Merge remote files and add my email app

Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.9
+
+# 1. Create a non-root user
+RUN useradd -m -u 1000 user
+USER user
+
+# 2. Set environment variables to ensure Python finds installed packages
+ENV HOME=/home/user \
+    PATH=/home/user/.local/bin:$PATH \
+    PYTHONPATH=/home/user/app
+
+WORKDIR /app
+
+# 3. Upgrade pip first (important for newer packages)
+RUN pip install --no-cache-dir --upgrade pip
+
+# 4. Copy requirements and install
+COPY --chown=user requirements.txt .
+# Adding --user ensures packages go to the path we set above
+RUN pip install --no-cache-dir --user -r requirements.txt
+
+# 5. Copy the rest of your app
+COPY --chown=user . .
+
+# 6. Run the app
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

@@ -1,10 +1,26 @@
----
-title: Email Classification
-emoji: 📉
-colorFrom: green
-colorTo: red
-sdk: docker
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+🔥 Firebase Configuration & Setup
+To get this application running successfully, please follow these two essential steps to link your Firebase account.
+
+🔑 1. Firebase Service Account Key
+The application requires a private key to authenticate with your Firebase project.
+
+Download: Get your serviceAccountKey.json from the Firebase Console.
+
+Place: Move the file into the root folder of this project.
+
+[!WARNING]
+
+Security: Ensure this file is added to your .gitignore to avoid leaking private credentials.
+
+🌐 2. Configure Firebase URL
+Before running the program, you must update the database URL in app.py.
+
+Open app.py.
+
+Locate the following line:
+
+Python
+
+# Replace the placeholder with your actual Firebase Realtime Database URL
+database_url = "ADD_YOUR_FIREBASE_URL_HERE"
+Replace "ADD_YOUR_FIREBASE_URL_HERE" with your actual URL.

