| from huggingface_hub import login, upload_folder | |
| # (optional) Login with your Hugging Face credentials | |
| login() | |
| # Push your model files | |
| upload_folder(folder_path=r"D:\Python\DEEP LEARNING\New folder\sentiment_analyzer", repo_id="kanha1111/sentiment_analysis", repo_type="model") | |
| # import gradio as gr | |
| # import pickle | |
| # import re | |
| # import string | |
| # import nltk | |
| # import numpy as np | |
| # from nltk.corpus import stopwords | |
| # from nltk.stem import WordNetLemmatizer | |
| # # =============================== | |
| # # DOWNLOAD NLTK FILES | |
| # # =============================== | |
| # nltk.download('stopwords') | |
| # nltk.download('wordnet') | |
| # # =============================== | |
| # # LOAD TFLITE MODEL | |
| # # =============================== | |
| # try: | |
| # import tensorflow as tf | |
| # interpreter = tf.lite.Interpreter(model_path="sentiment_model.tflite") | |
| # interpreter.allocate_tensors() | |
| # input_details = interpreter.get_input_details() | |
| # output_details = interpreter.get_output_details() | |
| # MODEL_LOADED = True | |
| # except Exception as e: | |
| # MODEL_LOADED = False | |
| # MODEL_ERROR = str(e) | |
| # # =============================== | |
| # # LOAD TOKENIZER | |
| # # =============================== | |
| # with open("tokenizer.pkl", "rb") as f: | |
| # tokenizer = pickle.load(f) | |
| # # =============================== | |
| # # NLP TOOLS | |
| # # =============================== | |
| # stop_words = set(stopwords.words('english')) | |
| # lemmatizer = WordNetLemmatizer() | |
| # MAX_LEN = 100 | |
| # # =============================== | |
| # # PAD SEQUENCES (manual) | |
| # # =============================== | |
| # def manual_pad_sequences(sequences, maxlen): | |
| # padded = np.zeros((len(sequences), maxlen), dtype=np.float32) | |
| # for i, seq in enumerate(sequences): | |
| # if len(seq) > maxlen: | |
| # padded[i] = seq[-maxlen:] | |
| # else: | |
| # padded[i, maxlen - len(seq):] = seq | |
| # return padded | |
| # # =============================== | |
| # # CLEAN TEXT | |
| # # =============================== | |
| # def clean_text(text): | |
| # text = text.lower() | |
| # text = re.sub(r'http\S+', '', text) | |
| # text = re.sub(r'@\w+', '', text) | |
| # text = re.sub(r'#', '', text) | |
| # text = re.sub(r'\d+', '', text) | |
| # text = text.translate(str.maketrans('', '', string.punctuation)) | |
| # text = text.strip() | |
| # words = text.split() | |
| # words = [lemmatizer.lemmatize(w) for w in words if w not in stop_words] | |
| # return ' '.join(words) | |
| # # =============================== | |
| # # PREDICT FUNCTION | |
| # # =============================== | |
| # def predict_sentiment(text): | |
| # if not text or text.strip() == "": | |
| # return error_card("Please enter some text to analyze.") | |
| # if not MODEL_LOADED: | |
| # return error_card(f"Model load failed: {MODEL_ERROR}") | |
| # try: | |
| # cleaned = clean_text(text) | |
| # sequence = tokenizer.texts_to_sequences([cleaned]) | |
| # padded = manual_pad_sequences(sequence, MAX_LEN) | |
| # # TFLite model expects batch size 256 - pad input accordingly | |
| # batch_size = input_details[0]['shape'][0] | |
| # full_input = np.zeros((batch_size, MAX_LEN), dtype=np.float32) | |
| # full_input[0] = padded[0] | |
| # interpreter.set_tensor(input_details[0]['index'], full_input) | |
| # interpreter.invoke() | |
| # score = float(interpreter.get_tensor(output_details[0]['index'])[0][0]) | |
| # is_positive = score > 0.5 | |
| # pct = score if is_positive else (1 - score) | |
| # pct_display = f"{pct:.4f}" | |
| # if is_positive: | |
| # sentiment_text = "Positive Sentiment" | |
| # sentiment_color = "#22c55e" | |
| # emoji = "😊" | |
| # else: | |
| # sentiment_text = "Negative Sentiment" | |
| # sentiment_color = "#ef4444" | |
| # emoji = "😞" | |
| # return f""" | |
| # <div style=' | |
| # background:#1e2d40; | |
| # border-radius:14px; | |
| # padding:24px 28px; | |
| # margin-top:8px; | |
| # border:1px solid #2d4a6b; | |
| # font-family:Inter,sans-serif; | |
| # '> | |
| # <h3 style='color:#f1f5f9;margin:0 0 16px 0;font-size:20px;font-weight:700;'> | |
| # {emoji} Prediction Result | |
| # </h3> | |
| # <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'> | |
| # <b style='color:#f1f5f9;'>Input Text:</b> {text} | |
| # </p> | |
| # <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'> | |
| # <b style='color:#f1f5f9;'>Cleaned Text:</b> {cleaned} | |
| # </p> | |
| # <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'> | |
| # <b style='color:#f1f5f9;'>Sentiment:</b> | |
| # <span style='color:{sentiment_color};font-weight:700;'>{sentiment_text}</span> | |
| # </p> | |
| # <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'> | |
| # <b style='color:#f1f5f9;'>Confidence Score:</b> | |
