Upload app.py

app.py

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+import gradio as gr
+import joblib
+import torch
+import torch.nn as nn
+import numpy as np
+import pandas as pd
+from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification
+from sklearn.preprocessing import LabelEncoder
+from huggingface_hub import hf_hub_download
+import re
+import pyarabic.araby as araby
+
+# Constants
+MODEL_NAME = "aubmindlab/bert-base-arabertv02"
+MODEL_HUB = "batool0/arabic-speech-act-models"
+MAX_LEN = 64
+CLASSES = ['Assertion', 'Expression', 'Question', 'Recommendation', 'Request']
+CLASS_AR = {
+    'Assertion': 'تأكيد',
+    'Expression': 'تعبير',
+    'Question': 'سؤال',
+    'Recommendation': 'توصية',
+    'Request': 'طلب'
+}
+ERROR_PATTERNS = {
+    ('Expression', 'Assertion'): "Expression/Assertion boundary: tweets describing events with emotional tone are often misclassified as Assertion.",
+    ('Assertion', 'Expression'): "Expression/Assertion boundary: factual tweets with emotional vocabulary are sometimes misclassified as Expression.",
+    ('Question', 'Expression'): "Implicit question: this question lacks an explicit interrogative particle (هل، ماذا), causing it to resemble an Expression.",
+    ('Expression', 'Question'): "Implicit question: emotionally phrased tweet contains question-like vocabulary.",
+    ('Request', 'Assertion'): "Analytical request: the request is framed as a logical argument, resembling an Assertion.",
+    ('Request', 'Recommendation'): "Request vs Recommendation: the boundary between requesting and recommending is thin in Arabic.",
+    ('Recommendation', 'Expression'): "Sarcastic recommendation misread as emotional Expression.",
+    ('Assertion', 'Question'): "Assertion/Question boundary: the tweet may contain an implicit question structure without explicit particles.",
+    ('Question', 'Assertion'): "Question/Assertion boundary: the question is phrased as a statement, common in Arabic rhetorical questions.",
+}
+
+CLASS_DESCRIPTIONS = {
+    'Assertion': "states a fact or conveys information objectively.",
+    'Expression': "expresses an opinion, emotion, or personal feeling.",
+    'Question': "asks for information or seeks clarification.",
+    'Recommendation': "suggests or advises a course of action.",
+    'Request': "asks someone to do something or take action.",
+}
+
+# Text Cleaning
+def clean_text(text):
+    text = re.sub(r'http\S+|www\S+', '', text)
+    text = re.sub(r'@\w+', '', text)
+    text = re.sub(r'#\w+', '', text)
+    text = re.sub(r'\d+', '', text)
+    text = re.sub(r'[^\w\s\u0600-\u06FF]', '', text)
+    text = araby.strip_tashkeel(text)
+    text = araby.strip_tatweel(text)
+    text = re.sub(r'[إأآا]', 'ا', text)
+    text = re.sub(r'ة', 'ه', text)
+    text = re.sub(r'ى', 'ي', text)
+    text = re.sub(r'\s+', ' ', text).strip()
+    return text
+
+# BiLSTM Architecture
+class AraBERTBiLSTM(nn.Module):
+    def __init__(self, bert_model_name, hidden_dim, num_layers, num_classes, dropout=0.3):
+        super(AraBERTBiLSTM, self).__init__()
+        self.bert = AutoModel.from_pretrained(bert_model_name)
+        for param in self.bert.parameters():
+            param.requires_grad = False
+        bert_hidden_size = self.bert.config.hidden_size
+        self.bilstm = nn.LSTM(
+            input_size=bert_hidden_size,
+            hidden_size=hidden_dim,
+            num_layers=num_layers,
+            batch_first=True,
+            bidirectional=True,
+            dropout=dropout if num_layers > 1 else 0.0
+        )
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(hidden_dim * 2, num_classes)
+
+    def forward(self, input_ids, attention_mask):
+        with torch.no_grad():
+            bert_output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        token_embeddings = bert_output.last_hidden_state
+        lstm_output, _ = self.bilstm(token_embeddings)
+        mask = attention_mask.unsqueeze(-1).float()
+        pooled = (lstm_output * mask).sum(dim=1) / mask.sum(dim=1).clamp(min=1e-9)
+        pooled = self.dropout(pooled)
+        return self.classifier(pooled)
+
+# AraBERTClassifier Architecture
+class AraBERTClassifier(nn.Module):
+    def __init__(self, model_name, num_classes, dropout=0.3):
+        super(AraBERTClassifier, self).__init__()
+        self.bert = AutoModel.from_pretrained(model_name)
+        self.dropout = nn.Dropout(dropout)
+        self.classifier = nn.Linear(self.bert.config.hidden_size, num_classes)
+
+    def forward(self, input_ids, attention_mask):
+        output = self.bert(input_ids=input_ids, attention_mask=attention_mask)
+        cls_output = output.last_hidden_state[:, 0, :]
+        cls_output = self.dropout(cls_output)
+        logits = self.classifier(cls_output)
+        return logits
+
+# Load Everything
+print("Loading models...")