app.py

@@ -0,0 +1,435 @@
+import os
+import gc
+import re
+import torch
+import numpy as np
+import firebase_admin
+from firebase_admin import credentials, db
+from fastapi import FastAPI, Form, Request
+from fastapi.responses import HTMLResponse
+from transformers import (
+    AutoTokenizer, 
+    AutoConfig, 
+    DistilBertForSequenceClassification, 
+    T5ForConditionalGeneration, 
+    T5Tokenizer
+)
+from lime.lime_text import LimeTextExplainer
+import uuid
+from datetime import datetime
+
+# FIREBASE INITIALIZATION
+# Ensure your serviceAccountKey.json is in the same directory
+if not firebase_admin._apps:
+    cred = credentials.Certificate("serviceAccountKey.json")
+    firebase_admin.initialize_app(cred, {
+   'databaseURL': 'https://your-project-id.firebaseio.com/'  # <--- Add Firebase URL here
+    })
+
+app = FastAPI()
+
+# Global variables
+tokenizer = None
+model = None
+explainer = LimeTextExplainer(class_names=["LEGITIMATE", "PHISHING"])
+gen_tokenizer = None
+gen_model = None
+MODEL_DIR = "phishing_model_optimized"
+WEIGHTS_NAME = "quantized_model.pt"
+
+
+# PHISHING INDICATOR PATTERNS (Original Rule-Based)
+
+PHISHING_PATTERNS = {
+    "urgency": [
+        r"\bimmediate(ly)?\b", r"\burgent(ly)?\b", r"\bact now\b", 
+        r"\baction required\b", r"\bwithin \d+ (hour|minute|day)s?\b",
+        r"\bexpir(e|es|ing|ed)\b", r"\bsuspended\b", r"\bcompromised\b",
+        r"\bverify (now|immediately|your)\b", r"\bfailure to\b",
+        r"\bpermanent(ly)?\b", r"\bdelete(d)?\b", r"\block(ed)?\b"
+    ],
+    "threat": [
+        r"\baccount.*(suspend|terminat|delet|lock|compromis)\w*",
+        r"\b(suspend|terminat|delet|lock|compromis)\w*.*account\b",
+        r"\blegal action\b", r"\bpenalt(y|ies)\b", r"\bconsequences\b"
+    ],
+    "credential_request": [
+        r"\bpassword\b", r"\bverify your (identity|account|email)\b",
+        r"\bconfirm your\b", r"\bupdate.*(payment|billing|account)\b",
+        r"\bssn\b", r"\bcredit card\b", r"\bbank account\b"
+    ],
+    "suspicious_links": [
+        r"https?://[^\s]*\.(xyz|tk|ml|ga|cf|gq|top|club|online)/",
+        r"https?://[^\s]*-[^\s]*\.(com|net|org)/",
+        r"https?://\d+\.\d+\.\d+\.\d+",
+        r"bit\.ly|tinyurl|short\.link|t\.co",
+        r"click.*here|click.*below|click.*link"
+    ],
+    "impersonation": [
+        r"\b(paypal|amazon|netflix|apple|microsoft|google|bank)\b",
+        r"\bcustomer (service|support)\b", r"\bsecurity (team|department)\b"
+    ]
+}
+
+def detect_phishing_indicators(text: str) -> dict:
+    text_lower = text.lower()
+    detected = {cat: [] for cat in PHISHING_PATTERNS}
+    for category, patterns in PHISHING_PATTERNS.items():
+        for pattern in patterns:
+            matches = re.findall(pattern, text_lower, re.IGNORECASE)
+            if matches:
+                detected[category].extend(matches if isinstance(matches[0], str) else [m[0] for m in matches])
+    for category in detected:
+        detected[category] = list(set(detected[category]))
+    return detected
+
+def calculate_phishing_score(indicators: dict) -> float:
+    weights = {"urgency": 0.25, "threat": 0.25, "credential_request": 0.20, "suspicious_links": 0.20, "impersonation": 0.10}
+    score = 0.0
+    for category, weight in weights.items():
+        if indicators[category]:
+            category_score = min(len(indicators[category]) * 0.4, 1.0)
+            score += weight * category_score
+    return min(score, 1.0)
+
+def get_confidence_label(confidence: float) -> str:
+    """Determine confidence level descriptor based on score"""
+    if confidence >= 0.90:
+        return "high confidence"
+    elif confidence >= 0.70:
+        return "moderate confidence"
+    else:
+        return "low confidence"
+
+def generate_explanation_with_flan(indicators: dict, label: str, confidence: float, email_text: str) -> str:
+    """Generate high-quality natural language explanation using FLAN-T5"""
+    
+    # Get confidence level descriptor
+    confidence_level = get_confidence_label(confidence)
+    
+    # Build detailed indicator analysis
+    indicator_details = []
+    urgency_count = len(indicators["urgency"])
+    threat_count = len(indicators["threat"])
+    cred_count = len(indicators["credential_request"])
+    link_count = len(indicators["suspicious_links"])
+    imperson_count = len(indicators["impersonation"])
+    
+    if urgency_count > 0:
+        urgency_examples = ', '.join([f'"{item}"' for item in indicators['urgency'][:2]])
+        indicator_details.append(f"high urgency language ({urgency_count} instances: {urgency_examples})")
+    if threat_count > 0:
+        threat_examples = ', '.join([f'"{item}"' for item in indicators['threat'][:2]])
+        indicator_details.append(f"threatening tone ({threat_count} instances: {threat_examples})")
+    if cred_count > 0:
+        cred_examples = ', '.join([f'"{item}"' for item in indicators['credential_request'][:2]])
+        indicator_details.append(f"credential requests ({cred_count} instances: {cred_examples})")
+    if link_count > 0:
+        indicator_details.append(f"suspicious links ({link_count} detected)")
+    if imperson_count > 0:
+        brands = ', '.join(indicators['impersonation'][:2])
+        indicator_details.append(f"brand impersonation attempts ({brands})")
+    
+    # Create sophisticated prompts based on label
+    if label == "PHISHING":
+        indicators_summary = "; ".join(indicator_details) if indicator_details else "general phishing patterns"
+        
+        prompt = f"""You are a cybersecurity expert. Explain why this email is phishing:
+
+Email sample: "{email_text[:250]}"
+
+Detected threats: {indicators_summary}
+
+Write a clear 2-sentence explanation that:
+1. States the classification with confidence level
+2. Describes specific malicious tactics (urgency, social engineering, credential harvesting, clickbait)
+3. Uses professional security terminology
+
+Explanation:"""
+    else:  # LEGITIMATE
+        safe_indicators = []
+        if not indicators["urgency"] and not indicators["threat"]:
+            safe_indicators.append("no urgency or threat language")
+        if not indicators["credential_request"]:
+            safe_indicators.append("no credential requests")
+        if not indicators["suspicious_links"]:
+            safe_indicators.append("no suspicious links")
+        if not indicators["impersonation"]:
+            safe_indicators.append("no brand impersonation")
+        
+        safety_summary = ", ".join(safe_indicators) if safe_indicators else "standard communication patterns"
+        
+        prompt = f"""You are a cybersecurity expert. Explain why this email is legitimate:
