Delete demo.py

demo.py

@@ -1,120 +0,0 @@
-import os
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
-
-from huggingface_hub import hf_hub_download
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-import torch
-
-import pickle
-import numpy as np
-from tensorflow.keras.models import load_model
-from tensorflow.keras.preprocessing.sequence import pad_sequences
-import re
-
-svm_repo_id = "HighFive-OPJ/Deep_Learning"
-svm_model_path = hf_hub_download(repo_id=svm_repo_id, filename="svm_model.pkl")
-with open(svm_model_path, "rb") as f:
-    svm_model = pickle.load(f)
-vectorizer_path = hf_hub_download(repo_id=svm_repo_id, filename="vectorizer.pkl")
-with open(vectorizer_path, "rb") as f:
-    vectorizer = pickle.load(f)
-
-lstm_repo_id = "HighFive-OPJ/Deep_Learning"
-lstm_model_path = hf_hub_download(repo_id=lstm_repo_id, filename="LSTM_model.h5")
-lstm_model = load_model(lstm_model_path)
-lstm_tokenizer_path = hf_hub_download(repo_id=lstm_repo_id, filename="my_tokenizer.pkl")
-with open(lstm_tokenizer_path, "rb") as f:
-    lstm_tokenizer = pickle.load(f)
-
-bert_tokenizer = AutoTokenizer.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
-bert_model = AutoModelForSequenceClassification.from_pretrained("nlptown/bert-base-multilingual-uncased-sentiment")
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-bert_model.to(device)
-
-def preprocess_text(text):
-    text = text.lower()
-    text = re.sub(r"[^a-zA-Z\s]", "", text).strip()
-    return text
-
-def predict_with_svm(text):
-    transformed = vectorizer.transform([text])
-    prediction = svm_model.predict(transformed)
-    return int(prediction[0])
-
-def predict_with_lstm(text):
-    cleaned = preprocess_text(text)
-    seq = lstm_tokenizer.texts_to_sequences([cleaned])
-    padded_seq = pad_sequences(seq, maxlen=200)
-    probs = lstm_model.predict(padded_seq)
-    predicted_class = np.argmax(probs, axis=1)[0]
-    return int(predicted_class)
-
-def predict_with_bert(text):
-    inputs = bert_tokenizer([text], padding=True, truncation=True, max_length=512, return_tensors="pt").to(device)
-    with torch.no_grad():
-        outputs = bert_model(**inputs)
-        logits = outputs.logits
-        predictions = logits.argmax(axis=-1).cpu().numpy()
-    bert_score = int(predictions[0])
-    if bert_score <= 2:
-        return 0
-    elif bert_score == 3:
-        return 1
-    else:
-        return 2
-
-def analyze_sentiment(text):
-    results = {
-        "SVM": predict_with_svm(text),
-        "LSTM": predict_with_lstm(text),
-        "BERT": predict_with_bert(text)
-    }
-    scores = list(results.values())
-    average = np.mean(scores)
-    stats = f"Average Score (0=Neg,1=Neu,2=Pos): {average:.2f}\n"
-    return (
-        convert_to_stars(results["SVM"]),
-        convert_to_stars(results["LSTM"]),
-        convert_to_stars(results["BERT"]),
-        stats
-    )
-
-def convert_to_stars(score):
-    # Map 0->1 star, 1->3 stars, 2->5 stars
-    star_map = {0: 1, 1: 3, 2: 5}
-    stars = star_map.get(score, 3)  
-    return "★" * stars + "☆" * (5 - stars)
-
-def process_input(text):
-    if not text.strip():
-        return ("", "", "", "Please enter valid text.")
-    return analyze_sentiment(text)
-
-with gr.Blocks() as demo:
-    gr.Markdown("# Sentiment Analysis Demo")
-    gr.Markdown("""
-        Enter a review and see how different models evaluate its sentiment! This app uses:
-        - SVM for classic machine learning
-        - LSTM for deep learning
-        - BERT for transformer-based analysis
-    """)
-
-    with gr.Row():
-        with gr.Column():
-            input_text = gr.Textbox(label="Enter your review:", lines=3)
-            analyze_button = gr.Button("Analyze Sentiment")
-
-        with gr.Column():
-            svm_output = gr.Textbox(label="SVM", interactive=False)
-            lstm_output = gr.Textbox(label="LSTM", interactive=False)
-            bert_output = gr.Textbox(label="BERT", interactive=False)
-            stats_output = gr.Textbox(label="Statistics", interactive=False)
-
-    analyze_button.click(
-        process_input,
-        inputs=[input_text],
-        outputs=[svm_output, lstm_output, bert_output, stats_output]
-    )
-
-demo.launch()