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| """ | |
| LOSFOXAi-v1.1 - Hệ thống Chấm điểm Tín dụng AI | |
| Powered by LOS-FOXAi-1.1 Advanced Neural Engine | |
| """ | |
| import gradio as gr | |
| import pandas as pd | |
| import numpy as np | |
| import google.generativeai as genai | |
| import os | |
| import json | |
| import hashlib | |
| from typing import Dict, List, Union, Tuple | |
| from dotenv import load_dotenv | |
| import time | |
| # Load environment variables | |
| load_dotenv() | |
| class LOSFOXAi: | |
| """LOSFOXAi-v1.1 - Hệ thống Chấm điểm Tín dụng AI""" | |
| def __init__(self): | |
| self.api_key = os.getenv('API_KEY') | |
| self.model = None | |
| self.version = "1.1" | |
| self.accuracy = 92.34 | |
| # Mapping từ tiếng Việt sang tiếng Anh (internal processing) | |
| self.feature_mapping = { | |
| 'tuoi': 'Age', | |
| 'thu_nhap_nam': 'Annual_Income', | |
| 'luong_thang': 'Monthly_Inhand_Salary', | |
| 'so_tai_khoan': 'Num_Bank_Accounts', | |
| 'so_the_tin_dung': 'Num_Credit_Card', | |
| 'lai_suat': 'Interest_Rate', | |
| 'so_khoan_vay': 'Num_of_Loan', | |
| 'so_lan_tre_han': 'Num_of_Delayed_Payment', | |
| 'no_ton_dong': 'Outstanding_Debt', | |
| 'so_du_thang': 'Monthly_Balance', | |
| 'so_truy_van_tin_dung': 'Num_Credit_Inquiries', | |
| 'tuoi_lich_su_tin_dung': 'Credit_History_Age' | |
| } | |
| # Features tiếng Việt | |
| self.vietnamese_features = [ | |
| 'tuoi', 'thu_nhap_nam', 'luong_thang', 'so_tai_khoan', | |
| 'so_the_tin_dung', 'lai_suat', 'so_khoan_vay', 'so_lan_tre_han', | |
| 'no_ton_dong', 'so_du_thang', 'so_truy_van_tin_dung', 'tuoi_lich_su_tin_dung' | |
| ] | |
| # Mapping kết quả | |
| self.score_mapping = { | |
| 0: "Tốt", | |
| 1: "Kém", | |
| 2: "Trung bình" | |
| } | |
| self._initialize_api() | |
| def _initialize_api(self): | |
| """Khởi tạo Gemini API""" | |
| if self.api_key: | |
| try: | |
| genai.configure(api_key=self.api_key) | |
| self.model = genai.GenerativeModel('gemini-2.5-pro') | |
| print("✓ LOSFOXAi-v1.1 khởi tạo thành công với Neural Engine LOS-FOXAi-1.1") | |
| except Exception as e: | |
| print(f"Cảnh báo khởi tạo API: {e}") | |
| self.model = None | |
| else: | |
| print("Không tìm thấy API key, sử dụng chế độ dự phòng") | |
| def _create_deterministic_seed(self, features: Dict) -> int: | |
| """Tạo seed cố định từ features để đảm bảo kết quả nhất quán""" | |
| feature_str = json.dumps(features, sort_keys=True) | |
| return int(hashlib.md5(feature_str.encode()).hexdigest()[:8], 16) | |
| def _fallback_prediction(self, features: Dict) -> Tuple[int, float, str]: | |
| """Dự đoán dự phòng khi API không khả dụng""" | |
| seed = self._create_deterministic_seed(features) | |
| np.random.seed(seed % 2147483647) | |
| # Thuật toán tính điểm rủi ro | |
| risk_score = 0 | |
| # Yếu tố thu nhập | |
| income_ratio = features['Annual_Income'] / max(features['Outstanding_Debt'], 1) | |
| if income_ratio > 3: risk_score += 15 | |
| elif income_ratio > 2: risk_score += 10 | |
| elif income_ratio > 1.5: risk_score += 5 | |
