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.env

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+API_URL=http://localhost:8000
+# Change this to your deployed API URL in production
+# API_URL=https://your-api-url.onrender.com 

.gitattributes

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.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+env/
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+ENV/
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# OS
+.DS_Store
+Thumbs.db
+
+# Logs
+*.log
+
+# Local development
+.env
+.env.local
+
+# Database
+*.db
+
+# Model files
+models/
+*.joblib
+*.pkl
+*.h5
+*.model
+
+# Git LFS
+.gitattributes 

Dockerfile

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+FROM python:3.10-slim
+
+WORKDIR /code
+
+COPY ./requirements.txt /code/requirements.txt
+COPY ./src /code/src
+COPY ./models /code/models
+COPY ./data /code/data
+
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+EXPOSE 7860
+
+CMD ["streamlit", "run", "src/frontend/app.py", "--server.address", "0.0.0.0", "--server.port", "7860"]

README.md

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----
-title: Synthack SyntaxSquad
-emoji: 🚀
-colorFrom: yellow
-colorTo: purple
-sdk: streamlit
-sdk_version: 1.44.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: AI Prediction Dashboard
+emoji: 🎯
+colorFrom: blue
+colorTo: indigo
+sdk: streamlit
+sdk_version: 1.32.0
+app_file: src/frontend/app.py
+pinned: false
+---
+
+# AI Prediction Dashboard
+
+A comprehensive AI-powered prediction dashboard that provides insights into:
+- Loan Approval Predictions
+- Employee Attrition Analysis
+- Healthcare Risk Assessment (Diabetes and Liver Disease)
+
+## Features
+- Interactive prediction interfaces
+- Detailed explanations and visualizations
+- Real-time risk assessment
+- Personalized recommendations
+
+## How to Use
+1. Select a prediction model from the dashboard
+2. Input the required information
+3. Get instant predictions with detailed analysis
+4. View personalized recommendations
+
+## Technical Details
+- Built with Streamlit
+- Powered by machine learning models
+- Real-time API integration
+- Interactive visualizations using Plotly
+
+## Technologies
+
+- Python
+- Streamlit
+- FastAPI
+- Scikit-learn
+- Pandas
+- NumPy
+- Plotly
+
+## Setup
+
+1. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+2. Run the api:
+```bash
+cd src/api
+python -m uvicorn main:app --reload
+```
+
+3. Run the application:
+```bash
+cd src/frontend
+streamlit run app.py
+```
+
+## Models
+
+The system includes pre-trained models for:
+- Loan approval prediction
+- Employee attrition prediction
+- Healthcare predictions (diabetes and liver disease)
+
+## Contributors
+
+- Team Syntax Squad

data/diabetes.csv

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+Pregnancies,Glucose,BloodPressure,SkinThickness,Insulin,BMI,DiabetesPedigreeFunction,Age,Outcome
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data/indian_liver_patient.csv

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docker-compose.yml

@@ -0,0 +1,13 @@
+version: '3'
+
+services:
+  xai-assist:
+    build: .
+    ports:
+      - "8000:8000"  # API
+      - "8501:8501"  # Streamlit
+    volumes:
+      - ./data:/app/data
+    environment:
+      - DATABASE_URL=sqlite:///./data/xai_assist.db
+    restart: unless-stopped

loan_applications.db

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d1b1872ab6ad008019d6b5e082cfcfaa7551c3e5aa3c954f63f73156d04e932e
+size 131

models/__init__.py

@@ -0,0 +1,4 @@
+"""
+Models package for the Synthack project.
+Contains machine learning models for loan approval, employee attrition, and healthcare predictions.
+""" 

models/__pycache__/__init__.cpython-311.pyc

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+version https://git-lfs.github.com/spec/v1
+oid sha256:3028f8bf21cd9f81dd3561f5354f370668acae256504105bd7b477caed9a338d
+size 128

models/attrition_model.joblib

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+version https://git-lfs.github.com/spec/v1
+oid sha256:78c82401b88dd086cad9e96a9c5c32c1e77855b988fe1424dde056426be3db49
+size 132

models/attrition_model.pkl

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models/loan_scaler.joblib

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roadmap.txt

@@ -0,0 +1,77 @@
+🚀 Project Title: XAI-Assist – Explainable AI for Critical Decision Support
+🎯 Problem Statement
+In high-stakes fields like Healthcare, Finance, and Legal Tech, AI-driven decisions can be black-boxed and hard to trust. Professionals (doctors, loan officers, lawyers) need a transparent AI system that provides clear, human-readable explanations for its decisions.
+
+✅ Objective
+Develop an Explainable AI decision support system that:
+
+Makes predictions (diagnosis, loan approval, legal outcomes).
+
+Explains why it made that decision using visual + textual insights.
+
+Allows experts to tweak or simulate decisions based on feature changes.
+
+💡 Project Scope & Use Cases
+Pick one of these (or build a general framework):
+
+Domain	Use Case	Example Prediction
+🏥 Healthcare	Disease Risk Prediction	"Will this patient develop diabetes in 5 years?"
+💰 Finance	Loan Approval System	"Should this applicant get a loan?"
+⚖️ Legal Tech	Case Outcome Prediction	"Will the court rule in favor of the defendant?"
+🔍 Core Features
+🔹 1. Model Transparency & Explainability
+Use SHAP, LIME, or RuleFit to explain AI predictions.
+
+Generate visual feature importance charts (SHAP force plots, waterfall plots).
+
+Provide natural language explanations like:
+"Loan denied due to low income ($20k), high debt-to-income ratio (40%), and low credit score (580)."
+
+🔹 2. Interactive "What-If" Analysis
+Allow users to change feature values and see how decisions change.
+
+Example: "If the income was $30k instead of $20k, the loan would have been approved."
+
+🔹 3. Comparative Decision Insights
+Compare two similar cases with different outcomes and highlight why.
+
+Example (Loan Application):
+
+Applicant A (Denied): Income = $20k, Credit Score = 580
+
+Applicant B (Approved): Income = $50k, Credit Score = 720
+
+Key Insight: Income and credit score had the biggest impact.
+
+🔹 4. Trust Score & Human Override System
+Show a Trust Score (how confident the AI is in its decision).
+
+Allow human experts to override AI decisions and provide a reason.
+
+Store overrides for model auditing and bias detection.
+
+⚙️ Tech Stack
+Component	Tech
+💻 Frontend	Streamlit / ReactJS for UI
+🧠 AI Model	Random Forest, XGBoost, or Neural Networks
+🔎 Explainability	SHAP, LIME, ELI5, Fairlearn
+📊 Visualization	Matplotlib, Plotly, SHAP force plots
+📦 Database	PostgreSQL / Firebase (for saving decisions & overrides)
+🎯 Why This Can Win the Hackathon
+✅ Highly relevant & ethical – Explainability is a hot topic in AI.
+✅ Real-world impact – Can be applied in multiple critical sectors.
+✅ Great UI & Visuals – Judges love interactive dashboards & visual explanations.
+✅ Customizable & expandable – Can work in healthcare, finance, or law.
+
+🎁 Bonus Features (If Time Allows)
+🚀 Bias Detection: Show if certain groups (e.g., women, minorities) are unfairly impacted.
+🚀 Explainable Chatbot: An AI chatbot that explains decisions interactively.
+🚀 PDF Report Generator: Generate a summary report of decisions and explanations.
+
+💬 Next Steps
+Do you want help with:
+✅ Setting up a GitHub repo with boilerplate code?
+✅ Designing an interactive UI mockup?
+✅ Choosing a specific use-case (health, finance, law)?
+
+I can help you with any of these! 🚀

src/__init__.py

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+"""
+Source package for the Synthack project.
+Contains the main application code, API endpoints, and data processing modules.
+""" 

src/__pycache__/__init__.cpython-311.pyc

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src/api/__init__.py

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+"""
+API package for the Synthack project.
+Contains FastAPI endpoints and related functionality.
+""" 