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+le = LabelEncoder()
+le.fit(CLASSES)
+
+svm_model = joblib.load('svm_model.pkl')
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+bilstm_path = hf_hub_download(repo_id=MODEL_HUB, filename='best_bilstm_arabert.pt')
+bilstm_model = AraBERTBiLSTM(MODEL_NAME, hidden_dim=128, num_layers=2, num_classes=5)
+bilstm_model.load_state_dict(torch.load(bilstm_path, map_location=device))
+bilstm_model.to(device)
+bilstm_model.eval()
+
+arabert_path = hf_hub_download(repo_id=MODEL_HUB, filename='best_arabert.pt')
+arabert_model = AraBERTClassifier(MODEL_NAME, num_classes=len(CLASSES), dropout=0.1).to(device)
+arabert_model.load_state_dict(torch.load(arabert_path, map_location=device))
+arabert_model.eval()
+
+test_df = pd.read_csv('test_with_labels.csv')
+print("All models loaded")
+
+# Predict Functions
+def predict_svm(text):
+    scores = svm_model.decision_function([text])[0]
+    scores = (scores - scores.min()) / (scores.max() - scores.min() + 1e-9)
+    pred_idx = scores.argmax()
+    pred_class = svm_model.classes_[pred_idx]
+    return pred_class, dict(zip(svm_model.classes_, scores.tolist()))
+
+def predict_bilstm(text):
+    enc = tokenizer(text, max_length=MAX_LEN, padding='max_length',
+                    truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        logits = bilstm_model(enc['input_ids'].to(device), enc['attention_mask'].to(device))
+    probs = torch.softmax(logits, dim=1).cpu().numpy()[0]
+    pred_class = le.classes_[probs.argmax()]
+    return pred_class, dict(zip(le.classes_, probs.tolist()))
+
+def predict_arabert(text):
+    enc = tokenizer(text, max_length=MAX_LEN, padding='max_length',
+                    truncation=True, return_tensors='pt')
+    with torch.no_grad():
+        logits = arabert_model(input_ids=enc['input_ids'].to(device),
+                               attention_mask=enc['attention_mask'].to(device))
+    probs = torch.softmax(logits, dim=1).cpu().numpy()[0]
+    pred_class = le.classes_[probs.argmax()]
+    return pred_class, dict(zip(le.classes_, probs.tolist()))
+
+# Ground Truth Lookup
+def get_ground_truth(text):
+    cleaned = clean_text(text)
+    match = test_df[test_df['text'] == cleaned]
+    if len(match) > 0:
+        return match.iloc[0]['label']
+    return None
+
+# Error Analysis for known tweets
+def get_error_analysis(true_label, pred_label):
+    key = (true_label, pred_label)
+    return ERROR_PATTERNS.get(key, f"The model predicted {pred_label} instead of {true_label}. This may reflect lexical overlap between these classes in Arabic social media text.")