+
+Email sample: "{email_text[:250]}"
+
+Safety indicators: {safety_summary}
+
+Write a clear 2-sentence explanation that:
+1. States the classification with confidence level
+2. Notes the absence of social-engineering cues, suspicious tokens, or phishing tactics
+3. Uses professional security terminology
+
+Explanation:"""
+    
+    try:
+        # Tokenize the prompt
+        inputs = gen_tokenizer(
+            prompt, 
+            return_tensors="pt", 
+            max_length=512, 
+            truncation=True
+        )
+        
+        # Generate explanation with optimized parameters
+        with torch.no_grad():
+            outputs = gen_model.generate(
+                inputs.input_ids,
+                max_length=180,
+                min_length=40,
+                num_beams=5,
+                length_penalty=1.2,
+                early_stopping=True,
+                temperature=0.8,
+                top_p=0.92,
+                do_sample=False,
+                no_repeat_ngram_size=3
+            )
+        
+        # Decode the generated text
+        explanation = gen_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Post-process: add confidence level prefix
+        explanation = f"The email was classified as {label} with {confidence_level} ({confidence:.2f}). {explanation}"
+        
+        return explanation
+        
+    except Exception as e:
+        print(f"FLAN-T5 generation error: {e}")
+        # Enhanced fallback explanations
+        if label == "PHISHING":
+            reasons = []
+            if indicators["urgency"]: reasons.append("uses high urgency tactics")
+            if indicators["threat"]: reasons.append("contains threatening language")
+            if indicators["credential_request"]: reasons.append("attempts credential harvesting")
+            if indicators["suspicious_links"]: reasons.append("includes clickbait keywords")
+            
+            reason_text = " and ".join(reasons) if reasons else "exhibits fraudulent patterns"
+            return f"The email was classified as PHISHING with {confidence_level} ({confidence:.2f}). The email {reason_text} suggesting a social-engineering attempt to capture sensitive information."
+        else:
+            return f"The email was classified as LEGITIMATE with {confidence_level} ({confidence:.2f}). The message appears routine and contains no social-engineering cues or suspicious tokens."
+
+@app.on_event("startup")
+def load_models():
+    global tokenizer, model, gen_tokenizer, gen_model
+    base_path = os.path.dirname(os.path.abspath(__file__))
+    model_path = os.path.join(base_path, MODEL_DIR)
+    weights_path = os.path.join(model_path, WEIGHTS_NAME)
+    gc.collect()
+    try:
+        # Load DistilBERT for classification
+        tokenizer = AutoTokenizer.from_pretrained(model_path, local_files_only=True)
+        config = AutoConfig.from_pretrained(model_path, local_files_only=True)
+        base_model = DistilBertForSequenceClassification(config)
+        model = torch.quantization.quantize_dynamic(base_model, {torch.nn.Linear}, dtype=torch.qint8)
+        if os.path.exists(weights_path):
+            model.load_state_dict(torch.load(weights_path, map_location='cpu'))
+            model.eval()
+        
+        # Load FLAN-T5 for explanation generation
+        print("Loading FLAN-T5 for explanation generation...")
+        gen_tokenizer = T5Tokenizer.from_pretrained("google/flan-t5-small", legacy=False)
+        gen_model = T5ForConditionalGeneration.from_pretrained("google/flan-t5-small")
+        gen_model.eval()
+        print("FLAN-T5 loaded successfully!")
+        
+    except Exception as e: 
+        print(f"Load Error: {e}")
+
+def predictor(texts):
+    probs_list = []
+    for text in texts:
+        inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=256)
+        with torch.no_grad():
+            outputs = model(**inputs)
+            probs = torch.softmax(outputs.logits, dim=1).numpy()
+            probs_list.append(probs[0])
+    return np.array(probs_list)
+
+def hybrid_predict(email_text: str) -> tuple:
+    ml_probs = predictor([email_text])[0]
+    indicators = detect_phishing_indicators(email_text)
+    rule_score = calculate_phishing_score(indicators)
+    # Combine scores based on your logic
+    if rule_score > 0.5: combined_prob = (ml_probs[1] * 0.3) + (rule_score * 0.7)
+    elif rule_score > 0.2: combined_prob = (ml_probs[1] * 0.5) + (rule_score * 0.5)
+    else: combined_prob = (ml_probs[1] * 0.7) + (rule_score * 0.3)
+    
+    label = "PHISHING" if combined_prob >= 0.5 else "LEGITIMATE"
+    confidence = combined_prob if label == "PHISHING" else 1 - combined_prob
+    return label, confidence, indicators
+
+@app.get("/", response_class=HTMLResponse)
+async def index():
+    return """
+    <html>
+    <head><title>Robust Explainable Phishing Classification</title></head>
+    <body style="font-family: sans-serif; background: #f4f7f6; display: flex; justify-content: center; padding-top: 50px;">
+        <div style="background: white; padding: 40px; border-radius: 15px; box-shadow: 0 10px 30px rgba(0,0,0,0.1); width: 800px;">
+            <form action="/predict" method="post">
+                <textarea name="email_text" rows="10" style="width: 100%; padding: 10px;" placeholder="Paste email here..." required></textarea>
+                <button type="submit" style="width: 100%; background: #007bff; color: white; padding: 15px; margin-top: 10px; border: none; cursor: pointer; border-radius: 8px;">Analyze & Explain</button>
+            </form>
+        </div>
+    </body>
+    </html>
+    """
+
+@app.post("/predict", response_class=HTMLResponse)
+async def predict(email_text: str = Form(...)):
+    label, confidence, indicators = hybrid_predict(email_text)
+    
+    # Generate unique token for this analysis
+    unique_token = str(uuid.uuid4())[:8].upper()
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Store analysis data in Firebase for reference
+    analysis_ref = db.reference('/analysis_records')
+    analysis_ref.child(unique_token).set({
+        'timestamp': timestamp,
+        'label': label,
+        'confidence': float(confidence),
+        'email_length': len(email_text)
+    })
+    
+    # Get LIME explanation for technical keywords
+    try:
+        exp = explainer.explain_instance(email_text, predictor, num_features=6, num_samples=100)
+        keyword_str = ", ".join([word for word, weight in exp.as_list() if abs(weight) > 0.01])
+    except:
+        keyword_str = "analysis unavailable"
+
+    # Generate natural language explanation using FLAN-T5
+    clean_explanation = generate_explanation_with_flan(indicators, label, confidence, email_text)
