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README.md

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-title: Hello
-emoji: 👀
-colorFrom: purple
-colorTo: pink
-sdk: gradio
-sdk_version: 6.5.1
-app_file: app.py
-pinned: false
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-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# 🍸 LocalAGI: The AI Sommelier
+
+## 📖 Overview
+LocalAGI is a multimodal Retrieval-Augmented Generation (RAG) application that acts as an intelligent, interactive bartender. By combining state-of-the-art computer vision with vector search, the application allows users to upload a photo of any liquor bottle and instantly receive curated cocktail recipes utilizing that specific spirit from a custom-ingested library. 
+
+Engineered to run entirely on CPU-bound cloud environments (like Hugging Face Spaces), this project showcases advanced optimization techniques, including dynamic image cropping, intelligent text-splitting, and dual-pass vision logic.
+
+## ✨ Key Features
+* **Visual Brand Recognition:** Utilizes a Vision-Language Model (VLM) to read labels and identify specific alcohol brands from user-uploaded photos, going beyond generic object categorization.
+* **Custom Knowledge Base (RAG):** Ingests raw `.txt` and `.pdf` recipe books, intelligently splitting them into discrete recipe chunks using RegEx and LangChain, and stores them in a local Chroma vector database.
+* **Smart Cropping Pipeline:** Implements YOLOv8 to locate bottles or glasses in an image, applying dynamic 25% padding to isolate the label and strip away background noise.
+* **Hardware-Optimized Processing:** Features custom logic to downscale images and restrict token generation limits, allowing complex 2-billion-parameter models to run efficiently on free-tier cloud CPUs.
+* **Interactive UI:** A Gradio 6.0 interface featuring a conversational chat format, session state memory, and a hidden "Vision Debug" gallery for real-time insight into the AI's detection process.
+
+## 🛠️ Technical Stack
+* **Frontend/UI:** Gradio 6.0
+* **Computer Vision:** Ultralytics YOLOv8 (Object Detection)
+* **Vision-Language Model:** HuggingFaceTB/SmolVLM-Instruct (Label OCR & Context)
+* **Vector Database:** ChromaDB
+* **Embeddings:** `sentence-transformers/all-MiniLM-L6-v2`
+* **Orchestration:** LangChain (Document Loaders, Text Splitters)
+
+## 🧠 How It Works Under the Hood
+1. **Document Ingestion:** The user uploads a recipe book. The system uses a strict "Hard Cut" method to split the document exactly at the start of every new recipe, ensuring clean data retrieval.
+2. **Object Detection:** When a photo is uploaded, YOLOv8 scans the image for bottles (Class 39) or glasses (Class 40/41), creating a focused, padded crop of the object.
+3. **Vision Processing:** The cropped image is aggressively downscaled (384x384) and passed to SmolVLM. The model is restricted to a 15-token output to rapidly extract just the brand name (e.g., "Absolut Vodka").
+4. **Fallback Logic:** If the VLM returns a generic term (e.g., just "Vodka") due to a bad crop, the system automatically triggers a secondary pass using the full, uncropped image to guarantee brand identification.
+5. **Context Retrieval (RAG):** The extracted brand name is embedded and queried against the Chroma database, retrieving the top 4 most relevant, full-text recipes.
+6. **Chat Output:** The system formats the retrieved recipes and returns them to the user via the conversational UI.
+
+## 🚀 Future Roadmap
+* Integration with hardware-accelerated APIs (Groq/Gemini) for sub-3-second vision processing.
+* User inventory tracking to suggest recipes based on a combination of multiple owned bottles.