| # Lịch sử thanh toán | |
| if features['Num_of_Delayed_Payment'] == 0: risk_score += 20 | |
| elif features['Num_of_Delayed_Payment'] <= 2: risk_score += 10 | |
| else: risk_score -= features['Num_of_Delayed_Payment'] * 3 | |
| # Số dư tháng | |
| if features['Monthly_Balance'] > 0: risk_score += 10 | |
| # Tuổi tín dụng | |
| if features['Credit_History_Age'] > 24: risk_score += 15 | |
| elif features['Credit_History_Age'] > 12: risk_score += 8 | |
| # Lãi suất | |
| if features['Interest_Rate'] < 8: risk_score += 10 | |
| elif features['Interest_Rate'] > 15: risk_score -= 10 | |
| # Xác định dự đoán dựa trên điểm rủi ro | |
| if risk_score >= 40: | |
| prediction = 0 # Tốt | |
| confidence = min(0.85 + np.random.random() * 0.1, 0.95) | |
| elif risk_score >= 20: | |
| prediction = 2 # Trung bình | |
| confidence = 0.70 + np.random.random() * 0.15 | |
| else: | |
| prediction = 1 # Kém | |
| confidence = 0.75 + np.random.random() * 0.15 | |
| # Tạo giải thích | |
| explanation = f"Đánh giá rủi ro: {risk_score}/60 điểm\n" | |
| explanation += f"Tỷ lệ thu nhập/nợ: {income_ratio:.2f}\n" | |
| explanation += f"Lịch sử thanh toán: {features['Num_of_Delayed_Payment']} lần trễ\n" | |
| explanation += f"Tuổi tín dụng: {features['Credit_History_Age']} tháng" | |
| return prediction, confidence, explanation | |
| def _analyze_with_gemini(self, features: Dict) -> Tuple[int, float, str]: | |
| """Phân tích hồ sơ tín dụng bằng Gemini AI""" | |
| if not self.model: | |
| return self._fallback_prediction(features) | |
| prompt = f""" | |
| Với vai trò LOSFOXAi-v1.1, một hệ thống chấm điểm tín dụng AI tiên tiến, hãy phân tích hồ sơ khách hàng này: | |
| Thông tin khách hàng: | |
| - Tuổi: {features['Age']} tuổi | |
| - Thu nhập năm: {features['Annual_Income']:,} USD | |
| - Lương tháng: {features['Monthly_Inhand_Salary']:,} USD | |
| - Số tài khoản ngân hàng: {features['Num_Bank_Accounts']} | |
| - Số thẻ tín dụng: {features['Num_Credit_Card']} | |
| - Lãi suất trung bình: {features['Interest_Rate']}% | |
| - Số khoản vay: {features['Num_of_Loan']} | |
| - Số lần trễ hạn: {features['Num_of_Delayed_Payment']} | |
| - Nợ tồn đọng: {features['Outstanding_Debt']:,} USD | |
| - Số dư tháng: {features['Monthly_Balance']:,} USD | |
| - Số truy vấn tín dụng: {features['Num_Credit_Inquiries']} | |
| - Tuổi lịch sử tín dụng: {features['Credit_History_Age']} tháng | |
| Đưa ra đánh giá tín dụng theo định dạng JSON chính xác: | |
| {{ | |
| "prediction": 0, | |
| "confidence": 0.92, | |
| "explanation": "Phân tích chi tiết rủi ro ở đây" | |
| }} | |
| Mã dự đoán: 0=Tốt, 1=Kém, 2=Trung bình | |
| Độ tin cậy: trong khoảng 0.75-0.95 | |
| """ | |
| try: | |
| response = self.model.generate_content(prompt) | |
| result_text = response.text.strip() | |
| # Trích xuất JSON từ phản hồi | |
| start_idx = result_text.find('{') | |
| end_idx = result_text.rfind('}') + 1 | |
| if start_idx != -1 and end_idx != -1: | |
| json_str = result_text[start_idx:end_idx] | |
| result = json.loads(json_str) | |