+    
+    color = "#dc3545" if label == "PHISHING" else "#28a745"
+    
+    # HTML Result with Enhanced Feedback Form
+    return f"""
+    <div style="font-family: sans-serif; max-width: 900px; margin: auto; padding: 20px;">
+        <div style="background: {color}; color: white; padding: 20px; border-radius: 20px; text-align: center;">
+            <h1 style="margin: 0;">{label}</h1>
+            <p>Confidence: {confidence:.2%}</p>
+        </div>
+        <div style="background: white; padding: 30px; border-radius: 15px; margin-top: 30px; box-shadow: 0 5px 15px rgba(0,0,0,0.05);">
+            <h3>Security Analysis (FLAN-T5 Generated)</h3>
+            <p style="font-size: 1.1em;">{clean_explanation}</p>
+            <p><b>Technical Triggers (LIME):</b> {keyword_str}</p>
+            
+            <hr style="margin: 40px 0;">
+            
+            <h3>Help Us Improve (XAI Evaluation)</h3>
+            <div style="background: #f8f9fa; padding: 15px; border-radius: 8px; margin-bottom: 20px;">
+                <p style="margin: 5px 0;"><b>Analysis Token:</b> <span style="font-family: monospace; font-size: 1.2em; color: {color}; font-weight: bold;">{unique_token}</span></p>
+                <p style="margin: 5px 0; font-size: 0.9em; color: #666;">Please save this token for your records</p>
+            </div>
+            
+            <form action="/feedback" method="post">
+                <input type="hidden" name="token" value="{unique_token}">
+                
+                <table style="width: 100%; border-collapse: collapse; margin: 20px 0;">
+                    <thead>
+                        <tr style="background: #f1f3f5;">
+                            <th style="padding: 15px; text-align: left; border: 1px solid #dee2e6; width: 50%;">Evaluation Criteria</th>
+                            <th style="padding: 10px; text-align: center; border: 1px solid #dee2e6; width: 10%;">1</th>
+                            <th style="padding: 10px; text-align: center; border: 1px solid #dee2e6; width: 10%;">2</th>
+                            <th style="padding: 10px; text-align: center; border: 1px solid #dee2e6; width: 10%;">3</th>
+                            <th style="padding: 10px; text-align: center; border: 1px solid #dee2e6; width: 10%;">4</th>
+                            <th style="padding: 10px; text-align: center; border: 1px solid #dee2e6; width: 10%;">5</th>
+                        </tr>
+                    </thead>
+                    <tbody>
+                        <tr>
+                            <td style="padding: 15px; border: 1px solid #dee2e6; background: #fff;">
+                                <b>1. Decision Clarity:</b><br>
+                                <span style="font-size: 0.9em; color: #666;">The explanation helped me understand the result.</span>
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6;">
+                                <input type="radio" name="understanding" value="1" required style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6;">
+                                <input type="radio" name="understanding" value="2" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6;">
+                                <input type="radio" name="understanding" value="3" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6;">
+                                <input type="radio" name="understanding" value="4" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6;">
+                                <input type="radio" name="understanding" value="5" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                        </tr>
+                        <tr>
+                            <td style="padding: 15px; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <b>2. Information Focus:</b><br>
+                                <span style="font-size: 0.9em; color: #666;">The explanation was concise and essential.</span>
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <input type="radio" name="clarity" value="1" required style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <input type="radio" name="clarity" value="2" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <input type="radio" name="clarity" value="3" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <input type="radio" name="clarity" value="4" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                            <td style="padding: 10px; text-align: center; border: 1px solid #dee2e6; background: #f8f9fa;">
+                                <input type="radio" name="clarity" value="5" style="width: 20px; height: 20px; cursor: pointer;">
+                            </td>
+                        </tr>
+                    </tbody>
+                </table>
+                
+                <p style="font-size: 0.85em; color: #666; text-align: center; margin-top: 10px;">
+                    Rating Scale: 1 = Strongly Disagree | 5 = Strongly Agree
+                </p>
+
+                <button type="submit" style="width: 100%; background: #28a745; color: white; padding: 12px; border: none; border-radius: 5px; cursor: pointer; font-size: 1.05em; margin-top: 15px;">Submit Feedback to Firebase</button>
+            </form>
+        </div>
+        
+        <div style="text-align: center; margin-top: 20px;">
+            <a href="/" style="color: #007bff; text-decoration: none;">← Analyze Another Email</a>
+        </div>
+    </div>
+    """
+
+@app.post("/feedback")
+async def save_feedback(token: str = Form(...), understanding: int = Form(...), clarity: int = Form(...)):
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    
+    # Save feedback with token reference
+    feedback_ref = db.reference('/xai_feedback')
+    feedback_ref.push({
+        'token': token,
+        'understanding': understanding,
+        'clarity': clarity,
+        'timestamp': timestamp
+    })
+    
+    return HTMLResponse(f"""
+    <div style="font-family: sans-serif; max-width: 800px; margin: 100px auto; padding: 40px; background: white; border-radius: 15px; box-shadow: 0 10px 30px rgba(0,0,0,0.1); text-align: center;">
+        <h2 style="color: #28a745;">Feedback Received!</h2>
+        <p>Thank you for contributing to our research.</p>
+        <p><b>Your Token:</b> <span style="font-family: monospace; font-size: 1.2em; color: #007bff;">{token}</span></p>
+        <a href="/" style="display: inline-block; margin-top: 20px; padding: 12px 30px; background: #007bff; color: white; text-decoration: none; border-radius: 5px;">Go back to Home</a>
+    </div>
+    """)
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)

