| import os |
| import pickle |
| import numpy as np |
| import tensorflow as tf |
| from tensorflow.keras.preprocessing.sequence import pad_sequences |
| from tensorflow.keras.models import Sequential |
| from tensorflow.keras.layers import Input, Embedding, LSTM, Bidirectional, GlobalAveragePooling1D, Dense, Dropout |
| import gradio as gr |
|
|
|
|
| MAX_LEN = 200 |
| MODEL_PATH = "sentiment_model.keras" |
| TOKENIZER_PATH = "tokenizer.pickle" |
|
|
|
|
|
|
|
|
| if os.path.exists(TOKENIZER_PATH): |
| with open(TOKENIZER_PATH, "rb") as handle: |
| tokenizer = pickle.load(handle) |
| print("Tokenizer loaded successfully.") |
| else: |
| raise FileNotFoundError(f"Missing critical asset: {TOKENIZER_PATH}") |
|
|
|
|
|
|
|
|
| def build_model(): |
| |
| model=Sequential() |
| model.add(Input(shape=(200,))) |
| model.add(Embedding(5000,128)) |
| model.add(LSTM(128,dropout=0.2)) |
| model.add(Dense(1,activation='sigmoid')) |
| return model |
|
|
| model = build_model() |
|
|
|
|
| model.load_weights("sentiment_weights.weights.h5") |
|
|
|
|
|
|
| def analyze_text(text): |
| |
| if not text or not text.strip(): |
| return {"Invalid Input: Please type some text.": 1.0} |
| |
| |
| sequences = tokenizer.texts_to_sequences([text]) |
| padded = pad_sequences(sequences, maxlen=MAX_LEN) |
| |
| |
| prediction = float(model.predict(padded)[0][0]) |
| |
| |
| return { |
| "Positive 😊": prediction, |
| "Negative 😞": 1.0 - prediction |
| } |
|
|
| |
|
|
|
|
| with gr.Blocks(theme=gr.themes.Soft(), title="Sentiment Analyzer Pro") as demo: |
| |
| |
| gr.Markdown( |
| """ |
| # 🧠 Deep Learning Sentiment Classifier |
| Analyze the emotional polarity of text using an optimized **Bidirectional LSTM** recurrent neural network. |
| """ |
| ) |
| |
| |
| with gr.Row(): |
| |
| with gr.Column(scale=2): |
| input_text = gr.Textbox( |
| label="Input Review / Text Passage", |
| placeholder="Type or paste your text here (e.g., product review, tweet, movie feedback)...", |
| lines=6, |
| max_lines=10, |
| interactive=True |
| ) |
| |
| with gr.Row(): |
| clear_btn = gr.Button("Clear", variant="secondary") |
| submit_btn = gr.Button("Analyze Sentiment", variant="primary") |
| |
| |
| with gr.Column(scale=1): |
| output_label = gr.Label( |
| label="Classification Output", |
| num_top_classes=2, |
| show_label=True |
| ) |
| |
| |
| gr.Examples( |
| examples=[ |
| ["The cinematic direction was absolutely breathtaking, and the cast delivered a masterful performance. Highly recommended!"], |
| ["An absolute disaster. The pacing was agonizingly slow, the dialogue felt completely forced, and I walked out early."], |
| ["The device has decent battery life and a nice screen, but the software bugs make it frustrating to use daily."] |
| ], |
| inputs=input_text, |
| label="Click an example to test instantly:" |
| ) |
| |
| |
| submit_btn.click( |
| fn=analyze_text, |
| inputs=input_text, |
| outputs=output_label |
| ) |
|
|
| input_text.submit( |
| fn=analyze_text, |
| inputs=input_text, |
| outputs=output_label |
| ) |
|
|
| clear_btn.click( |
| fn=lambda: ("", None), |
| inputs=None, |
| outputs=[input_text, output_label] |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| |
| demo.launch() |