src/api/__pycache__/__init__.cpython-311.pyc

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src/api/attrition_model.py

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+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler, OneHotEncoder
+from sklearn.compose import ColumnTransformer
+from sklearn.pipeline import Pipeline
+from sklearn.ensemble import RandomForestClassifier
+import pickle
+import os
+import sys
+from typing import List
+
+# Add the project root to Python path
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+sys.path.append(project_root)
+
+class AttritionModel:
+    def __init__(self):
+        self.model = None
+        self.preprocessor = None
+        self.model_path = os.path.join(project_root, "models", "attrition_model.pkl")
+        self.preprocessor_path = os.path.join(project_root, "models", "attrition_preprocessor.pkl")
+        
+        # Create models directory if it doesn't exist
+        os.makedirs(os.path.dirname(self.model_path), exist_ok=True)
+        
+        # Define the features we'll use
+        self.numeric_features = [
+            'Age', 'DistanceFromHome', 'EnvironmentSatisfaction',
+            'JobLevel', 'JobSatisfaction', 'MonthlyIncome',
+            'TotalWorkingYears', 'WorkLifeBalance', 'YearsAtCompany'
+        ]
+        self.categorical_features = ['OverTime']
+        
+        # Try to load existing model and preprocessor
+        try:
+            with open(self.model_path, 'rb') as f:
+                self.model = pickle.load(f)
+            with open(self.preprocessor_path, 'rb') as f:
+                self.preprocessor = pickle.load(f)
+        except:
+            print("No existing model found. Please train the model first.")
+    
+    def preprocess_data(self, X):
+        """Preprocess the input data"""
+        # Create preprocessing steps for numeric and categorical data
+        numeric_transformer = StandardScaler()
+        categorical_transformer = OneHotEncoder(drop='first', sparse=False)
+        
+        # Combine preprocessing steps
+        self.preprocessor = ColumnTransformer(
+            transformers=[
+                ('num', numeric_transformer, self.numeric_features),
+                ('cat', categorical_transformer, self.categorical_features)
+            ],
+            remainder='drop'  # Drop any columns not specified in features
+        )
+        
+        return self.preprocessor.fit_transform(X)
+    
+    def train(self, X, y):
+        """Train the model with the given data"""
+        # Preprocess the data
+        X_processed = self.preprocess_data(X)
+        
+        # Create and train the model
+        self.model = RandomForestClassifier(
+            n_estimators=100,
+            max_depth=10,
+            random_state=42
+        )
+        self.model.fit(X_processed, y)
+        
+        # Save the model and preprocessor
+        with open(self.model_path, 'wb') as f:
+            pickle.dump(self.model, f)
+        with open(self.preprocessor_path, 'wb') as f:
+            pickle.dump(self.preprocessor, f)
+    
+    def predict(self, features):
+        """Make a prediction using the trained model."""
+        try:
+            if self.model is None:
+                raise ValueError("Model not loaded. Please ensure model file exists and is valid.")
+            
+            print(f"Input features: {features}")
+            
+            # Convert string inputs to appropriate types
+            processed_features = {}
+            for key, value in features.items():
+                if key == 'OverTime':
+                    # Convert 'Yes'/'No' to 1/0
+                    if isinstance(value, str):
+                        processed_features[key] = 1 if value.lower() in ['yes', 'true', '1'] else 0
+                    else:
+                        processed_features[key] = 1 if value else 0
+                else:
+                    # Convert other values to appropriate numeric types
+                    try:
+                        processed_features[key] = float(value)
+                    except (ValueError, TypeError):
+                        # Handle conversion errors
+                        raise ValueError(f"Invalid value for feature {key}: {value}. Expected numeric value.")
+            
+            print(f"Processed features: {processed_features}")
+            
+            # Create DataFrame with processed values
+            X = pd.DataFrame([processed_features])
+            
+            # Ensure all required columns are present
+            required_columns = self.numeric_features + self.categorical_features
+            
+            for col in required_columns:
+                if col not in X.columns:
+                    raise ValueError(f"Missing required feature: {col}")
+            
+            # Ensure columns are in the correct order for the preprocessor
+            X = X[required_columns]
+            
+            # Debug information
+            print(f"Input data types before conversion: {X.dtypes}")
+            
+            # Convert all numeric columns to float64
+            for col in self.numeric_features:
+                X[col] = pd.to_numeric(X[col], errors='coerce').astype(np.float64)
+            
+            # Convert categorical columns to appropriate types
+            for col in self.categorical_features:
+                X[col] = X[col].astype(np.int64)
+            
+            print(f"Input data types after conversion: {X.dtypes}")
+            print(f"Input data: {X.to_dict('records')}")
+            
+            # Check for NaN values
+            if X.isnull().any().any():
+                print(f"Warning: NaN values detected in input: {X.isnull().sum()}")
+                # Fill NaN values with appropriate defaults
+                X = X.fillna(X.mean())
+            
+            # Use preprocessor
+            if self.preprocessor is not None:
+                try:
+                    X_processed = self.preprocessor.transform(X)
+                    print("Preprocessing successful")
+                except Exception as e:
+                    print(f"Error during preprocessing: {str(e)}")
+                    # Try direct prediction without preprocessing as fallback
+                    try:
+                        # For direct prediction, we need to handle categorical features manually
+                        # Convert 'OverTime' to one-hot encoding manually
+                        X_direct = X.copy()
+                        X_direct['OverTime_Yes'] = X_direct['OverTime']
+                        X_direct = X_direct.drop('OverTime', axis=1)
+                        
+                        # Make prediction with direct features
+                        prediction = bool(self.model.predict(X_direct.values)[0])
+                        probability = float(self.model.predict_proba(X_direct.values)[0][1])
+                        
+                        print("Used direct prediction as fallback")
+                        return {
+                            "prediction": prediction,
+                            "probability": probability
+                        }
+                    except Exception as direct_error:
+                        print(f"Direct prediction also failed: {str(direct_error)}")
+                        raise ValueError(f"Failed to process input data: {str(e)}")
+            else:
+                # If no preprocessor, just use the raw values
+                X_processed = X.values
+                print("No preprocessor available, using raw values")
+            
+            # Make prediction
+            prediction = bool(self.model.predict(X_processed)[0])
+            probability = float(self.model.predict_proba(X_processed)[0][1])
+            
+            print(f"Prediction result: {prediction}, probability: {probability}")
+            
+            return {
+                "prediction": prediction,
+                "probability": probability
+            }
+            
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            raise ValueError(f"Error during prediction: {str(e)}")
+
+    def get_feature_importance(self) -> List[float]:
+        """Get the feature importance scores as a list of floats."""
+        try:
+            if hasattr(self.model, 'feature_importances_'):
+                # Convert feature importances to a list of floats
+                return [float(x) for x in self.model.feature_importances_]
+            return None
+        except Exception as e:
+            print(f"Error getting feature importance: {str(e)}")
+            return None
+
+def train_model():
+    """Train and save the attrition prediction model"""
+    try:
+        model = AttritionModel()
+        
+        # Get absolute paths
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        project_root = os.path.dirname(os.path.dirname(current_dir))
+        data_file = os.path.join(project_root, "data", "HR-Employee-Attrition.csv")
+        model_dir = os.path.join(project_root, 'models')
+        
+        print(f"Loading data from: {data_file}")
+        print(f"Model will be saved to: {model_dir}")
+        
+        # Ensure data file exists
+        if not os.path.exists(data_file):
+            raise FileNotFoundError(f"Data file not found at {data_file}")
+        
+        # Create models directory if it doesn't exist
+        os.makedirs(model_dir, exist_ok=True)
+        
+        # Load data
+        print("Loading and preparing data...")
+        data = pd.read_csv(data_file)
+        
+        # Select only the features we want to use
+        features = model.numeric_features + model.categorical_features
+        print(f"Using features: {features}")
+        
+        X = data[features]
+        y = data['Attrition'].map({'Yes': 1, 'No': 0})
+        
+        # Train the model
+        print("Training model...")
+        model.train(X, y)
+        print("Model trained and saved successfully")
+        
+    except Exception as e:
+        print(f"Error during model training: {str(e)}")
+        import traceback
+        print(traceback.format_exc())
+        sys.exit(1)
+
+if __name__ == "__main__":
+    train_model()

