Revamp explanation generation in YOLO model using FLAN-T5, and add Florence-2 Fusion model

Fix libxcb ImportError: Add GUI Linux dependencies for OpenCV into explicit Dockerfile layer

Add packages.txt to fix OpenCV missing libxcb.so.1 GUI dependencies

Fix ImportError and RAM overload: blip_yolo_model seamlessly reuses blip_model instances natively

Expand to 5 models safely, no secrets

Simplify down to only 3 pure models: Local, Gemini, Groq Llama 4 Scout. Removed broken 90B/Fusion models.

Remove Fusion. Provide two purely native Groq endpoints (90b and Scout)

Add native Groq vision alongside Fusion model

Switch Groq strategy to Fusion (BLIP + LLaMA 3.3 70B Text) to maximize rate limits instantly

Update Groq to Llama 4 Scout as Llama 3.2 is decommissioned

Add Groq Llama 3.2 Vision implementation with strict formatting constraints

Remove Florence and HF API models, keep only Gemini and local pipelines

Fix HF Vision API URL to proper router domain

Switch HF Vision pure API to raw requests to expose HTTP errors

Switch HF Vision pure API to Llava 1.5 to avoid timeout payload drops

Switch HF Vision API strictly to Llama Vision

Implement 5th Option: Raw HF Serverless Vision-Instruct API

Migrate text api to official hf hub and bypass gated models

Implement ultimate Florence + Llama 70B text fusion pipeline

Pin transformers to 4.45.2 for Florence-2

Fix PyTorch dependency version conflict

Fix Florence-2 Flash Attention dependency crash

Implement local Florence-2 fallback

Switch HF API Model to Qwen2.5-VL due to Llama server issues

Fix Hugging Face API URL routing

Add Hugging Face Serverless API option (Option 3)

Initialize VQA system deployment

Initial commit of GenAI VQA System