+
+# Smart Analysis for new tweets
+def get_smart_analysis(svm_pred, bilstm_pred, ara_pred, svm_probs, bilstm_probs, ara_probs):
+    predictions = [svm_pred, bilstm_pred, ara_pred]
+    unique_preds = set(predictions)
+
+    if len(unique_preds) == 1:
+        pred = ara_pred
+        ara_conf = round(ara_probs.get(pred, 0) * 100)
+        if ara_conf >= 80:
+            return {'type': 'agree_high', 'message': f"All 3 models confidently agree: this tweet <b style='color:inherit;'>{CLASS_DESCRIPTIONS.get(pred, '')}</b> ({ara_conf}% confidence by AraBERT). This is an unambiguous case."}
+        else:
+            return {'type': 'agree_low', 'message': f"All 3 models agree on <b style='color:inherit;'>{pred}</b>, but with moderate confidence ({ara_conf}%). The tweet may have overlapping features with other classes."}
+
+    if len(unique_preds) == 2:
+        majority = max(set(predictions), key=predictions.count)
+        minority_model = None
+        minority_pred = None
+        for model_name, pred in [("SVM", svm_pred), ("BiLSTM", bilstm_pred), ("AraBERT", ara_pred)]:
+            if pred != majority:
+                minority_model = model_name
+                minority_pred = pred
+        pattern_key = (majority, minority_pred)
+        pattern_explanation = ERROR_PATTERNS.get(pattern_key, f"The boundary between {majority} and {minority_pred} can be ambiguous in Arabic social media text.")
+        return {'type': 'partial_disagree', 'message': f"2 models agree on <b style='color:inherit;'>{majority}</b> while {minority_model} predicts <b style='color:inherit;'>{minority_pred}</b>. {pattern_explanation}"}
+
+    return {'type': 'full_disagree', 'message': f"All 3 models disagree — SVM: <b style='color:inherit;'>{svm_pred}</b>, BiLSTM: <b style='color:inherit;'>{bilstm_pred}</b>, AraBERT: <b style='color:inherit;'>{ara_pred}</b>. This tweet is inherently ambiguous, likely due to mixed communicative intent, sarcasm, or dialectal phrasing."}
+
+# SVM Top Features
+def get_top_features(text, pred_class):
+    vec = svm_model.named_steps['tfidf']
+    clf = svm_model.named_steps['svm']
+    feature_names = vec.get_feature_names_out()
+    transformed = vec.transform([text])
+    class_idx = list(svm_model.classes_).index(pred_class)
+    scores = transformed.toarray()[0] * clf.coef_[class_idx]
+    top_idx = scores.argsort()[-5:][::-1]
+    return [feature_names[i] for i in top_idx if scores[i] > 0]
+
+# Main Classify Function
+def classify(text):
+    if not text.strip():
+        return "<p style='color:#555;font-family:sans-serif;'>Please enter an Arabic tweet.</p>"
+
+    cleaned = clean_text(text)
+
+    svm_pred, svm_probs       = predict_svm(cleaned)
+    bilstm_pred, bilstm_probs = predict_bilstm(cleaned)
+    ara_pred, ara_probs       = predict_arabert(cleaned)
+    ground_truth              = get_ground_truth(cleaned)
+    top_features              = get_top_features(cleaned, svm_pred)
+
+    def conf(probs, cls):
+        return round(probs.get(cls, 0) * 100)
+
+    def verdict(pred, gt):
+        if gt is None: return ""
+        return "Correct" if pred == gt else f"Wrong — True: {gt}"
+
+    svm_v    = verdict(svm_pred, ground_truth)
+    bilstm_v = verdict(bilstm_pred, ground_truth)
+    ara_v    = verdict(ara_pred, ground_truth)
+
+    # ── Colors: hard-coded to work on both light & dark themes ──
+    # Cards use white background with dark text always
+    card_bg       = "#ffffff"
+    card_text     = "#111111"
+    card_sub      = "#555555"
+    card_conf     = "#333333"
+
+    features_bg   = "#d4edda"
+    features_text = "#155724"
+    features_title= "#0a4520"
+
+    bar_bg        = "#dddddd"
+    bar_fill      = "#2563eb"
+    bar_label     = "#222222"
+    bar_pct       = "#444444"
+
+    breakdown_title = "#333333"
+
+    correct_color = "#15803d"
