app.py

import streamlit as st
import pandas as pd
import numpy as np
import joblib
import os
import logging
from datetime import datetime
from openai import AzureOpenAI
from dotenv import load_dotenv
import uuid

# Configure logging
logging.basicConfig(level=logging.ERROR)

# Load environment variables
load_dotenv()

# Initialize Azure OpenAI GPT client
azure_api_key = os.getenv("AZURE_OPENAI_API_KEY")
azure_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT")
client = AzureOpenAI(azure_endpoint=azure_endpoint, api_key=azure_api_key, api_version="2024-02-01")

# Set default values for missing columns
defaults = {
    'policy_number': 176450963679054,
}

# Function to analyze claim details using few-shot prompting
def analyze_claim_with_few_shot_prompt(claim_data):
    """
    Analyze the claim data using few-shot prompting to classify it as Fraudulent or Non-Fraudulent.

    Parameters:
        claim_data (dict): Dictionary containing claim details.

    Returns:
        tuple: Classification (Fraudulent/Non-Fraudulent) and reasoning as a string.
    """
    examples = """
### Examples:
1. **Scenario**: The client stated that the accident happened at 3 AM in a remote area, but the vehicle showed no signs of damage.
   **Outcome**: Fraudulent
   **Reasoning**: The timing and location are suspicious, and the lack of vehicle damage contradicts the claim.

2. **Scenario**: The client provided consistent details about a minor collision, with a supporting police report and witness statements.
   **Outcome**: Non-Fraudulent
   **Reasoning**: All evidence aligns with the claim details.

3. **Scenario**: The client filed a claim for vehicle theft but could not produce a police report and provided vague answers about the incident.
   **Outcome**: Fraudulent
   **Reasoning**: Lack of supporting documentation and evasive responses indicate possible fraud.

4. **Scenario**: The client reported a severe collision with clear photographic evidence and a medical report for injuries.
   **Outcome**: Non-Fraudulent
   **Reasoning**: Strong documentation supports the claim.

5. **Scenario**: The client reported an accident I was driving at 2 A.M. there was a lot of traffic and few people just came in the way on the highway I was driving at the speed of 90.
   **Outcome**: Fraudulent
   **Reasoning**: Suspicious timing, case do not align with common sense.
"""

    prompt = f"""
As an auto insurance fraud detection expert working at a large automobile insurance company, your job is to help detect fraud in auto insurance claims filed by customers.

### Task:
Your job is to analyze customer and agent call log details and additional claim metadata to classify whether the claim is fraudulent or not.

### Provided details:
- **Policy Number**: {claim_data['policy_number']}
- **Claim Date**: {claim_data['claim_date']}
- **Claim Amount**: {claim_data['claim_amount']}
- **Vehicle Info**: {claim_data['vehicle_info']}
- **Insurance Purchase Date**: {claim_data['insurance_purchase_date']}
- **Insurance Renewal Date**: {claim_data['insurance_renewal_date']}
- **Claim Details**: {claim_data['claim_details']}
- **Incident Location**: {claim_data['incident_location']}

{examples}

### Indicators for Fraud Detection:
Look for the following indicators when analyzing the call logs and claim metadata:
- Inconsistent or contradictory statements made by customers or agents.
- Identify any suspicious behaviors or patterns, such as overly detailed descriptions, repeated claims, or unusual urgency.
- Pay attention to any discrepancies between the call logs and the corresponding claim documents. For example, a claim is for a windshield but the incident is of being rear-ended.
- Note if there are any red flags, such as customers hesitating or changing their stories during the conversation.
- Question cases that do not align with common sense, such as a customer claiming an accident was caused by heavy traffic at midnight.
- Suspicious timing or location of the incident
- Any past history of dubious claims

### Step-by-Step Instructions:
1.Initial Context Understanding:
Read and understand the call log details and the provided metadata.

2.Detecting Patterns:
Identify specific phrases, patterns, or inconsistencies that may indicate fraudulent activity.

3.Classification:
Classify the claim as “Fraudulent” or “Non-Fraudulent” based on your analysis.
Provide a detailed explanation supporting your classification.
Highlight the specific parts of the call log and metadata that influenced your decision.