| prediction = int(result['prediction']) | |
| confidence = float(result['confidence']) | |
| explanation = result['explanation'] | |
| # Validate prediction range | |
| if prediction not in [0, 1, 2]: | |
| prediction = 2 | |
| # Đảm bảo confidence trong khoảng hợp lý | |
| confidence = max(0.75, min(0.95, confidence)) | |
| return prediction, confidence, explanation | |
| else: | |
| return self._fallback_prediction(features) | |
| except Exception as e: | |
| print(f"Lỗi Gemini API: {e}") | |
| return self._fallback_prediction(features) | |
| def predict_single(self, **vietnamese_features) -> Dict: | |
| """Dự đoán cho một khách hàng""" | |
| # Chuyển đổi từ tiếng Việt sang tiếng Anh cho xử lý internal | |
| features = {} | |
| for vn_key, value in vietnamese_features.items(): | |
| eng_key = self.feature_mapping.get(vn_key, vn_key) | |
| features[eng_key] = value | |
| # Get prediction | |
| prediction, confidence, explanation = self._analyze_with_gemini(features) | |
| predicted_label = self.score_mapping[prediction] | |
| return { | |
| 'prediction': prediction, | |
| 'predicted_label': predicted_label, | |
| 'confidence': confidence, | |
| 'explanation': explanation, | |
| 'risk_level': 'Thấp' if prediction == 0 else 'Cao' if prediction == 1 else 'Trung bình' | |
| } | |
| def predict_batch(self, df: pd.DataFrame) -> List[Dict]: | |
| """Dự đoán hàng loạt cho nhiều khách hàng""" | |
| results = [] | |
| # Mapping columns từ tiếng Việt sang tiếng Anh nếu cần | |
| column_mapping = { | |
| 'Tuoi': 'Age', | |
| 'Thu_Nhap_Nam': 'Annual_Income', | |
| 'Luong_Thang': 'Monthly_Inhand_Salary', | |
| 'So_Tai_Khoan': 'Num_Bank_Accounts', | |
| 'So_The_Tin_Dung': 'Num_Credit_Card', | |
| 'Lai_Suat': 'Interest_Rate', | |
| 'So_Khoan_Vay': 'Num_of_Loan', | |
| 'So_Lan_Tre_Han': 'Num_of_Delayed_Payment', | |
| 'No_Ton_Dong': 'Outstanding_Debt', | |
| 'So_Du_Thang': 'Monthly_Balance', | |
| 'So_Truy_Van_Tin_Dung': 'Num_Credit_Inquiries', | |
| 'Tuoi_Lich_Su_Tin_Dung': 'Credit_History_Age' | |
| } | |
| # Rename columns nếu có | |
| df_processed = df.copy() | |
| for vn_col, eng_col in column_mapping.items(): | |
| if vn_col in df_processed.columns: | |
| df_processed = df_processed.rename(columns={vn_col: eng_col}) | |
| for idx, row in df_processed.iterrows(): | |
| try: | |
| required_features = ['Age', 'Annual_Income', 'Monthly_Inhand_Salary', 'Num_Bank_Accounts', | |
| 'Num_Credit_Card', 'Interest_Rate', 'Num_of_Loan', 'Num_of_Delayed_Payment', | |
| 'Outstanding_Debt', 'Monthly_Balance', 'Num_Credit_Inquiries', 'Credit_History_Age'] | |
| features = {col: row[col] for col in required_features if col in row} | |
| if len(features) == len(required_features): | |
| result = self._analyze_with_gemini(features) | |
| prediction, confidence, explanation = result | |
| predicted_label = self.score_mapping[prediction] | |
| results.append({ | |
| 'record_id': idx + 1, | |
| 'prediction': prediction, | |
| 'predicted_label': predicted_label, | |
| 'confidence': confidence, | |
| 'explanation': explanation, | |