src/api/diabetes_model.py

@@ -0,0 +1,320 @@
+import pandas as pd
+import numpy as np
+import pickle
+import os
+import sys
+import logging
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from typing import List
+
+logger = logging.getLogger(__name__)
+
+class DiabetesModel:
+    def __init__(self):
+        self.model = None
+        self.scaler = None
+        self.feature_names = None
+        self.model_metrics = None
+        
+        # Get the project root directory
+        self.project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        
+        # Set paths for model files
+        self.model_path = os.path.join(self.project_root, 'models', 'diabetes_model.pkl')
+        self.feature_names_path = os.path.join(self.project_root, 'models', 'diabetes_feature_names.pkl')
+        self.model_metrics_path = os.path.join(self.project_root, 'models', 'diabetes_model_metrics.pkl')
+        
+        # Default feature names if not loaded from file
+        self.default_feature_names = [
+            'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness',
+            'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'
+        ]
+        
+        # Initialize feature names first
+        self.feature_names = self.default_feature_names
+        
+        # Load the model and related files
+        self.load_model()
+
+    def load_model(self):
+        """Load the trained model and related files from disk."""
+        try:
+            # Try to load feature names first
+            if os.path.exists(self.feature_names_path):
+                try:
+                    with open(self.feature_names_path, 'rb') as f:
+                        self.feature_names = pickle.load(f, encoding='latin1')
+                        logger.info("Feature names loaded successfully")
+                except Exception as e:
+                    logger.warning(f"Error loading feature names: {str(e)}. Using defaults.")
+                    self.feature_names = self.default_feature_names
+            else:
+                logger.warning("Feature names file not found, using defaults")
+                self.feature_names = self.default_feature_names
+        
+            # Try to load the model
+            if os.path.exists(self.model_path):
+                try:
+                    with open(self.model_path, 'rb') as f:
+                        model_data = pickle.load(f, encoding='latin1')
+                        if isinstance(model_data, dict):
+                            self.model = model_data.get('model')
+                            self.scaler = model_data.get('scaler')
+                            if self.model is None or self.scaler is None:
+                                raise ValueError("Model or scaler missing from loaded data")
+                        else:
+                            self.model = model_data
+                            # Create a new scaler if not found in model data
+                            self.scaler = StandardScaler()
+                            logger.warning("Model loaded but scaler not found. Creating new scaler.")
+                    logger.info("Model loaded successfully")
+                except Exception as e:
+                    logger.error(f"Error loading model: {str(e)}")
+                    raise ValueError(f"Failed to load diabetes model: {str(e)}")
+            else:
+                logger.error("Model file not found.")
+                raise FileNotFoundError(f"Diabetes model file not found at {self.model_path}")
+        
+            # Try to load model metrics
+            if os.path.exists(self.model_metrics_path):
+                try:
+                    with open(self.model_metrics_path, 'rb') as f:
+                        self.model_metrics = pickle.load(f, encoding='latin1')
+                        logger.info("Model metrics loaded successfully")
+                except Exception as e:
+                    logger.warning(f"Error loading model metrics: {str(e)}")
+                    self.model_metrics = None
+            else:
+                logger.warning("Model metrics file not found")
+                self.model_metrics = None
+        except Exception as e:
+            logger.error(f"Error in load_model: {str(e)}")
+            raise ValueError(f"Failed to load diabetes model: {str(e)}")
+    
+    # Remove the _create_dummy_model method entirely
+    def _create_dummy_model(self):
+        """Create a dummy model for testing purposes."""
+        try:
+            logger.warning("Creating dummy model")
+            self.model = RandomForestClassifier(n_estimators=100, random_state=42)
+            self.scaler = StandardScaler()
+            
+            # Create dummy data to fit the scaler and model
+            dummy_data = pd.DataFrame(np.random.randn(100, len(self.feature_names)), 
+                                    columns=self.feature_names)
+            self.scaler.fit(dummy_data)
+            
+            # Fit the model with dummy data
+            dummy_target = np.random.randint(0, 2, 100)
+            self.model.fit(dummy_data, dummy_target)
+            logger.info("Dummy model created successfully")
+        except Exception as e:
+            logger.error(f"Error creating dummy model: {str(e)}")
+            raise
+
+    def save_model(self):
+        """Save the model and scaler together in one file."""
+        try:
+            # Create a dictionary containing both model and scaler
+            model_data = {
+                'model': self.model,
+                'scaler': self.scaler
+            }
+            
+            # Save to file
+            with open(self.model_path, 'wb') as f:
+                pickle.dump(model_data, f)
+            logger.info("Model and scaler saved successfully")
+            
+        except Exception as e:
+            logger.error(f"Error saving model: {str(e)}")
+            raise
+
+    def predict(self, features):
+        """Make a prediction using the trained model."""
+        try:
+            if self.model is None:
+                raise ValueError("Model not loaded. Please ensure model file exists and is valid.")
+            
+            print(f"Input features for diabetes prediction: {features}")
+            
+            # Convert string inputs to appropriate numeric types
+            processed_features = {}
+            for key, value in features.items():
+                try:
+                    processed_features[key] = float(value)
+                except (ValueError, TypeError):
+                    # Handle conversion errors
+                    raise ValueError(f"Invalid value for feature {key}: {value}. Expected numeric value.")
+            
+            # Create DataFrame with processed values
+            X = pd.DataFrame([processed_features])
+            
+            # Ensure all required columns are present
+            required_columns = [
+                'Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 
+                'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'
+            ]
+            
+            for col in required_columns:
+                if col not in X.columns:
+                    raise ValueError(f"Missing required feature: {col}")
+            
+            # Ensure columns are in the correct order
+            X = X[required_columns]
+            
+            # Convert all data to float64 to ensure compatibility
+            X = X.astype(float)
+            
+            # Scale features if scaler is available
+            if hasattr(self, 'scaler') and self.scaler is not None:
+                X_scaled = self.scaler.transform(X)
+            else:
+                X_scaled = X.values
+            
+            # Make prediction
+            prediction = bool(self.model.predict(X_scaled)[0])
+            
+            # Get probability - handle different model types
+            if hasattr(self.model, 'predict_proba'):
+                # For models that provide probability
+                proba = self.model.predict_proba(X_scaled)[0]
+                # Make sure we get the probability for the positive class (index 1)
+                probability = float(proba[1]) if len(proba) > 1 else float(proba[0])
+            else:
+                # For models that don't provide probability
+                probability = 0.5 + (float(self.model.decision_function(X_scaled)[0]) / 10)
+                probability = max(0, min(1, probability))  # Clamp between 0 and 1
+            
+            return {
+                "prediction": prediction,
+                "probability": probability
+            }
+            
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            raise ValueError(f"Error during prediction: {str(e)}")
+
+    def get_feature_importance(self) -> List[float]:
+        """Get the feature importance scores as a list of floats."""
+        try:
+            if hasattr(self.model, 'feature_importances_'):
+                # Convert feature importances to a list of floats
+                importances = [float(x) for x in self.model.feature_importances_]
+                # Ensure we have the same number of importances as features
+                if len(importances) == len(self.feature_names):
+                    return importances
+            # If we can't get valid feature importances, return None
+            logger.warning("Could not get valid feature importances")
+            return None
+        except Exception as e:
+            logger.error(f"Error getting feature importance: {str(e)}")
+            return None
+
+    def get_model_metrics(self):
+        """Get the model metrics."""
+        return self.model_metrics if self.model_metrics else None
+
+    def train_model(self, X, y):
+        """Train the model with the given data."""
+        try:
+            logger.info("Starting model training...")
+            
+            # Initialize the scaler and scale the features
+            self.scaler = StandardScaler()
+            X_scaled = self.scaler.fit_transform(X)
+            
+            # Initialize and train the model
+            self.model = RandomForestClassifier(
+                n_estimators=100,
+                max_depth=10,
+                random_state=42
+            )
+            self.model.fit(X_scaled, y)
+            
+            # Calculate and store model metrics
+            train_score = self.model.score(X_scaled, y)
+            feature_importance = self.model.feature_importances_
+            
+            self.model_metrics = {
+                'train_score': train_score,
+                'feature_importance': feature_importance.tolist()
+            }
+            
+            # Save the model, scaler, and metrics
+            self.save_model()
+            self.save_metrics()
+            self.save_feature_names()
+            
+            logger.info(f"Model trained successfully. Training score: {train_score:.4f}")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error in train_model: {str(e)}")
+            raise
+
+    def save_metrics(self):
+        """Save model metrics to file."""
+        try:
+            with open(self.model_metrics_path, 'wb') as f:
+                pickle.dump(self.model_metrics, f)
+            logger.info("Model metrics saved successfully")
+        except Exception as e:
+            logger.error(f"Error saving model metrics: {str(e)}")
+            raise
+
+    def save_feature_names(self):
+        """Save feature names to file."""
+        try:
+            with open(self.feature_names_path, 'wb') as f:
+                pickle.dump(self.feature_names, f)
+            logger.info("Feature names saved successfully")
+        except Exception as e:
+            logger.error(f"Error saving feature names: {str(e)}")
+            raise
+
+def train_model():
+    """Train and save the diabetes prediction model"""
+    try:
+        model = DiabetesModel()
+        
+        # Get absolute paths
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        project_root = os.path.dirname(os.path.dirname(current_dir))
+        data_file = os.path.join(project_root, "data", "diabetes.csv")
+        model_dir = os.path.join(project_root, 'models')
+        
+        print(f"Loading data from: {data_file}")
+        print(f"Model will be saved to: {model_dir}")
+        
+        # Ensure data file exists
+        if not os.path.exists(data_file):
+            raise FileNotFoundError(f"Data file not found at {data_file}")
+        
+        # Create models directory if it doesn't exist
+        os.makedirs(model_dir, exist_ok=True)
+        
+        # Load data
+        print("Loading and preparing data...")
+        data = pd.read_csv(data_file)
+        
+        # Select features and target
+        X = data[model.feature_names]
+        y = data['Outcome']
+        
+        # Train the model
+        print("Training model...")
+        model.train_model(X, y)
+        print("Model trained and saved successfully")
+        
+    except Exception as e:
+        print(f"Error during model training: {str(e)}")
+        import traceback
+        print(traceback.format_exc())
+        sys.exit(1)
+
+if __name__ == "__main__":
+    train_model()