+    wrong_color   = "#dc2626"
+
+    features_html = " ".join([
+        f"<span style='background:#d4edda;color:#155724;padding:3px 10px;border-radius:20px;font-size:13px;font-weight:500;'>{w}</span>"
+        for w in top_features
+    ]) or "<span style='color:#555;'>—</span>"
+
+    # Analysis Section
+    if ground_truth:
+        errors = []
+        for model_name, pred in [("SVM", svm_pred), ("BiLSTM", bilstm_pred), ("AraBERT", ara_pred)]:
+            if pred != ground_truth:
+                analysis = get_error_analysis(ground_truth, pred)
+                errors.append(f"<b style='color:#111111;'>{model_name}:</b> <span style='color:#111111;'>{analysis}</span>")
+        if errors:
+            error_html = "<br>".join(errors)
+            analysis_section = f"""
+            <div style='margin-top:16px;padding:14px;background:#fde8e8;border-left:4px solid #dc2626;border-radius:8px;'>
+                <b style='color:#991b1b;font-size:13px;'>Error analysis — ground truth: <span style='color:#111111;'>{ground_truth}</span></b><br>
+                <span style='font-size:13px;color:#111111;line-height:1.6;'>{error_html}</span>
+            </div>"""
+        else:
+            analysis_section = f"""
+            <div style='margin-top:16px;padding:14px;background:#dcfce7;border-left:4px solid #16a34a;border-radius:8px;'>
+                <b style='color:#15803d;font-size:13px;'>All models correct</b>
+                <span style='color:#111111;font-size:13px;'> — ground truth: <b style='color:#111111;'>{ground_truth}</b>. Unambiguous tweet, all 3 models agree.</span>
+            </div>"""
+    else:
+        smart = get_smart_analysis(svm_pred, bilstm_pred, ara_pred, svm_probs, bilstm_probs, ara_probs)
+        styles = {
+            'agree_high':       ('#dcfce7', '#15803d', '#16a34a'),
+            'agree_low':        ('#fef9c3', '#854d0e', '#a16207'),
+            'partial_disagree': ('#ffedd5', '#9a3412', '#c2410c'),
+            'full_disagree':    ('#fde8e8', '#991b1b', '#dc2626'),
+        }
+        bg, text_col, title_col = styles.get(smart['type'], ('#f5f5f5', '#333', '#555'))
+        border_colors = {
+            'agree_high': '#16a34a',
+            'agree_low': '#a16207',
+            'partial_disagree': '#c2410c',
+            'full_disagree': '#dc2626',
+        }
+        border_col = border_colors.get(smart['type'], '#888')
+        analysis_section = f"""
+        <div style='margin-top:16px;padding:14px;background:{bg};border-left:4px solid {border_col};border-radius:8px;'>
+            <b style='color:{title_col};font-size:13px;'>Model analysis — new tweet</b><br>
+            <span style='font-size:13px;color:{text_col};line-height:1.6;'>{smart['message']}</span>
+        </div>"""
+
+    bars_html = ""
+    for cls in CLASSES:
+        pct = conf(ara_probs, cls)
+        bars_html += f"""
+        <div style='display:flex;align-items:center;gap:10px;margin-bottom:7px;'>
+            <span style='font-size:12px;width:130px;color:{bar_label};font-weight:500;'>{cls}</span>
+            <div style='flex:1;background:{bar_bg};border-radius:4px;height:7px;'>
+                <div style='background:{bar_fill};width:{pct}%;height:7px;border-radius:4px;'></div>
+            </div>
+            <span style='font-size:12px;width:36px;text-align:right;color:{bar_pct};font-weight:600;'>{pct}%</span>
+        </div>"""
+
+    html = f"""
+    <div style='font-family:sans-serif;max-width:660px;'>
+
+        <div style='display:grid;grid-template-columns:repeat(3,1fr);gap:10px;margin-bottom:16px;'>
+
+            <div style='background:{card_bg};border:1.5px solid #6ee7b7;border-radius:10px;padding:14px;box-shadow:0 1px 4px rgba(0,0,0,0.08);'>
+                <p style='font-size:11px;color:#059669;margin:0 0 6px;font-weight:700;letter-spacing:0.3px;'>SVM + TF-IDF</p>