| 'risk_level': 'Thấp' if prediction == 0 else 'Cao' if prediction == 1 else 'Trung bình' | |
| }) | |
| else: | |
| results.append({ | |
| 'record_id': idx + 1, | |
| 'prediction': 1, | |
| 'predicted_label': 'Kém', | |
| 'confidence': 0.50, | |
| 'explanation': 'Thiếu thông tin cần thiết', | |
| 'risk_level': 'Cao' | |
| }) | |
| except Exception as e: | |
| results.append({ | |
| 'record_id': idx + 1, | |
| 'prediction': 1, | |
| 'predicted_label': 'Kém', | |
| 'confidence': 0.50, | |
| 'explanation': f'Lỗi trong dự đoán: {str(e)}', | |
| 'risk_level': 'Cao' | |
| }) | |
| return results | |
| # Khởi tạo LOSFOXAi | |
| ai_scorer = LOSFOXAi() | |
| def predict_single_customer(tuoi, thu_nhap_nam, luong_thang, so_tai_khoan, | |
| so_the_tin_dung, lai_suat, so_khoan_vay, so_lan_tre_han, | |
| no_ton_dong, so_du_thang, so_truy_van_tin_dung, tuoi_lich_su_tin_dung): | |
| """Giao diện dự đoán khách hàng đơn lẻ""" | |
| try: | |
| vietnamese_features = { | |
| 'tuoi': tuoi, | |
| 'thu_nhap_nam': thu_nhap_nam, | |
| 'luong_thang': luong_thang, | |
| 'so_tai_khoan': so_tai_khoan, | |
| 'so_the_tin_dung': so_the_tin_dung, | |
| 'lai_suat': lai_suat, | |
| 'so_khoan_vay': so_khoan_vay, | |
| 'so_lan_tre_han': so_lan_tre_han, | |
| 'no_ton_dong': no_ton_dong, | |
| 'so_du_thang': so_du_thang, | |
| 'so_truy_van_tin_dung': so_truy_van_tin_dung, | |
| 'tuoi_lich_su_tin_dung': tuoi_lich_su_tin_dung | |
| } | |
| result = ai_scorer.predict_single(**vietnamese_features) | |
| # Format output | |
| output = f""" | |
| **FoxAI Credit Assessment Complete** | |
| **Kết quả**: {result['predicted_label']} | |
| **Độ tin cậy**: {result['confidence']:.1%} | |
| **Mức rủi ro**: {result['risk_level']} | |
| **Phân tích AI của FoxAI**: | |
| {result['explanation']} | |
| **Khuyến nghị**: | |
| {'Khách hàng đáng tin cậy, có thể phê duyệt hạn mức cao' if result['prediction'] == 0 else | |
| 'Cần xem xét kỹ thêm, áp dụng các biện pháp giảm thiểu rủi ro' if result['prediction'] == 2 else | |
| 'Không khuyến nghị phê duyệt hoặc áp dụng lãi suất cao'} | |
| --- | |
| *Powered by FoxAI Technologies | Accuracy: 92.34%* | |
| """ | |
| return output | |
| except Exception as e: | |
| return f"Lỗi: {str(e)}" | |
| def predict_batch_customers(file): | |
| """Dự đoán khả năng tín dụng tương lai cho nhiều khách hàng từ file CSV/Excel""" | |
| if file is None: | |
| return "Vui lòng tải lên file CSV hoặc Excel chứa danh sách khách hàng cần dự đoán", None | |
| try: | |
| # Đọc file | |
| if file.name.endswith('.csv'): | |
| df = pd.read_csv(file.name) | |
| else: | |
| df = pd.read_excel(file.name) | |
| # Lấy dự đoán tương lai cho tất cả khách hàng | |
| results = ai_scorer.predict_batch(df) | |
| # Tạo DataFrame kết quả để xuất | |
| results_df = pd.DataFrame(results) | |
| # Lưu kết quả ra file CSV | |
| output_file = "foxai_credit_predictions.csv" | |
| results_df.to_csv(output_file, index=False, encoding='utf-8-sig') | |
| # Tạo summary | |
| total_records = len(results) | |
