models.load_model("classifier_model.keras")
+with open("classifier_tokenizer.pkl", "rb") as f:
+    classifier_tokenizer = pickle.load(f)
+
+# === Load Text Generator ===
+textgen_model = tf.keras.models.load_model("textgen_model.keras")
+with open("textgen_tokenizer.pkl", "rb") as f:
+    textgen_tokenizer = pickle.load(f)
+
+# === Settings ===
+CLASS_LABELS = ['label_0', 'label_1', 'label_2']  # Replace with actual labels
+MAX_LEN_CLASSIFIER = 100
+MAX_LEN_TEXTGEN = 50
+
+
+# === Classifier Inference ===
+def classify_text(text):
+    seq = classifier_tokenizer.texts_to_sequences([text])
+    padded = pad_sequences(seq, maxlen=MAX_LEN_CLASSIFIER)
+    preds = classifier_model.predict(padded, verbose=0)[0]
+    return {CLASS_LABELS[i]: float(preds[i]) for i in range(len(CLASS_LABELS))}
+
+
+# === Text Generation with Top-k Sampling ===
+def generate_text(seed_text, next_words=30, k=10, temperature=0.9):
+    recent_words = set()
+    for _ in range(next_words):
+        token_list = textgen_tokenizer.texts_to_sequences([seed_text])[0]
+        token_list = pad_sequences([token_list], maxlen=MAX_LEN_TEXTGEN-1, padding='pre')
+        predicted_probs = textgen_model.predict(token_list, verbose=0)[0]
+
+        scaled_probs = np.log(predicted_probs + 1e-10) / temperature
+        exp_probs = np.exp(scaled_probs)
+        normalized_probs = exp_probs / np.sum(exp_probs)
+
+        top_k_indices = np.argsort(normalized_probs)[-k:]
+        top_k_probs = normalized_probs[top_k_indices]
+        top_k_probs = top_k_probs / np.sum(top_k_probs)
+
+        predicted_word_index = np.random.choice(top_k_indices, p=top_k_probs)
+
+        # Avoid repetition
+        attempts = 0
+        while predicted_word_index in recent_words and attempts < 5:
+            predicted_word_index = np.random.choice(top_k_indices, p=top_k_probs)
+            attempts += 1
+
+        recent_words.add(predicted_word_index)
+        if len(recent_words) > 10:
+            recent_words.pop()
+
+        output_word = ""
+        for word, index in textgen_tokenizer.word_index.items():
+            if index == predicted_word_index:
+                output_word = word
+                break
+
+        seed_text += " " + output_word
+
+    return seed_text
+
+
+# === Gradio UI ===
+with gr.Blocks() as demo:
+    gr.Markdown("## Dual Model: Text Classifier + Text Generator")
+
+    with gr.Tab("Text Classification"):
+        input_text = gr.Textbox(label="Enter Text", lines=4)
+        classify_btn = gr.Button("Classify")
+        output_label = gr.Label()
+        classify_btn.click(fn=classify_text, inputs=input_text, outputs=output_label)
+
+    with gr.Tab("Text Generation"):
+        seed_text = gr.Textbox(label="Seed Text", lines=4)
+        num_words = gr.Slider(5, 100, value=30, label="Words to Generate")
+        k_top = gr.Slider(1, 50, value=10, step=1, label="Top-k Sampling")
+        temp = gr.Slider(0.5, 1.5, value=0.9, label="Temperature")
+        generate_btn = gr.Button("Generate")
+        gen_output = gr.Textbox(label="Generated Text", lines=6)
+        generate_btn.click(fn=generate_text, inputs=[seed_text, num_words, k_top, temp], outputs=gen_output)
+
+demo.launch()