Merge pull request #1 from amitdevx/v1.2.0

Upgrade to Gemini 3 models (v1.2.0)

Author: amitdevx

ARCHITECTURE.md

@@ -56,7 +56,7 @@ User Query → Runner → Root Agent (professor_profiler_agent)
 ```
 
 ## Technology Stack
-- **LLM**: Google Gemini 2.5 (Flash for classification, Pro for analysis)
+- **LLM**: Google Gemini 3 (Flash for classification, Pro for analysis)
 - **Framework**: Custom ADK (Agent Development Kit)
 - **PDF Processing**: pypdf
 - **Visualization**: matplotlib
@@ -67,25 +67,25 @@ User Query → Runner → Root Agent (professor_profiler_agent)
 ## Agent Specifications
 
 ### Root Agent: professor_profiler_agent
-- **Model**: gemini-2.5-pro
+- **Model**: gemini-3-pro
 - **Role**: Orchestrator
 - **Tools**: read_pdf_content, visualize_trends, analyze_statistics
 - **Sub-agents**: taxonomist, trend_spotter, strategist
 
 ### Sub-Agent: taxonomist
-- **Model**: gemini-2.5-flash (cost-effective for classification)
+- **Model**: gemini-3-flash (cost-effective for classification)
 - **Role**: Question classification
 - **Output**: Tagged questions with topics and Bloom's levels
 - **Callback**: Suppresses intermediate output
 
 ### Sub-Agent: trend_spotter
-- **Model**: gemini-2.5-pro (complex analysis)
+- **Model**: gemini-3-pro (complex analysis)
 - **Role**: Statistical trend analysis
 - **Output**: Shift report with frequency and cognitive trends
 - **Callback**: Suppresses intermediate output
 
 ### Sub-Agent: strategist
-- **Model**: gemini-2.5-pro
+- **Model**: gemini-3-pro
 - **Role**: Study plan generation
 - **Output**: Hit List, Safe Zone, Drop List recommendations
 

FEATURES.md

@@ -5,7 +5,7 @@
 ### 1. Multi-Agent System ✓
 - [x] **Hub-and-Spoke Architecture**: Root orchestrator (`professor_profiler_agent`) + 3 specialized sub-agents
 - [x] **Sequential Agents**: Taxonomist → Trend Spotter → Strategist workflow
-- [x] **Agent Powered by LLM**: All agents use Google Gemini 2.5 (Flash/Pro)
+- [x] **Agent Powered by LLM**: All agents use Google Gemini 3 (Flash/Pro)
 - [x] **Parallel Processing Ready**: Batch question classification infrastructure
 - [x] **Agent Delegation**: Root agent delegates specialized tasks
 
@@ -98,7 +98,7 @@
 - **Test Coverage**: 5 comprehensive tests
 - **Agent Count**: 4 (1 root + 3 sub-agents)
 - **Custom Tools**: 4
-- **API Integration**: Google Gemini 2.5
+- **API Integration**: Google Gemini 3
 - **Architecture Pattern**: Hub-and-Spoke
 
 ## 🎯 Features Demonstrated
@@ -156,7 +156,7 @@ python demo.py
 2. **Tool Integration**: Custom tools with Gemini function calling
 3. **State Management**: Sessions and long-term memory
 4. **Production Patterns**: Logging, tracing, metrics, error handling
-5. **API Integration**: Google Gemini 2.5 with streaming responses
+5. **API Integration**: Google Gemini 3 with streaming responses
 6. **Testing**: Integration tests and comprehensive validation
 7. **Documentation**: Clear architecture and usage documentation
 

README.md

@@ -1,7 +1,7 @@
 # Professor Profiler 
 
 [![Python](https://img.shields.io/badge/Python-3.11%2B-blue?style=flat&logo=python&logoColor=white)](https://www.python.org/)
-[![Google Gemini](https://img.shields.io/badge/Google%20Gemini-2.5%20Pro-8E44AD?style=flat&logo=google&logoColor=white)](https://ai.google.dev/)
+[![Google Gemini](https://img.shields.io/badge/Google%20Gemini-3%20Pro-8E44AD?style=flat&logo=google&logoColor=white)](https://ai.google.dev/)
 [![Architecture](https://img.shields.io/badge/Architecture-Hub%20%26%20Spoke-orange?style=flat)](https://github.com/uffamit/Professor_Profiler)
 [![License](https://img.shields.io/badge/License-MIT-green?style=flat)](LICENSE)
 [![Quality Assurance Pipeline](https://github.com/uffamit/Professor_Profiler/actions/workflows/quality-assurance.yml/badge.svg)](https://github.com/uffamit/Professor_Profiler/actions/workflows/quality-assurance.yml)
@@ -13,7 +13,7 @@
 
 ## Overview
 
-**Professor Profiler** is not just a document reader; it is a **Hierarchical Multi-Agent System (HMAS)** designed to mimic the cognitive process of an expert tutor. By orchestrating specialized AI agents powered by **Google Gemini 2.5**, it ingests raw exam PDFs, breaks them down into cognitive components (Bloom's Taxonomy), identifies statistical patterns, and formulates actionable "Safe Zone" and "Hit List" study plans.
+**Professor Profiler** is not just a document reader; it is a **Hierarchical Multi-Agent System (HMAS)** designed to mimic the cognitive process of an expert tutor. By orchestrating specialized AI agents powered by **Google Gemini 3**, it ingests raw exam PDFs, breaks them down into cognitive components (Bloom's Taxonomy), identifies statistical patterns, and formulates actionable "Safe Zone" and "Hit List" study plans.
 
 This project serves as a reference implementation for:
 *   **Hub-and-Spoke Agent Architecture**
@@ -43,7 +43,7 @@ flowchart TD
     end
 
     subgraph Agent_Layer [" Agent Hierarchy"]
-        Root[<b>ROOT AGENT</b><br><i>Gemini 2.5 Pro</i><br>The Project Manager]
+        Root[<b>ROOT AGENT</b><br><i>Gemini 3 Pro</i><br>The Project Manager]
         
         subgraph Workers ["Specialized Sub-Agents"]
             Taxonomist[<b>Taxonomist</b><br><i>Gemini Flash</i><br>Topic & Bloom's Classification]
@@ -124,7 +124,7 @@ sequenceDiagram
 | Component | Technology | Description |
 | :--- | :--- | :--- |
 | **Core Logic** | Python 3.10+ | Type-hinted, async-native codebase. |
-| **LLM Engine** | Google Gemini 2.5 | Uses `Pro` for reasoning and `Flash` for high-speed tasks. |
+| **LLM Engine** | Google Gemini 3 | Uses `Pro` for reasoning and `Flash` for high-speed tasks. |
 | **Orchestrator** | Google ADK (Custom) | Custom implementation of the Agent Development Kit patterns. |
 | **Document Processing** | `pypdf` | Robust text extraction from standardized exam papers. |
 | **Visualization** | `matplotlib` | Generates distribution bar charts and pie charts on the fly. |

profiler_agent/config.py

@@ -15,7 +15,7 @@
 
 @dataclass
 class ProfilerConfiguration:
-    classifier_model: str = "gemini-2.5-flash"
-    analyzer_model: str = "gemini-2.5-pro"
+    classifier_model: str = "gemini-3-flash"
+    analyzer_model: str = "gemini-3-pro"
 
 config = ProfilerConfiguration()