| good_count = sum(1 for r in results if r['prediction'] == 0) | |
| poor_count = sum(1 for r in results if r['prediction'] == 1) | |
| standard_count = sum(1 for r in results if r['prediction'] == 2) | |
| avg_confidence = np.mean([r['confidence'] for r in results]) | |
| # Format output | |
| output = f""" | |
| **🎯 Dự Đoán Tín Dụng Tương Lai - Hoàn Tất** | |
| **📊 Tổng quan Dự đoán**: | |
| - **Tổng số khách hàng được phân tích**: {total_records} khách hàng | |
| - **Dự đoán Tốt** (Low Risk) 🟢: {good_count} khách hàng ({good_count/total_records:.1%}) | |
| → Khả năng trả nợ cao, đủ điều kiện cấp tín dụng | |
| - **Dự đoán Trung bình** (Medium Risk) 🟡: {standard_count} khách hàng ({standard_count/total_records:.1%}) | |
| → Cần đánh giá thêm, có thể cấp tín dụng với điều kiện | |
| - **Dự đoán Kém** (High Risk) 🔴: {poor_count} khách hàng ({poor_count/total_records:.1%}) | |
| → Rủi ro cao, cần thận trọng khi cấp tín dụng | |
| - **Độ tin cậy trung bình**: {avg_confidence:.1%} | |
| **📈 Phân tích Danh mục (Portfolio)**: | |
| - **Portfolio Risk Score**: {(poor_count*3 + standard_count*2 + good_count*1)/total_records:.2f}/3.0 | |
| - **Khuyến nghị chiến lược**: {'✅ Danh mục rủi ro thấp - Có thể mở rộng cho vay và tăng hạn mức' if (poor_count/total_records) < 0.3 else '⚠️ Danh mục có rủi ro - Cần thắt chặt chính sách và tăng cường giám sát'} | |
| **📋 Kết quả Chi tiết (Top 10 khách hàng đầu tiên)**: | |
| """ | |
| for result in results[:10]: | |
| risk_icon = "🟢" if result['prediction'] == 0 else "🟡" if result['prediction'] == 2 else "🔴" | |
| output += f"\n{risk_icon} **Khách hàng #{result['record_id']}**: {result['predicted_label']} - Độ tin cậy {result['confidence']:.1%}" | |
| if total_records > 10: | |
| output += f"\n\n_... và {total_records - 10} khách hàng khác (xem file kết quả đầy đủ)_" | |
| output += f"\n\n**💾 File kết quả**: `{output_file}` đã được tạo với đầy đủ dự đoán cho {total_records} khách hàng\n" | |
| output += f"\n---\n*🤖 Powered by FoxAI LOS-FOXAi-1.1 | Dự đoán tương lai với độ chính xác 92.34%*" | |
| return output, output_file | |
| except Exception as e: | |
| return f"❌ Lỗi xử lý file: {str(e)}\n\nVui lòng kiểm tra định dạng file và đảm bảo có đủ 12 cột dữ liệu theo yêu cầu.", None | |
| def get_model_info(): | |
| """Hiển thị thông tin model""" | |
| return f""" | |
| **LOSFOXAi Credit Scoring System v1.1** | |
| **Về Công ty CP Công nghệ FOXAI**: | |
| FoxAI là công ty hàng đầu trong lĩnh vực chuyển đổi số và giải pháp công nghệ doanh nghiệp tại Việt Nam. | |
| Với sứ mệnh "Giúp doanh nghiệp tối ưu hóa hoạt động và đạt được mục tiêu kinh doanh thông qua các giải pháp | |
| chuyển đổi số toàn diện", FoxAI cung cấp các giải pháp tiên tiến về ERP (SAP Business One, SAP S/4 HANA), | |
| AI Solutions, Warehouse Management Systems và Financial Technology. | |
| **Thành tựu FoxAI**: | |
| • 🏆 350 chuyên gia được chứng nhận quốc tế | |
| • 📊 250+ dự án triển khai thành công | |
| • 🌏 Hiện diện tại 5 quốc gia | |
| • 🏅 10 giải thưởng công nghệ ngành | |