| # <span style='color:#60a5fa;font-weight:600;'>{pct_display}</span> | |
| # </p> | |
| # </div> | |
| # """ | |
| # except Exception as e: | |
| # return error_card(f"Prediction error: {str(e)}") | |
| # def error_card(msg): | |
| # return f""" | |
| # <div style='background:#2d1515;border:1px solid #ef4444;border-radius:12px;padding:18px;margin-top:8px;'> | |
| # <p style='color:#fca5a5;margin:0;font-size:14px;'>⚠️ {msg}</p> | |
| # </div> | |
| # """ | |
| # # =============================== | |
| # # CSS | |
| # # =============================== | |
| # css = """ | |
| # body, .gradio-container { | |
| # background: #0f172a !important; | |
| # font-family: Inter, sans-serif !important; | |
| # } | |
| # .gradio-container { | |
| # max-width: 720px !important; | |
| # margin: auto !important; | |
| # padding: 30px 20px !important; | |
| # } | |
| # textarea { | |
| # background: #1e2d40 !important; | |
| # border: 1px solid #2d4a6b !important; | |
| # border-radius: 12px !important; | |
| # color: #f1f5f9 !important; | |
| # font-size: 15px !important; | |
| # } | |
| # textarea::placeholder { color: #64748b !important; } | |
| # textarea:focus { | |
| # border-color: #3b82f6 !important; | |
| # box-shadow: 0 0 0 3px rgba(59,130,246,0.2) !important; | |
| # } | |
| # label span { color: #94a3b8 !important; font-size: 13px !important; } | |
| # button.primary { | |
| # background: linear-gradient(135deg, #3b82f6, #6366f1) !important; | |
| # border: none !important; | |
| # border-radius: 12px !important; | |
| # color: white !important; | |
| # font-size: 16px !important; | |
| # font-weight: 600 !important; | |
| # } | |
| # button.primary:hover { | |
| # transform: translateY(-2px) !important; | |
| # box-shadow: 0 8px 25px rgba(59,130,246,0.4) !important; | |
| # } | |
| # footer { display: none !important; } | |
| # """ | |
| # # =============================== | |
| # # UI | |
| # # =============================== | |
| # with gr.Blocks() as demo: | |
| # gr.HTML(""" | |
| # <div style='text-align:center; margin-bottom:24px;'> | |
| # <h1 style='font-size:32px;font-weight:800;color:#f1f5f9;margin:0 0 8px 0;'> | |
| # AI Sentiment Analyzer | |
| # </h1> | |
| # <p style='color:#94a3b8;font-size:15px;margin:0;'> | |
| # Real-Time Twitter Sentiment Analysis using BiLSTM | |
| # </p> | |
| # </div> | |
| # """) | |
| # text_input = gr.Textbox( | |
| # label="Enter Text", | |
| # placeholder="Type or paste any tweet, review, or sentence...", | |
| # lines=4, | |
| # elem_id="text_input", | |
| # ) | |
| # gr.HTML(""" | |
| # <div style='margin: 8px 0 4px 0;'> | |
| # <p style='color:#64748b; font-size:12px; margin:0 0 8px 0;'>Try a sample →</p> | |
| # <div style='display:flex; flex-wrap:wrap; gap:8px;'> | |
| # <button onclick="fillText('This AI project is absolutely amazing and the results are very accurate')" | |
| # style='background:#1e3a5f; color:#e2e8f0; border:1px solid #2d5a8e; | |
| # border-radius:8px; padding:6px 12px; font-size:12px; cursor:pointer;'> | |
| # 😊 Positive | |
| # </button> | |
| # <button onclick="fillText('I love this product, it works amazingly!')" | |
| # style='background:#1e3a5f; color:#e2e8f0; border:1px solid #2d5a8e; | |
| # border-radius:8px; padding:6px 12px; font-size:12px; cursor:pointer;'> | |
| # 🌟 Amazing | |
| # </button> | |
| # <button onclick="fillText('This is the worst experience I have ever had.')" | |
| # style='background:#1e3a5f; color:#e2e8f0; border:1px solid #2d5a8e; | |
| # border-radius:8px; padding:6px 12px; font-size:12px; cursor:pointer;'> | |
| # 😤 Negative | |
| # </button> | |
| # <button onclick="fillText('The app keeps crashing, very frustrating.')" | |
| # style='background:#1e3a5f; color:#e2e8f0; border:1px solid #2d5a8e; | |
| # border-radius:8px; padding:6px 12px; font-size:12px; cursor:pointer;'> | |
| # 😞 Frustrating | |
| # </button> | |
| # </div> | |
| # </div> | |
| # <script> | |
| # function fillText(text) { | |
| # const textarea = document.querySelector('#text_input textarea'); | |
| # if (textarea) { | |
| # textarea.value = text; | |
| # textarea.dispatchEvent(new Event('input', { bubbles: true })); | |
| # } | |
| # } | |
| # </script> | |
| # """) | |
| # analyze_btn = gr.Button("Analyze Sentiment", variant="primary", size="lg") | |
| # result_out = gr.HTML() | |
| # analyze_btn.click( | |
| # fn=predict_sentiment, | |
| # inputs=text_input, | |
| # outputs=result_out, | |
| # ) | |
| # gr.HTML(""" | |
| # <div style='text-align:center;margin-top:24px;font-size:12px;color:#475569;'> | |
| # Built with TFLite + Gradio · | |
| # <a href='https://github.com/kanha165/Sentiment-Analysis-using-BiLSTM' | |
| # style='color:#3b82f6;text-decoration:none;' target='_blank'>GitHub ↗</a> | |
| # </div> | |
| # """) | |
| # demo.launch(ssr_mode=False, css=css) | |