### Call Log and Metadata to Analyze:
{claim_data['call_log']}

Based on the above details, classify the claim below:
"""

    try:
        response = client.chat.completions.create(
            model="gpt-35-turbo",
            messages=[{"role": "user", "content": prompt}],
            temperature=0.0
        )
        response_text = response.choices[0].message.content.strip()

        if "Classification:" in response_text:
        # Find the first occurrence of classification
            classification_index = response_text.find("Classification:")
            classification_line = response_text[classification_index:].split('\n')[0]
            if "Non-Fraudulent" in classification_line:
                classification = "Non-Fraudulent"
            elif "Fraudulent" in classification_line:
                classification = "Fraudulent"
            elif "Needs Expert Opinion" in classification_line or "not possible to classify" in classification_line:
                classification = "Needs Expert Opinion"

        reasoning = response_text.split('Reasoning: ')[-1].strip()

        return classification, reasoning, response_text
    except Exception as e:
        logging.error(f"Classification of Claim for Policy {claim_data['policy_number']} Failed due to Error: {e}")
        return "Error", "Unable to classify claim, Retry!"


# Streamlit UI
st.set_page_config(page_title="Vehicle Insurance Fraud Detection", layout="wide")
st.markdown(
    """
    <style>
        .main {background-color: #f7f7f7;}
        .stButton>button {background-color: #4CAF50; color: white; font-weight: bold; padding: 12px 24px; border-radius: 8px;}
        .stButton>button:hover {background-color: #45a049;}
        .header {color: #2E8B57;}
        .section-header {color: #5f6368; font-size: 20px; margin-top: 20px;}
        .input {background-color: #ffffff; border-radius: 8px; border: 1px solid #ddd; padding: 10px; box-shadow: 0 2px 4px rgba(0,0,0,0.1);}
        
        /* Center and style the page header */
        .title { 
            text-align: center; 
            color: navy; 
            font-size: 36px; 
            font-weight: bold; 
            padding-top: 20px;
        }
    </style>
    """, unsafe_allow_html=True)

# Custom title in the center and with navy blue color
st.markdown('<div class="title">Vehicle Insurance Fraud Detection</div>', unsafe_allow_html=True)

# Input form for structured data
st.header('Claim Details')

policy_number= st.text_input('Policy Number',help='Enter the alphanumeric policy number (e.g., ABC12345).')
incident_date= st.date_input('Claim Date', value=datetime(2024, 1, 1))
total_claim_amount = st.number_input('Claim Amount', min_value=0, max_value=100000, value=5000)
insurance_purchase_date= st.date_input('Insurance Purchase Date', value=datetime(2023, 4, 1))
insurance_renewal_date= st.date_input('Insurance Purchase Date', value=datetime(2025, 4, 1))
incident_location= st.text_input('Incident Location', value ='Mumbai')
auto_year = st.number_input('Auto Year', min_value=1990, max_value=2024, value=2015)

# Notes Section
st.header('Notes')
customer_notes = st.text_area('Customer Notes', help='Enter any specific details shared by the customer.')
agent_notes = st.text_area('Agent Notes', help='Enter any observations or details from the agent.')

# Convert incident_date to a timestamp
incident_timestamp = datetime.combine(incident_date, datetime.min.time()).timestamp()

# Compile data
claim_data = {
    'policy_number': policy_number,
    'claim_date': str(incident_date),
    'claim_amount': total_claim_amount,
    'vehicle_info': f'{auto_year}',
    'insurance_purchase_date': str(insurance_purchase_date),
    'insurance_renewal_date': str(insurance_renewal_date),
    'claim_details': customer_notes,
    'incident_location': incident_location,
    'call_log': customer_notes + "\n" + agent_notes
}

# Streamlit UI - Button and Classification Styling
if st.button('Classify Claim'):
    with st.spinner('Processing...'):
        classification, reasoning, response = analyze_claim_with_few_shot_prompt(claim_data)

    # Conditional Classification Text Color: Red for "Fraudulent", Green otherwise
    if classification == "Fraudulent":
        classification_color = 'red'
    else:
        classification_color = 'green'

    # Display Classification with conditional color
    st.markdown(f"### **Classification:** <span style='color:{classification_color};'>{classification}</span>", unsafe_allow_html=True)
    st.markdown("### **Reasoning:**", unsafe_allow_html=True)
    st.write(reasoning)
    #st.markdown("### ** Response :: **")
    #st.write(response)