+                <p style='font-size:17px;font-weight:700;margin:0;color:{card_text};'>{svm_pred}</p>
+                <p style='font-size:11px;color:{card_sub};margin:2px 0 6px;'>{CLASS_AR.get(svm_pred,'')}</p>
+                <p style='font-size:12px;margin:0;color:{card_conf};'>{conf(svm_probs, svm_pred)}% confidence</p>
+                <p style='font-size:11px;margin:5px 0 0;font-weight:600;color:{"" + correct_color if "Correct" in svm_v else wrong_color};'>{svm_v}</p>
+            </div>
+
+            <div style='background:{card_bg};border:2px solid #a5b4fc;border-radius:10px;padding:14px;box-shadow:0 1px 4px rgba(0,0,0,0.08);'>
+                <p style='font-size:11px;color:#4f46e5;margin:0 0 6px;font-weight:700;letter-spacing:0.3px;'>BiLSTM</p>
+                <p style='font-size:17px;font-weight:700;margin:0;color:{card_text};'>{bilstm_pred}</p>
+                <p style='font-size:11px;color:{card_sub};margin:2px 0 6px;'>{CLASS_AR.get(bilstm_pred,'')}</p>
+                <p style='font-size:12px;margin:0;color:{card_conf};'>{conf(bilstm_probs, bilstm_pred)}% confidence</p>
+                <p style='font-size:11px;margin:5px 0 0;font-weight:600;color:{"" + correct_color if "Correct" in bilstm_v else wrong_color};'>{bilstm_v}</p>
+            </div>
+
+            <div style='background:{card_bg};border:1.5px solid #93c5fd;border-radius:10px;padding:14px;box-shadow:0 1px 4px rgba(0,0,0,0.08);'>
+                <p style='font-size:11px;color:#1d4ed8;margin:0 0 6px;font-weight:700;letter-spacing:0.3px;'>AraBERT</p>
+                <p style='font-size:17px;font-weight:700;margin:0;color:{card_text};'>{ara_pred}</p>
+                <p style='font-size:11px;color:{card_sub};margin:2px 0 6px;'>{CLASS_AR.get(ara_pred,'')}</p>
+                <p style='font-size:12px;margin:0;color:{card_conf};'>{conf(ara_probs, ara_pred)}% confidence</p>
+                <p style='font-size:11px;margin:5px 0 0;font-weight:600;color:{"" + correct_color if "Correct" in ara_v else wrong_color};'>{ara_v}</p>
+            </div>
+
+        </div>
+
+        <div style='padding:14px;background:{features_bg};border-radius:8px;margin-bottom:14px;'>
+            <p style='font-size:12px;color:{features_title};margin:0 0 8px;font-weight:700;'>Top signals </p>
+            {features_html}
+        </div>
+
+        <div style='margin-bottom:14px;background:#ffffff;padding:12px;border-radius:8px;box-shadow:0 1px 4px rgba(0,0,0,0.06);'>
+            <p style='font-size:12px;color:{breakdown_title};margin:0 0 10px;font-weight:700;'>All classes — confidence breakdown </p>
+            {bars_html}
+        </div>
+
+        {analysis_section}
+    </div>
+    """
+    return html
+
+# Launch
+demo = gr.Interface(
+    fn=classify,
+    inputs=gr.Textbox(
+        label="Enter an Arabic tweet",
+        placeholder="مثال: الـ AraBERT فهم التغريدات العربية احسن مني انا ههههههههه",
+        rtl=True,
+        lines=3
+    ),
+    outputs=gr.HTML(label="Results"),
+    title="Arabic Dialogue/Speech Act Classifier",
+    description="AI 445 — NLP Project | Jordan University of Science and Technology",
+    examples=[
+        [""],
+        ["الأكل كان رائع جداً!"],
+        ["مين المسؤول ان الصوت بيقطع و مش ماشي مع كلام الرئيس اودام العالم كله"],
+        ["رئيس الجمهوريه التونسيه حاضرا مباراه بلاده في تصفيات كاس العالم"],
+        ["المشروع سينتهي غداً"],
+        ["ليش ال Recommendation غالبا بفشل؟ لانه مثلي ماحدا بسمعه"],
+        ["ماذا قال محمد صلاح عن اداء وتاهل تونس والمغرب الي المونديال"],
+        ["عندي اقتراح للشيخ عزمي بشاره بما ان رايه صائب الي هذه الدرجه ان يجلس مع الشيخ تميم ويوضعوا خطه محكمه لاعاده فلسطين او اعاده القدس ويتركوا الربيع العربي مؤقتا"],
+        ["رياضه محمد صلاح ينافس نجوم علي جائزه BBC للافضل في افريقيا"]
+    ],
+    flagging_mode="never"
+)
+
+demo.launch()