phishing_model_optimized/config.json

@@ -0,0 +1,24 @@
+{
+  "activation": "gelu",
+  "architectures": [
+    "DistilBertForMaskedLM"
+  ],
+  "attention_dropout": 0.1,
+  "dim": 768,
+  "dropout": 0.1,
+  "dtype": "float32",
+  "hidden_dim": 3072,
+  "initializer_range": 0.02,
+  "max_position_embeddings": 512,
+  "model_type": "distilbert",
+  "n_heads": 12,
+  "n_layers": 6,
+  "pad_token_id": 0,
+  "problem_type": "single_label_classification",
+  "qa_dropout": 0.1,
+  "seq_classif_dropout": 0.2,
+  "sinusoidal_pos_embds": false,
+  "tie_weights_": true,
+  "transformers_version": "4.57.3",
+  "vocab_size": 30522
+}

phishing_model_optimized/quantized_model.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f4baee6ffe5da36849e00cd0180354af5d72d80968cf85fb2efb809b4ea80ef2
+size 138716071

phishing_model_optimized/special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

phishing_model_optimized/tokenizer_config.json

@@ -0,0 +1,56 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "100": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "101": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "102": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "103": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": false,
+  "cls_token": "[CLS]",
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 512,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "DistilBertTokenizer",
+  "unk_token": "[UNK]"
+}

requirements.txt

@@ -0,0 +1,9 @@
+fastapi
+uvicorn
+python-multipart
+transformers
+firebase-admin
+lime
+scikit-learn
+numpy
+torch --index-url https://download.pytorch.org/whl/cpu