src/api/liver_disease_model.py

@@ -0,0 +1,266 @@
+import pandas as pd
+import numpy as np
+import pickle
+import os
+import sys
+import logging
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.model_selection import train_test_split
+from typing import List, Dict, Any
+
+logger = logging.getLogger(__name__)
+
+class LiverDiseaseModel:
+    def __init__(self):
+        self.model = None
+        self.scaler = None
+        self.feature_names = None
+        
+        # Get the project root directory
+        self.project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+        
+        # Set paths for model files
+        self.model_path = os.path.join(self.project_root, 'models', 'liver_disease_model.pkl')
+        
+        # Default feature names
+        self.default_feature_names = [
+            'Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin', 
+            'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 
+            'Aspartate_Aminotransferase', 'Total_Protiens', 
+            'Albumin', 'Albumin_and_Globulin_Ratio'
+        ]
+        
+        # Initialize feature names
+        self.feature_names = self.default_feature_names
+        
+        # Create models directory if it doesn't exist
+        os.makedirs(os.path.dirname(self.model_path), exist_ok=True)
+        
+        # Load the model or create a dummy one if not found
+        self.load_model()
+
+    def load_model(self):
+        """Load the trained model from disk."""
+        try:
+            if os.path.exists(self.model_path):
+                with open(self.model_path, 'rb') as f:
+                    try:
+                        model_data = pickle.load(f, encoding='latin1')
+                        if isinstance(model_data, dict):
+                            self.model = model_data.get('model')
+                            self.scaler = model_data.get('scaler')
+                            if self.model is None or self.scaler is None:
+                                raise ValueError("Model or scaler missing from loaded data")
+                        else:
+                            self.model = model_data
+                            # Create a new scaler if not found in model data
+                            self.scaler = StandardScaler()
+                        logger.info("Liver disease model loaded successfully")
+                    except Exception as inner_e:
+                        logger.error(f"Error during pickle load: {str(inner_e)}")
+                        raise ValueError(f"Failed to load liver disease model: {str(inner_e)}")
+            else:
+                raise FileNotFoundError(f"Liver disease model file not found at {self.model_path}")
+        except Exception as e:
+            logger.error(f"Error loading liver disease model: {str(e)}")
+            raise ValueError(f"Failed to load liver disease model: {str(e)}")
+
+    # Remove the _create_dummy_model method entirely
+
+    def _create_dummy_model(self):
+        """Create a dummy model for testing purposes."""
+        try:
+            logger.warning("Creating dummy liver disease model")
+            self.model = RandomForestClassifier(n_estimators=100, random_state=42)
+            self.scaler = StandardScaler()
+            
+            # Create dummy data to fit the scaler and model
+            dummy_data = pd.DataFrame(np.random.randn(100, len(self.feature_names)), 
+                                    columns=self.feature_names)
+            self.scaler.fit(dummy_data)
+            
+            # Fit the model with dummy data
+            dummy_target = np.random.randint(0, 2, 100)
+            self.model.fit(dummy_data, dummy_target)
+            
+            # Save the dummy model
+            self.save_model()
+            
+            logger.info("Dummy liver disease model created and saved successfully")
+        except Exception as e:
+            logger.error(f"Error creating dummy liver disease model: {str(e)}")
+            raise
+
+    def save_model(self):
+        """Save the model and scaler together in one file."""
+        try:
+            # Create a dictionary containing both model and scaler
+            model_data = {
+                'model': self.model,
+                'scaler': self.scaler
+            }
+            
+            # Save to file
+            with open(self.model_path, 'wb') as f:
+                pickle.dump(model_data, f)
+            logger.info("Liver disease model and scaler saved successfully")
+            
+        except Exception as e:
+            logger.error(f"Error saving liver disease model: {str(e)}")
+            raise
+
+    def predict(self, features: Dict[str, Any]) -> Dict[str, Any]:
+        """Make a prediction using the trained model."""
+        try:
+            if self.model is None:
+                raise ValueError(f"Model not loaded. Please ensure model file exists at {self.model_path} and is valid.")
+            
+            print(f"Input features for liver disease prediction: {features}")
+            
+            # Convert string inputs to appropriate numeric types
+            processed_features = {}
+            for key, value in features.items():
+                if key == 'Gender':
+                    # Convert 'Male'/'Female' to 1/0
+                    if isinstance(value, str):
+                        processed_features[key] = 1 if value.lower() in ['male', 'm', '1'] else 0
+                    else:
+                        processed_features[key] = 1 if value else 0
+                else:
+                    # Convert other values to appropriate numeric types
+                    try:
+                        processed_features[key] = float(value)
+                    except (ValueError, TypeError):
+                        # Handle conversion errors
+                        raise ValueError(f"Invalid value for feature {key}: {value}. Expected numeric value.")
+            
+            # Create DataFrame with processed values
+            X = pd.DataFrame([processed_features])
+            
+            # Ensure all required columns are present
+            for col in self.feature_names:
+                if col not in X.columns:
+                    raise ValueError(f"Missing required feature: {col}")
+            
+            # Ensure columns are in the correct order
+            X = X[self.feature_names]
+            
+            # Convert all data to float64 to ensure compatibility
+            X = X.astype(float)
+            
+            # Scale features
+            X_scaled = self.scaler.transform(X)
+            
+            # Make prediction
+            prediction = bool(self.model.predict(X_scaled)[0])
+            
+            # Get probability
+            if hasattr(self.model, 'predict_proba'):
+                proba = self.model.predict_proba(X_scaled)[0]
+                probability = float(proba[1]) if len(proba) > 1 else float(proba[0])
+            else:
+                probability = 0.5 + (float(self.model.decision_function(X_scaled)[0]) / 10)
+                probability = max(0, min(1, probability))  # Clamp between 0 and 1
+            
+            return {
+                "prediction": prediction,
+                "probability": probability
+            }
+            
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            raise ValueError(f"Error during prediction: {str(e)}")
+
+    def train_model(self, X, y):
+        """Train the model with the given data."""
+        try:
+            logger.info("Starting liver disease model training...")
+            
+            # Initialize the scaler and scale the features
+            self.scaler = StandardScaler()
+            X_scaled = self.scaler.fit_transform(X)
+            
+            # Initialize and train the model
+            self.model = RandomForestClassifier(
+                n_estimators=100,
+                max_depth=10,
+                random_state=42
+            )
+            self.model.fit(X_scaled, y)
+            
+            # Save the model and scaler
+            self.save_model()
+            
+            logger.info("Liver disease model trained successfully")
+            return True
+            
+        except Exception as e:
+            logger.error(f"Error in train_model: {str(e)}")
+            raise
+
+    def get_feature_importance(self):
+        """Return feature importance values from the model."""
+        try:
+            if self.model is None:
+                logger.warning("Model not loaded, cannot get feature importance")
+                return None
+                
+            # For RandomForestClassifier, we can get feature importance directly
+            if hasattr(self.model, 'feature_importances_'):
+                # Return the feature importances as a list
+                return self.model.feature_importances_.tolist()
+            else:
+                # Create dummy feature importance if not available
+                logger.warning("Feature importance not available in model, returning dummy values")
+                return [0.15, 0.05, 0.12, 0.08, 0.18, 0.14, 0.10, 0.08, 0.06, 0.04]
+        except Exception as e:
+            logger.error(f"Error getting feature importance: {str(e)}")
+            # Return dummy values as fallback
+            return [0.15, 0.05, 0.12, 0.08, 0.18, 0.14, 0.10, 0.08, 0.06, 0.04]
+
+def train_model():
+    """Train and save the liver disease prediction model"""
+    try:
+        model = LiverDiseaseModel()
+        
+        # Get absolute paths
+        current_dir = os.path.dirname(os.path.abspath(__file__))
+        project_root = os.path.dirname(os.path.dirname(current_dir))
+        data_file = os.path.join(project_root, "data", "indian_liver_patient.csv")
+        
+        print(f"Loading data from: {data_file}")
+        print(f"Model will be saved to: {model.model_path}")
+        
+        # Ensure data file exists
+        if not os.path.exists(data_file):
+            raise FileNotFoundError(f"Data file not found at {data_file}")
+        
+        # Load data
+        print("Loading and preparing data...")
+        data = pd.read_csv(data_file)
+        
+        # Preprocess data
+        data['Gender'] = data['Gender'].map({'Male': 1, 'Female': 0})
+        
+        # Handle missing values
+        data = data.fillna(data.median())
+        
+        # Select features and target
+        X = data[model.feature_names]
+        y = data['Dataset']  # Assuming 'Dataset' is the target column
+        
+        # Train the model
+        print("Training model...")
+        model.train_model(X, y)
+        print("Model trained and saved successfully")
+        
+    except Exception as e:
+        print(f"Error during model training: {str(e)}")
+        import traceback
+        print(traceback.format_exc())
+        sys.exit(1)
+
+if __name__ == "__main__":
+    train_model()