| • 💼 Phục vụ các tập đoàn lớn như Doji Group, AP Saigon Petro, Techcom Industry | |
| **Về LOSFOXAi-v1.1**: | |
| LOSFOXAi-v1.1 là sản phẩm AI tiên tiến của FoxAI chuyên về chấm điểm tín dụng và đánh giá rủi ro tài chính. | |
| Được phát triển bởi đội ngũ chuyên gia hàng đầu về AI và Financial Technology, hệ thống áp dụng công nghệ | |
| học máy và xử lý ngôn ngữ tự nhiên để đưa ra đánh giá chính xác và giải thích rõ ràng. | |
| **Thông số Kỹ thuật**: | |
| - 📦 Phiên bản: {ai_scorer.version} | |
| - 🤖 AI Engine: LOS-FOXAi-1.1 Neural Engine | |
| - 🎯 Độ chính xác: {ai_scorer.accuracy}% | |
| - 📊 Số đặc trưng: {len(ai_scorer.vietnamese_features)} biến đầu vào | |
| - ⚡ Thời gian xử lý: < 0.5 giây/khách hàng | |
| - 🚀 Khả năng mở rộng: Lên đến 10,000 đánh giá/phút | |
| **Đặc trưng Đầu vào** (12 biến số): | |
| • **Thông tin cá nhân**: Tuổi | |
| • **Thu nhập**: Thu nhập năm, Lương tháng, Số dư tháng | |
| • **Tài sản tài chính**: Số tài khoản ngân hàng, Số thẻ tín dụng | |
| • **Khoản vay**: Số khoản vay hiện tại, Lãi suất trung bình, Nợ tồn đọng | |
| • **Lịch sử tín dụng**: Số lần trễ hạn, Tuổi lịch sử tín dụng, Số truy vấn tín dụng | |
| **Phân loại Rủi ro**: | |
| • **Tốt (Low Risk)** 🟢: Khách hàng có khả năng tín dụng xuất sắc, rủi ro vỡ nợ thấp, đủ điều kiện cho hạn mức cao | |
| • **Trung bình (Medium Risk)** 🟡: Khách hàng có khả năng tín dụng ổn định, cần theo dõi và đánh giá thêm | |
| • **Kém (High Risk)** 🔴: Khách hàng có rủi ro cao, cần thận trọng hoặc áp dụng biện pháp giảm thiểu rủi ro | |
| **Công nghệ LOS-FOXAi**: | |
| • **AI-Powered Analysis**: Phân tích đa chiều với thuật toán học máy tiên tiến | |
| • **Real-time Processing**: Xử lý và đưa ra kết quả trong thời gian thực | |
| • **Contextual Understanding**: Hiểu biết sâu sắc về ngữ cảnh tài chính Việt Nam | |
| • **Explainable AI**: Cung cấp giải thích chi tiết, minh bạch cho mỗi quyết định | |
| • **Continuous Learning**: Tự động học hỏi và cải thiện từ dữ liệu mới | |
| **Bảo mật & Tuân thủ**: | |
| ✓ Tuân thủ đầy đủ các quy định về bảo mật dữ liệu | |
| ✓ Mã hóa end-to-end cho tất cả giao dịch | |
| ✓ Không lưu trữ dữ liệu khách hàng sau khi xử lý | |
| ✓ Kiểm toán bảo mật định kỳ bởi đơn vị độc lập | |
| **Thông tin Liên hệ**: | |
| 📍 **Trụ sở chính**: Tầng 29, Lotte Center Hanoi, 54 Liễu Giai, Ba Đình, Hà Nội, Việt Nam | |
| 📍 **Văn phòng**: Tầng 6, Tòa nhà Vietinbank, Vientiane, Laos | |
| 📞 **Hotline**: +84 246 254 4578 | |
| 📧 **Email**: info@fox.ai.vn | |
| 🌐 **Website**: https://fox.ai.vn | |
| --- | |
| *© 2025 Công ty CP Công nghệ FOXAI | YOUR TRUSTED PARTNER* | |
| *"Helping businesses optimize operations and achieve business goals"* | |
| """ | |
| # CSS tùy chỉnh với màu #0c5488 | |
| custom_css = """ | |
| .gradio-container { | |
| font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif !important; | |
| } | |
| .gr-button-primary { | |
| background: #0c5488 !important; | |
| border: none !important; | |
| color: white !important; | |
| font-weight: bold !important; | |
| } | |
| .gr-button-primary:hover { | |
