File size: 10,049 Bytes
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>&nbsp;{text}
#             </p>
#             <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'>
#                 <b style='color:#f1f5f9;'>Cleaned Text:</b>&nbsp;{cleaned}
#             </p>
#             <p style='margin:8px 0;color:#cbd5e1;font-size:14px;line-height:1.8;'>
#                 <b style='color:#f1f5f9;'>Sentiment:</b>&nbsp;
#                 <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>&nbsp;
#                 <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 &nbsp;·&nbsp;
#             <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)