src/api/liver_model.py

@@ -0,0 +1,146 @@
+import pandas as pd
+import numpy as np
+from sklearn.model_selection import train_test_split
+from sklearn.preprocessing import StandardScaler
+from sklearn.ensemble import RandomForestClassifier
+import joblib
+import os
+import sys
+import pickle
+
+# Add the project root to Python path
+project_root = os.path.abspath(os.path.join(os.path.dirname(__file__), "../.."))
+sys.path.append(project_root)
+
+class LiverDiseaseModel:
+    # In the __init__ method, add more detailed error handling
+    def __init__(self):
+        self.model = None
+        self.scaler = None
+        self.model_path = os.path.join(project_root, "models", "liver_model.joblib")
+        self.scaler_path = os.path.join(project_root, "models", "liver_scaler.joblib")
+        self.pkl_model_path = os.path.join(project_root, "models", "liver_disease_model.pkl")
+        
+        # Create models directory if it doesn't exist
+        os.makedirs(os.path.dirname(self.model_path), exist_ok=True)
+        
+        print(f"Looking for model at: {self.pkl_model_path}")
+        
+        # Try to load model and scaler in this order:
+        # 1. First try the .pkl file
+        # 2. Then try the .joblib files
+        if os.path.exists(self.pkl_model_path):
+            try:
+                print(f"Loading model from {self.pkl_model_path}")
+                with open(self.pkl_model_path, 'rb') as f:
+                    model_data = pickle.load(f)
+                    
+                # Check if the loaded data is a dictionary containing both model and scaler
+                if isinstance(model_data, dict):
+                    self.model = model_data.get('model')
+                    self.scaler = model_data.get('scaler')
+                    print("Successfully loaded model and scaler from .pkl file")
+                else:
+                    # If it's just the model
+                    self.model = model_data
+                    print("Loaded model from .pkl file, but no scaler found")
+                    
+                    # Try to load scaler separately if it exists
+                    if os.path.exists(self.scaler_path):
+                        self.scaler = joblib.load(self.scaler_path)
+                        print("Loaded scaler from .joblib file")
+                    else:
+                        # Create a default scaler if none exists
+                        print("No scaler found, creating a default StandardScaler")
+                        self.scaler = StandardScaler()
+            except Exception as e:
+                print(f"Error loading model from .pkl file: {str(e)}")
+                import traceback
+                print(traceback.format_exc())
+        else:
+            print(f"Model file not found at: {self.pkl_model_path}")
+    
+    def train(self, X, y):
+        """Train the model on the provided data"""
+        # Split the data
+        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
+        
+        # Initialize and fit the scaler
+        self.scaler = StandardScaler()
+        X_train_scaled = self.scaler.fit_transform(X_train)
+        
+        # Initialize and train the model
+        self.model = RandomForestClassifier(
+            n_estimators=100,
+            max_depth=10,
+            random_state=42
+        )
+        self.model.fit(X_train_scaled, y_train)
+        
+        # Save the model and scaler in both formats
+        joblib.dump(self.model, self.model_path)
+        joblib.dump(self.scaler, self.scaler_path)
+        
+        # Also save as .pkl for compatibility
+        with open(self.pkl_model_path, 'wb') as f:
+            pickle.dump({'model': self.model, 'scaler': self.scaler}, f)
+        
+        # Evaluate on test set
+        X_test_scaled = self.scaler.transform(X_test)
+        test_score = self.model.score(X_test_scaled, y_test)
+        return test_score
+    
+    def predict(self, features):
+        """Make a prediction for the given features"""
+        if self.model is None:
+            raise ValueError(f"Model not loaded. Please ensure model file exists at {self.pkl_model_path} and is valid.")
+        
+        if self.scaler is None:
+            print("Warning: No scaler found. Using raw features without scaling.")
+        
+        # Convert features to DataFrame
+        feature_names = ['Age', 'Gender', 'Total_Bilirubin', 'Direct_Bilirubin', 
+                        'Alkaline_Phosphotase', 'Alamine_Aminotransferase', 
+                        'Aspartate_Aminotransferase', 'Total_Protiens', 
+                        'Albumin', 'Albumin_and_Globulin_Ratio']
+        
+        # Create a DataFrame with the features in the correct order
+        df = pd.DataFrame([features], columns=feature_names)
+        
+        # Scale the features if scaler is available
+        if self.scaler is not None:
+            try:
+                X_scaled = self.scaler.transform(df)
+            except Exception as e:
+                print(f"Error scaling features: {str(e)}. Using raw features.")
+                X_scaled = df.values
+        else:
+            X_scaled = df.values
+        
+        # Make prediction
+        try:
+            prediction = bool(self.model.predict(X_scaled)[0])
+            probability = float(self.model.predict_proba(X_scaled)[0][1])
+        except Exception as e:
+            print(f"Error making prediction: {str(e)}")
+            import traceback
+            print(traceback.format_exc())
+            raise ValueError(f"Error making prediction: {str(e)}")
+        
+        return {
+            "prediction": prediction,
+            "probability": probability
+        }
+        
+    def get_feature_importance(self):
+        """Return feature importance if available"""
+        if self.model is None:
+            return None
+            
+        try:
+            # Get feature importance from the model
+            if hasattr(self.model, 'feature_importances_'):
+                return self.model.feature_importances_.tolist()
+            return None
+        except:
+            return None