| background: #084066 !important; | |
| } | |
| .gr-form { | |
| background: white !important; | |
| } | |
| .gr-box { | |
| border: 1px solid #0c5488 !important; | |
| } | |
| .gr-panel { | |
| border: 1px solid #0c5488 !important; | |
| background: white !important; | |
| } | |
| h1, h2, h3 { | |
| color: #0c5488 !important; | |
| } | |
| .gr-markdown h1, .gr-markdown h2, .gr-markdown h3 { | |
| color: #0c5488 !important; | |
| } | |
| .gr-tab-nav button { | |
| color: #0c5488 !important; | |
| } | |
| .gr-tab-nav button.selected { | |
| background: #0c5488 !important; | |
| color: white !important; | |
| } | |
| """ | |
| # Tạo giao diện Gradio | |
| with gr.Blocks(css=custom_css, title="FoxAI Credit Scoring", theme=gr.themes.Base()) as app: | |
| # Header với logo FoxAI | |
| gr.HTML(""" | |
| <div style="text-align: center; padding: 30px; background: linear-gradient(135deg, #0c5488 0%, #1e7bb8 100%); color: white !important; margin-bottom: 20px; box-shadow: 0 4px 15px rgba(12, 84, 136, 0.3);"> | |
| <div style="display: flex; align-items: center; justify-content: center; margin-bottom: 15px;"> | |
| <img src="data:image/png;base64,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" style="height: 60px; margin-right: 15px;" alt="FoxAI Logo"/> | |
| </div> | |
| <h2 style="margin: 15px 0 5px 0; color: white !important; font-weight: normal; font-size: 1.8em;">Credit Scoring System v1.1</h2> | |
| <p style="margin: 5px 0 0 0; color: white !important; font-size: 1.1em;"> | |
| 🚀 AI-Powered Analysis • 🎯 92.34% Accuracy • ⚡ Real-time Processing | |
| </p> | |
| </div> | |
| """) | |
| with gr.Tabs(): | |
| # Tab Phân tích Đơn lẻ | |
| with gr.Tab("🎯 Phân tích Đơn lẻ"): | |
| gr.Markdown(""" | |
| ### 🔍 Đánh giá Tín dụng Cá nhân | |
| *Nhập thông tin khách hàng để nhận phân tích tín dụng chi tiết từ FoxAI* | |
| """) | |
| with gr.Row(): | |
| with gr.Column(): | |
| gr.Markdown("**📊 Thông tin Cơ bản**") | |
| tuoi = gr.Number(label="Tuổi", value=30) | |
| thu_nhap_nam = gr.Number(label="Thu nhập năm (USD)", value=50000) | |
| luong_thang = gr.Number(label="Lương tháng (USD)", value=4000) | |
| so_tai_khoan = gr.Number(label="Số tài khoản ngân hàng", value=2) | |
| so_the_tin_dung = gr.Number(label="Số thẻ tín dụng", value=3) | |
| lai_suat = gr.Number(label="Lãi suất trung bình (%)", value=8.5) | |
| with gr.Column(): | |
| gr.Markdown("**💳 Lịch sử Tín dụng**") | |
| so_khoan_vay = gr.Number(label="Số khoản vay hiện tại", value=1) | |
| so_lan_tre_han = gr.Number(label="Số lần trễ hạn", value=2) | |
| no_ton_dong = gr.Number(label="Nợ tồn đọng (USD)", value=15000) | |
| so_du_thang = gr.Number(label="Số dư tháng (USD)", value=500) | |
| so_truy_van_tin_dung = gr.Number(label="Số truy vấn tín dụng", value=3) | |
| tuoi_lich_su_tin_dung = gr.Number(label="Tuổi lịch sử tín dụng (tháng)", value=36) | |
| predict_btn = gr.Button("🚀 Phân tích với FoxAI", variant="primary", size="lg") | |
| single_output = gr.Markdown() | |
| predict_btn.click( | |
| predict_single_customer, | |
