RAHULJUNEJA33/AgenticAI_DecisionTree_app

1import streamlit as st2from faiss import IndexFlatL23import numpy as np4from transformers import T5Tokenizer, T5ForConditionalGeneration5from graphviz import Digraph6import torch7 8# Initialize T5 model for both summarization and feature extraction9tokenizer = T5Tokenizer.from_pretrained("t5-large")10model = T5ForConditionalGeneration.from_pretrained("t5-large")11 12# Initialize FAISS index13index = IndexFlatL2(1024)  # T5 embeddings are 1024-dimensional14responses = []  # Store responses for FAISS15 16# Streamlit page setup17st.title("Banking AI Decision-Making Assistant 🤖")18st.markdown("""19### **Understanding AI Reasoning Methods**20Before answering, here’s how AI chooses the best reasoning method for your banking use case:21| Method | How It Works | Best For | Example Use Cases |22|--------|-------------|----------|------------------|23| **Chain-of-Thought (CoT)** | Step-by-step reasoning | Math, logic, coding | Solving word problems, code generation |24| **Tree-of-Thoughts (ToT)** | Explores multiple solution paths | Games, decision-making | Chess, strategic planning |25| **Self-Consistency (SC)** | Selects the most frequent correct answer | Fact-checking, accuracy | Medical diagnosis, legal cases |26| **PAL (Program-Aided LMs)** | Uses external tools for precise answers | Math, finance, databases | Financial projections, data queries |27| **ReAct (Reasoning + Acting)** | AI interacts with tools & takes actions | AI Agents, automation | AI assistants, automated workflows |28| **Graph-of-Thoughts (GoT)** | Thoughts form a flexible network | Research, innovation | Scientific discovery, brainstorming |29""")30 31# Collect use case from the user32st.markdown("### **Describe Your Banking Use Case:**")33use_case = st.text_area("Enter your banking use case:")34 35# Initialize session state for responses36if 'responses' not in st.session_state:37    st.session_state.responses = {}38 39# Collect responses from user - checkboxes for independent selection40st.session_state.responses['multiple_factors'] = st.checkbox(41    "🔄 Does this involve multiple decision factors? (e.g., risk, compliance, fraud)",42    value=False,43    key="multiple_factors"44)45 46st.session_state.responses['real_time_validation'] = st.checkbox(47    "⏳ Does this require real-time validation? (e.g., fraud detection, transaction monitoring)",48    value=False,49    key="real_time_validation"50)51 52st.session_state.responses['user_feedback'] = st.checkbox(53    "👥 Does this need user feedback handling? (e.g., customer disputes, support tickets)",54    value=False,55    key="user_feedback"56)57 58st.session_state.responses['complexity'] = st.checkbox(59    "🧩 Is the decision-making process complex? (e.g., multi-step approvals, AI model predictions)",60    value=False,61    key="complexity"62)63 64st.session_state.responses['security_concern'] = st.checkbox(65    "🔐 Are there security concerns? (e.g., sensitive data, encryption, compliance)",66    value=False,67    key="security_concern"68)69 70st.session_state.responses['automation_level'] = st.checkbox(71    "🤖 Is this process fully automated? (e.g., auto-loan approvals, AI-driven compliance checks)",72    value=False,73    key="automation_level"74)75 76# Function to determine the best AI method based on responses77def determine_method(responses):78    """Determines the best AI method based on user responses."""79    if responses['multiple_factors'] and responses['complexity']:80        rationale = "Yes, this requires multi-step decision-making, strategic planning."81        return "Tree-of-Thoughts (ToT)", rationale82    elif responses['real_time_validation']:83        rationale = "Yes, this requires real-time data validation for fraud detection or monitoring."84        return "PAL (Program-Aided LMs)", rationale85    elif responses['user_feedback']:86        rationale = "Yes, this involves dynamic user feedback handling."87        return "ReAct (Reasoning + Acting)", rationale88    elif responses['security_concern']:89        rationale = "Yes, there are concerns regarding data security and accuracy."90        return "Self-Consistency (SC)", rationale91    elif responses['automation_level']:92        rationale = "Yes, this process requires a fully automated system with external tools."93        return "PAL (Program-Aided LMs)", rationale94    else:95        rationale = "No, this decision-making is more straightforward and does not involve complex factors."96        return "Chain-of-Thought (CoT)", rationale97 98# Function to store responses in FAISS index99def store_response(responses):100    """Stores user responses in FAISS."""101    response_str = " ".join([f"{key}: {value}" for key, value in responses.items()])102    inputs = tokenizer(response_str, return_tensors="pt", padding=True, truncation=True)103    with torch.no_grad():  # Disable gradient calculation for inference104        outputs = model.encoder(inputs["input_ids"])  # Encoder for feature extraction105        embeddings = outputs.last_hidden_state.mean(dim=1).detach().numpy()  # Use mean of hidden states as embedding106 107    # Add the embeddings to the FAISS index108    index.add(np.array(embeddings))109 110# Function to generate a decision tree visualization111def visualize_decision_tree(responses, selected_method, rationale):112    """Generates a decision tree visualization using Graphviz."""113    dot = Digraph()114    dot.node("Use Case Input", "🏦 Banking Use Case")115    dot.node("Multiple Decision Factors", f"Yes: {responses['multiple_factors']}" if responses['multiple_factors'] else "No")116    dot.node("Real-Time Validation", f"Yes: {responses['real_time_validation']}" if responses['real_time_validation'] else "No")117    dot.node("User Feedback Handling", f"Yes: {responses['user_feedback']}" if responses['user_feedback'] else "No")118    dot.node("Complexity", f"High: {responses['complexity']}" if responses['complexity'] else "Low")119    dot.node("Security Concern", f"Yes: {responses['security_concern']}" if responses['security_concern'] else "No")120    dot.node("Automation Level", f"Automated: {responses['automation_level']}" if responses['automation_level'] else "Human Oversight")121    dot.node("Final Method", f"🎯 {selected_method}\nRationale: {rationale}")122 123    # Connect nodes124    dot.edge("Use Case Input", "Multiple Decision Factors")125    dot.edge("Multiple Decision Factors", "Real-Time Validation")126    dot.edge("Real-Time Validation", "User Feedback Handling")127    dot.edge("User Feedback Handling", "Complexity")128    dot.edge("Complexity", "Security Concern")129    dot.edge("Security Concern", "Automation Level")130    dot.edge("Automation Level", "Final Method")131 132    st.graphviz_chart(dot)133 134# Summarization using T5135def get_summary(use_case):136    """Generates a summary using T5."""137    try:138        inputs = tokenizer(f"summarize: {use_case}", return_tensors="pt", max_length=512, truncation=True)139        summary_ids = model.generate(inputs["input_ids"], max_length=200, num_beams=4, early_stopping=True)140        summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)141        return summary142    except Exception as e:143        st.error(f"❌ Error generating summary: {e}")144        return None145 146# Analyze button147if st.button("🔍 Analyze Use Case"):148    # Get summary of the use case149    summary = get_summary(use_case)150    if summary:151        st.write("### **📄 Summary of Your Use Case:**")152        st.write(summary)153 154        # Determine the best AI method155        method, rationale = determine_method(st.session_state.responses)156        st.write(f"## 🚀 Recommended AI Method: {method}")157        st.write(f"### Reasoning: {rationale}")158 159        # Store response in FAISS index160        store_response(st.session_state.responses)161 162        # Visualize the decision tree163        visualize_decision_tree(st.session_state.responses, method, rationale)