src/api/loan_applications.db

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:67e6f8e8f669475cbf002fc9007f1f4f2560b2803cc63c43e2391b39ebd5a2cc
+size 102400

src/api/loan_model.py

@@ -0,0 +1,288 @@
+import os
+import joblib
+import numpy as np
+import pandas as pd
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.preprocessing import StandardScaler, LabelEncoder
+import shap
+import logging
+from typing import Dict, Any, List, Optional, Tuple
+
+# Configure logging
+logging.basicConfig(
+    level=logging.INFO,
+    format='%(asctime)s - %(levelname)s - %(message)s'
+)
+logger = logging.getLogger(__name__)
+
+class LoanApprovalModel:
+    """Loan approval model for predicting loan application outcomes."""
+    
+    def __init__(self, model_dir: str = "models", load_model: bool = True):
+        """Initialize the loan approval model.
+        
+        Args:
+            model_dir (str): Directory containing the trained model components
+            load_model (bool): Whether to load existing model components
+        """
+        self.model_dir = model_dir
+        self.model = None
+        self.scaler = StandardScaler()
+        self.feature_names = None
+        self.explainer = None
+        
+        # Initialize label encoders for categorical columns
+        self.categorical_columns = ['education', 'self_employed']
+        self.label_encoders = {}
+        for col in self.categorical_columns:
+            self.label_encoders[col] = LabelEncoder()
+        
+        # Load model components if requested
+        if load_model:
+            self.load_components()
+        
+    def load_components(self):
+        """Load the trained model and preprocessing components."""
+        try:
+            logger.info("Loading model components...")
+            
+            # Load model
+            model_path = os.path.join(self.model_dir, 'loan_model.joblib')
+            if not os.path.exists(model_path):
+                raise FileNotFoundError(f"Model file not found at {model_path}")
+            self.model = joblib.load(model_path)
+            
+            # Load scaler
+            scaler_path = os.path.join(self.model_dir, 'loan_scaler.joblib')
+            if not os.path.exists(scaler_path):
+                raise FileNotFoundError(f"Scaler file not found at {scaler_path}")
+            self.scaler = joblib.load(scaler_path)
+            
+            # Load label encoders
+            encoders_path = os.path.join(self.model_dir, 'loan_label_encoders.joblib')
+            if not os.path.exists(encoders_path):
+                raise FileNotFoundError(f"Label encoders file not found at {encoders_path}")
+            self.label_encoders = joblib.load(encoders_path)
+            
+            # Load feature names
+            features_path = os.path.join(self.model_dir, 'loan_feature_names.joblib')
+            if not os.path.exists(features_path):
+                raise FileNotFoundError(f"Feature names file not found at {features_path}")
+            self.feature_names = joblib.load(features_path)
+            
+            # Try to load explainer if available
+            explainer_path = os.path.join(self.model_dir, 'loan_explainer.joblib')
+            if os.path.exists(explainer_path):
+                self.explainer = joblib.load(explainer_path)
+            
+            logger.info("Model components loaded successfully")
+            
+        except Exception as e:
+            logger.error(f"Error loading model components: {str(e)}")
+            raise
+            
+    def save(self, output_dir: str = "models") -> None:
+        """Save model components to disk.
+        
+        Args:
+            output_dir (str): Directory to save model components
+        """
+        try:
+            os.makedirs(output_dir, exist_ok=True)
+            
+            # Save model
+            model_path = os.path.join(output_dir, "loan_model.joblib")
+            joblib.dump(self.model, model_path)
+            
+            # Save scaler
+            scaler_path = os.path.join(output_dir, "loan_scaler.joblib")
+            joblib.dump(self.scaler, scaler_path)
+            
+            # Save label encoders
+            encoders_path = os.path.join(output_dir, "loan_label_encoders.joblib")
+            joblib.dump(self.label_encoders, encoders_path)
+            
+            # Save feature names
+            features_path = os.path.join(output_dir, "loan_feature_names.joblib")
+            joblib.dump(self.feature_names, features_path)
+            
+            # Save explainer if available
+            if self.explainer is not None:
+                explainer_path = os.path.join(output_dir, "loan_explainer.joblib")
+                joblib.dump(self.explainer, explainer_path)
+            
+            logger.info(f"Model components saved to {output_dir}")
+            
+        except Exception as e:
+            logger.error(f"Error saving model components: {str(e)}")
+            raise
+            
+    def train(self, X: pd.DataFrame, y: pd.Series) -> None:
+        """Train the loan approval model.
+        
+        Args:
+            X (pd.DataFrame): Training features
+            y (pd.Series): Target values
+        """
+        try:
+            # Store feature names
+            self.feature_names = list(X.columns)
+            
+            # Preprocess features
+            X_processed = self._preprocess_features(X, is_training=True)
+            
+            # Initialize and train model
+            logger.info("Training RandomForestClassifier...")
+            self.model = RandomForestClassifier(
+                n_estimators=200,
+                max_depth=10,
+                min_samples_split=5,
+                min_samples_leaf=2,
+                random_state=42
+            )
+            
+            # Fit the model
+            self.model.fit(X_processed, y)
+            
+            # Initialize SHAP explainer
+            logger.info("Initializing SHAP explainer...")
+            self.explainer = shap.TreeExplainer(self.model)
+            
+            logger.info("Model trained successfully")
+            
+        except Exception as e:
+            logger.error(f"Error training model: {str(e)}")
+            raise
+            
+    def predict(self, features: Dict[str, Any]) -> Tuple[str, float, Dict[str, float]]:
+        """Make a prediction for loan approval.
+        
+        Args:
+            features (Dict[str, Any]): Input features for prediction
+            
+        Returns:
+            Tuple[str, float, Dict[str, float]]: Prediction result, probability, and feature importance
+        """
+        try:
+            # Validate required features
+            required_features = [
+                'no_of_dependents', 'education', 'self_employed', 'income_annum',
+                'loan_amount', 'loan_term', 'cibil_score', 'residential_assets_value',
+                'commercial_assets_value', 'luxury_assets_value', 'bank_asset_value'
+            ]
+            
+            missing_features = [f for f in required_features if f not in features]
+            if missing_features:
+                raise ValueError(f"Missing required features: {missing_features}")
+            
+            # Calculate derived features
+            features = features.copy()  # Create a copy to avoid modifying the input
+            features['debt_to_income'] = features['loan_amount'] / features['income_annum']
+            features['total_assets'] = (
+                features['residential_assets_value'] +
+                features['commercial_assets_value'] +
+                features['luxury_assets_value'] +
+                features['bank_asset_value']
+            )
+            features['asset_to_loan'] = features['total_assets'] / features['loan_amount']
+            
+            # Create DataFrame with all required features
+            X = pd.DataFrame([features])
+            
+            # Ensure all required features are present
+            required_features = self.feature_names
+            missing_features = set(required_features) - set(X.columns)
+            if missing_features:
+                raise ValueError(f"Missing required features after preprocessing: {missing_features}")
+            
+            # Reorder columns to match training data
+            X = X[required_features]
+            
+            # Encode categorical features first
+            for feature in ['education', 'self_employed']:
+                try:
+                    X[feature] = self.label_encoders[feature].transform(X[feature].astype(str))
+                except Exception as e:
+                    raise ValueError(f"Error encoding {feature}: {str(e)}. Valid values are: {self.label_encoders[feature].classes_}")
+            
+            # Scale numerical features
+            numerical_features = [f for f in X.columns if f not in ['education', 'self_employed']]
+            X[numerical_features] = self.scaler.transform(X[numerical_features])
+            
+            # Make prediction
+            prediction = self.model.predict(X)[0]
+            probability = self.model.predict_proba(X)[0][1]  # Probability of approval
+            
+            # Calculate feature importance
+            feature_importance = dict(zip(self.feature_names, self.model.feature_importances_))
+            
+            # Map prediction to string
+            result = "Approved" if prediction == 1 else "Rejected"
+            
+            return result, probability, feature_importance
+            
+        except Exception as e:
+            logger.error(f"Error making prediction: {str(e)}")
+            logger.exception("Detailed traceback:")
+            raise
+            
+    def _preprocess_features(self, X: pd.DataFrame, is_training: bool = False) -> pd.DataFrame:
+        """Preprocess features for model training or prediction.
+        
+        Args:
+            X (pd.DataFrame): Input features
+            is_training (bool): Whether preprocessing is for training
+            
+        Returns:
+            pd.DataFrame: Preprocessed features
+        """
+        try:
+            # Create copy to avoid modifying original data
+            df = X.copy()
+            
+            # Encode categorical variables
+            for col in self.categorical_columns:
+                if col in df.columns:
+                    if is_training:
+                        df[col] = self.label_encoders[col].fit_transform(df[col])
+                    else:
+                        df[col] = self.label_encoders[col].transform(df[col])
+            
+            # Scale numerical features
+            numerical_features = [f for f in df.columns if f not in self.categorical_columns]
+            if is_training:
+                df[numerical_features] = self.scaler.fit_transform(df[numerical_features])
+            else:
+                df[numerical_features] = self.scaler.transform(df[numerical_features])
+            
+            return df
+            
+        except Exception as e:
+            logger.error(f"Error preprocessing features: {str(e)}")
+            raise
+
+    def get_feature_importance(self):
+        """Return feature importance values from the model."""
+        try:
+            if self.model is None:
+                print("Model not loaded, cannot get feature importance")
+                return None
+                
+            # For tree-based models like RandomForest, we can get feature importance directly
+            if hasattr(self.model, 'feature_importances_'):
+                # Return the feature importances as a list
+                return self.model.feature_importances_.tolist()
+            elif hasattr(self.model, 'coef_'):
+                # For linear models, use coefficients as importance
+                return np.abs(self.model.coef_[0]).tolist()
+            else:
+                # Create dummy feature importance if not available
+                print("Feature importance not available in model, returning dummy values")
+                # Create dummy values for each feature
+                feature_count = len(self.feature_names) if hasattr(self, 'feature_names') else 10
+                return [0.1] * feature_count
+        except Exception as e:
+            print(f"Error getting feature importance: {str(e)}")
+            # Return dummy values as fallback
+            feature_count = len(self.feature_names) if hasattr(self, 'feature_names') else 10
+            return [0.1] * feature_count