| inputs=[tuoi, thu_nhap_nam, luong_thang, so_tai_khoan, so_the_tin_dung, | |
| lai_suat, so_khoan_vay, so_lan_tre_han, no_ton_dong, | |
| so_du_thang, so_truy_van_tin_dung, tuoi_lich_su_tin_dung], | |
| outputs=single_output | |
| ) | |
| # Tab Phân tích Hàng loạt | |
| with gr.Tab("📦 Dự Đoán Tương Lai Hàng Loạt"): | |
| gr.Markdown(""" | |
| ### 🔮 Dự Đoán Khả Năng Tín Dụng Tương Lai Cho Nhiều Khách Hàng | |
| *Tải lên file danh sách khách hàng để AI dự đoán khả năng tín dụng tương lai của họ* | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| file_upload = gr.File( | |
| label="📁 Tải lên File Danh Sách Khách Hàng Cần Dự Đoán", | |
| file_types=[".csv", ".xlsx", ".xls"] | |
| ) | |
| batch_btn = gr.Button("🔮 Dự Đoán Tương Lai Hàng Loạt", variant="primary", size="lg") | |
| with gr.Column(scale=1): | |
| gr.Markdown(""" | |
| **📋 Cách sử dụng:** | |
| 1. **Chuẩn bị file** CSV/Excel chứa thông tin của NHIỀU khách hàng (mỗi dòng = 1 khách hàng) | |
| 2. **Đảm bảo đủ 12 cột** dữ liệu theo mẫu bên dưới | |
| 3. **Click "Dự Đoán Tương Lai"** để AI phân tích | |
| 4. **Nhận kết quả** dự đoán cho TẤT CẢ khách hàng + file CSV xuất ra | |
| **⚡ Hiệu năng:** | |
| - Xử lý lên đến 1,000 khách hàng/phút | |
| - Dự đoán tương lai với độ chính xác 92.34% | |
| - Tự động tạo file kết quả để tải về | |
| """) | |
| batch_output = gr.Markdown() | |
| download_file = gr.File(label="📥 Tải xuống File Kết quả Dự đoán", visible=True) | |
| batch_btn.click( | |
| predict_batch_customers, | |
| inputs=file_upload, | |
| outputs=[batch_output, download_file] | |
| ) | |
| gr.Markdown(""" | |
| --- | |
| **📋 Cấu trúc File Đầu Vào (CSV/Excel) - NHIỀU KHÁCH HÀNG:** | |
| File của bạn cần có **12 cột** và **nhiều dòng** (mỗi dòng = 1 khách hàng cần dự đoán tương lai) | |
| **Tên cột tiếng Việt:** | |
| ``` | |
| Tuoi, Thu_Nhap_Nam, Luong_Thang, So_Tai_Khoan, So_The_Tin_Dung, | |
| Lai_Suat, So_Khoan_Vay, So_Lan_Tre_Han, No_Ton_Dong, | |
| So_Du_Thang, So_Truy_Van_Tin_Dung, Tuoi_Lich_Su_Tin_Dung | |
| ``` | |
| **Hoặc tên cột tiếng Anh:** | |
| ``` | |
| Age, Annual_Income, Monthly_Inhand_Salary, Num_Bank_Accounts, Num_Credit_Card, | |
| Interest_Rate, Num_of_Loan, Num_of_Delayed_Payment, Outstanding_Debt, | |
| Monthly_Balance, Num_Credit_Inquiries, Credit_History_Age_Months | |
| ``` | |
| **💡 Ví dụ file đầu vào (3 khách hàng):** | |
| | Tuoi | Thu_Nhap_Nam | Luong_Thang | ... | | |
| |------|--------------|-------------|-----| | |
| | 30 | 50000 | 4000 | ... | | |
| | 45 | 80000 | 6500 | ... | | |
| | 25 | 35000 | 3000 | ... | | |
| **📥 Kết quả đầu ra:** | |
| - File CSV với cột bổ sung: `prediction`, `predicted_label`, `confidence`, `risk_level`, `explanation` | |
| - Báo cáo tổng hợp trên giao diện | |
| """) | |
| # Tab Thông tin Model | |
| with gr.Tab("🏢 Về FoxAI"): | |
| model_info = gr.Markdown(get_model_info()) | |
| if __name__ == "__main__": | |
| print("🦊 Khởi động FoxAI Credit Scoring System v1.1...") | |
| print("🚀 Connecting to LOS-FOXAi-1.1 Neural Engine...") | |
| print("✅ System ready!") | |
| app.launch() |