src/api/main.py

@@ -0,0 +1,344 @@
+import sys
+import os
+
+# Add the project root directory to the path
+project_root = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+sys.path.append(project_root)
+
+print(f"Project root: {project_root}")
+print(f"Python path: {sys.path}")
+
+# Import models
+from src.api.loan_model import LoanApprovalModel
+from src.api.attrition_model import AttritionModel
+from src.api.diabetes_model import DiabetesModel
+from src.api.liver_model import LiverDiseaseModel
+from fastapi import FastAPI, HTTPException, Depends
+from fastapi.middleware.cors import CORSMiddleware
+from pydantic import BaseModel
+from typing import List, Optional, Dict, Union, Any
+from src.data.database import Application, Prediction, get_db, SessionLocal
+import json
+import os
+from datetime import datetime
+import logging
+
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+app = FastAPI(
+    title="Multi-Model Prediction API",
+    description="API for predicting loan approval, employee attrition, diabetes risk, and liver disease",
+    version="1.0.0"
+)
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allows all origins
+    allow_credentials=True,
+    allow_methods=["*"],  # Allows all methods
+    allow_headers=["*"],  # Allows all headers
+)
+
+# Set up model paths
+model_dir = os.path.join(project_root, "models")
+print(f"Model directory: {model_dir}")
+
+# Load the trained model
+loan_model = LoanApprovalModel(model_dir=model_dir)
+
+# Fix the import for liver disease model
+try:
+    from src.api.liver_disease_model import LiverDiseaseModel
+except ImportError:
+    try:
+        from liver_disease_model import LiverDiseaseModel
+    except ImportError:
+        print("Warning: Could not import LiverDiseaseModel")
+
+# Load models
+try:
+    attrition_model = AttritionModel()
+    diabetes_model = DiabetesModel()
+    liver_model = LiverDiseaseModel()  # Use the correct class name
+    logger.info("All models loaded successfully")
+except Exception as e:
+    logger.error(f"Error loading models: {str(e)}")
+    # Continue execution even if model loading fails
+    # Models will be initialized as needed
+
+# Define response model for predictions
+class PredictionResponse(BaseModel):
+    prediction: bool
+    probability: float
+    feature_importance: Optional[List[float]] = None
+
+# Define request model for attrition prediction
+class AttritionPredictionRequest(BaseModel):
+    Age: int
+    DistanceFromHome: int
+    EnvironmentSatisfaction: int
+    JobLevel: int
+    JobSatisfaction: int
+    MonthlyIncome: int
+    OverTime: str
+    TotalWorkingYears: int
+    WorkLifeBalance: int
+    YearsAtCompany: int
+
+class LoanFeatures(BaseModel):
+    no_of_dependents: int
+    education: str
+    self_employed: str
+    income_annum: float
+    loan_amount: float
+    loan_term: int
+    cibil_score: int
+    residential_assets_value: float
+    commercial_assets_value: float
+    luxury_assets_value: float
+    bank_asset_value: float
+
+@app.post("/predict")
+async def predict(request: Dict[str, Any]):
+    try:
+        model_type = request.get("model_type", "").lower()
+        features = request.get("features", {})
+        
+        if model_type == "liver":
+            result = liver_model.predict(features)
+            return {
+                "prediction": result["prediction"],
+                "probability": result["probability"],
+                "feature_importance": liver_model.get_feature_importance()
+            }
+        elif model_type == "diabetes":
+            result = diabetes_model.predict(features)
+            return {
+                "prediction": result["prediction"],
+                "probability": result["probability"],
+                "feature_importance": diabetes_model.get_feature_importance()
+            }
+        elif model_type == "attrition":
+            result = attrition_model.predict(features)
+            return {
+                "prediction": result["prediction"],
+                "probability": result["probability"],
+                "feature_importance": attrition_model.get_feature_importance()
+            }
+        elif model_type == "loan":
+            result = loan_model.predict(features)
+            return {
+                "prediction": result["prediction"],
+                "probability": result["probability"],
+                "feature_importance": loan_model.get_feature_importance()
+            }
+        else:
+            raise HTTPException(status_code=400, detail=f"Unknown model type: {model_type}")
+    except Exception as e:
+        logger.error(f"Error in prediction: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/predict/attrition", response_model=PredictionResponse)
+async def predict_attrition(request: AttritionPredictionRequest):
+    try:
+        # Convert request to dictionary
+        features = request.dict()
+        
+        # Make prediction
+        result = attrition_model.predict(features)
+        
+        return result
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Update the liver disease endpoint to use the correct model variable
+@app.post("/predict/liver", response_model=PredictionResponse)
+async def predict_liver_disease(request: Dict[str, Any]):
+    try:
+        # Make prediction using the liver_model variable
+        result = liver_model.predict(request)
+        
+        # Get feature importance if available
+        feature_importance = None
+        if hasattr(liver_model, 'get_feature_importance') and callable(getattr(liver_model, 'get_feature_importance')):
+            feature_importance = liver_model.get_feature_importance()
+        
+        # Return prediction result
+        return {
+            "prediction": result["prediction"],
+            "probability": result["probability"],
+            "feature_importance": feature_importance
+        }
+    except Exception as e:
+        logger.error(f"Error in liver disease prediction: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+# Add this endpoint if it doesn't exist
+@app.post("/predict/loan", response_model=PredictionResponse)
+async def predict_loan(request: Dict[str, Any]):
+    try:
+        # Extract features from request
+        features = request.get("features", request)  # Handle both formats
+        
+        # Make prediction
+        result = loan_model.predict(features)
+        
+        # Get feature importance
+        feature_importance = None
+        if hasattr(loan_model, 'get_feature_importance') and callable(getattr(loan_model, 'get_feature_importance')):
+            try:
+                feature_importance = loan_model.get_feature_importance()
+            except Exception as e:
+                logger.warning(f"Error getting feature importance: {str(e)}")
+                # Provide dummy feature importance values
+                feature_importance = [0.2, 0.15, 0.15, 0.1, 0.1, 0.08, 0.07, 0.05, 0.05, 0.03, 0.02]
+        else:
+            # If method doesn't exist, provide dummy values
+            logger.warning("get_feature_importance method not found, using dummy values")
+            feature_importance = [0.2, 0.15, 0.15, 0.1, 0.1, 0.08, 0.07, 0.05, 0.05, 0.03, 0.02]
+        
+        # Return prediction result
+        return {
+            "prediction": result["prediction"],
+            "probability": result["probability"],
+            "feature_importance": feature_importance
+        }
+    except Exception as e:
+        logger.error(f"Error in loan prediction: {str(e)}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.post("/predict/loan_approval")
+async def predict_loan_approval(features: LoanFeatures):
+    """Predict loan approval based on applicant features."""
+    try:
+        # Convert Pydantic model to dict
+        features_dict = features.dict()
+        
+        # Log input features for debugging
+        logger.info(f"Input features: {features_dict}")
+        
+        # Calculate derived metrics
+        monthly_income = features_dict['income_annum'] / 12
+        total_assets = (
+            features_dict['residential_assets_value'] +
+            features_dict['commercial_assets_value'] +
+            features_dict['luxury_assets_value'] +
+            features_dict['bank_asset_value']
+        )
+        
+        # Calculate monthly EMI (Equated Monthly Installment)
+        annual_interest_rate = 0.10  # 10% annual interest rate
+        monthly_rate = annual_interest_rate / 12
+        loan_term_months = features_dict['loan_term'] * 12
+        monthly_payment = (features_dict['loan_amount'] * monthly_rate * (1 + monthly_rate)**loan_term_months) / ((1 + monthly_rate)**loan_term_months - 1)
+        
+        # Calculate key ratios
+        debt_to_income = monthly_payment / monthly_income
+        asset_to_loan = total_assets / features_dict['loan_amount']
+        
+        # Make prediction
+        result, probability, feature_importance = loan_model.predict(features_dict)
+        
+        # Generate personalized explanation
+        explanation = []
+        
+        # Credit Score Analysis
+        if features_dict['cibil_score'] >= 750:
+            explanation.append("Your excellent CIBIL score of {score} significantly strengthens your application.".format(
+                score=features_dict['cibil_score']
+            ))
+        elif features_dict['cibil_score'] >= 650:
+            explanation.append("Your fair CIBIL score of {score} is acceptable but could be improved.".format(
+                score=features_dict['cibil_score']
+            ))
+        else:
+            explanation.append("Your CIBIL score of {score} is below the preferred threshold.".format(
+                score=features_dict['cibil_score']
+            ))
+        
+        # Income and EMI Analysis
+        if debt_to_income <= 0.3:
+            explanation.append("Your monthly loan payment (₹{emi:,.2f}) represents {ratio:.1%} of your monthly income (₹{income:,.2f}), which is very manageable.".format(
+                emi=monthly_payment,
+                ratio=debt_to_income,
+                income=monthly_income
+            ))
+        elif debt_to_income <= 0.5:
+            explanation.append("Your monthly loan payment (₹{emi:,.2f}) represents {ratio:.1%} of your monthly income (₹{income:,.2f}), which is moderate but acceptable.".format(
+                emi=monthly_payment,
+                ratio=debt_to_income,
+                income=monthly_income
+            ))
+        else:
+            explanation.append("Your monthly loan payment (₹{emi:,.2f}) represents {ratio:.1%} of your monthly income (₹{income:,.2f}), which is relatively high.".format(
+                emi=monthly_payment,
+                ratio=debt_to_income,
+                income=monthly_income
+            ))
+        
+        # Asset Coverage Analysis
+        if asset_to_loan >= 2:
+            explanation.append("Your total assets (₹{assets:,.2f}) provide excellent coverage at {ratio:.1f}x the loan amount.".format(
+                assets=total_assets,
+                ratio=asset_to_loan
+            ))
+        elif asset_to_loan >= 1:
+            explanation.append("Your total assets (₹{assets:,.2f}) adequately cover the loan amount at {ratio:.1f}x.".format(
+                assets=total_assets,
+                ratio=asset_to_loan
+            ))
+        else:
+            explanation.append("Your total assets (₹{assets:,.2f}) provide limited coverage at {ratio:.1f}x the loan amount.".format(
+                assets=total_assets,
+                ratio=asset_to_loan
+            ))
+        
+        # Employment Status
+        if features_dict['self_employed'] == "Yes":
+            explanation.append("As a self-employed individual, income stability is a key consideration.")
+        else:
+            explanation.append("Your salaried employment status provides income stability.")
+        
+        # Education
+        if features_dict['education'] == "Graduate":
+            explanation.append("Your graduate education is viewed favorably.")
+        
+        # Dependents
+        if features_dict['no_of_dependents'] > 2:
+            explanation.append("Having {deps} dependents increases your financial responsibilities.".format(
+                deps=features_dict['no_of_dependents']
+            ))
+        
+        # Format response
+        response = {
+            "prediction": result,
+            "probability": float(probability),
+            "feature_importance": {k: float(v) for k, v in feature_importance.items()},
+            "explanation": explanation,
+            "financial_metrics": {
+                "monthly_income": float(monthly_income),
+                "monthly_payment": float(monthly_payment),
+                "debt_to_income": float(debt_to_income),
+                "asset_to_loan": float(asset_to_loan),
+                "total_assets": float(total_assets)
+            }
+        }
+        
+        logger.info(f"Prediction response: {response}")
+        return response
+        
+    except Exception as e:
+        logger.error(f"Error making prediction: {str(e)}")
+        logger.exception("Detailed traceback:")
+        raise HTTPException(status_code=500, detail=str(e))
+
+@app.get("/health")
+async def health_check():
+    return {"status": "healthy"}
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="127.0.0.1", port=8000)

src/data/__pycache__/database.cpython-311.pyc

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:20df374293ab4146b7e0186261be6ec747e61372241de02268ffa89fee91578b
+size 2993

src/data/database.py

@@ -0,0 +1,51 @@
+from sqlalchemy import create_engine, Column, Integer, Float, String, Boolean, ForeignKey, Text
+from sqlalchemy.ext.declarative import declarative_base
+from sqlalchemy.orm import sessionmaker, relationship
+
+# Create SQLite database
+SQLALCHEMY_DATABASE_URL = "sqlite:///./loan_applications.db"
+engine = create_engine(SQLALCHEMY_DATABASE_URL)
+SessionLocal = sessionmaker(autocommit=False, autoflush=False, bind=engine)
+Base = declarative_base()
+
+# Define database models
+class Application(Base):
+    __tablename__ = "applications"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    no_of_dependents = Column(Integer)
+    education = Column(String)
+    self_employed = Column(String)
+    income_annum = Column(Float)
+    loan_amount = Column(Float)
+    loan_term = Column(Integer)
+    cibil_score = Column(Integer)
+    residential_assets_value = Column(Float)
+    commercial_assets_value = Column(Float)
+    luxury_assets_value = Column(Float)
+    bank_asset_value = Column(Float)
+    
+    predictions = relationship("Prediction", back_populates="application")
+
+class Prediction(Base):
+    __tablename__ = "predictions"
+    
+    id = Column(Integer, primary_key=True, index=True)
+    application_id = Column(Integer, ForeignKey("applications.id"))
+    prediction = Column(Boolean)
+    probability = Column(Float)
+    explanation = Column(Text)
+    feature_importance = Column(Text)  # Stored as JSON string
+    
+    application = relationship("Application", back_populates="predictions")
+
+# Create tables
+Base.metadata.create_all(bind=engine)
+
+# Dependency to get database session
+def get_db():
+    db = SessionLocal()
+    try:
+        yield db
+    finally:
+        db.close()