Infrastructure: Implement master/dev/vX branching, save git path, and fix username casing

backend/ai_vision.py

@@ -0,0 +1,47 @@
+import os
+from dotenv import load_dotenv
+from .ai import gemini, claude
+
+# Load environment variables from the directory where this file resides
+base_dir = os.path.dirname(os.path.abspath(__file__))
+dotenv_path = os.path.join(base_dir, ".env")
+load_dotenv(dotenv_path)
+
+def extract_label_info(image_bytes: bytes):
+    """
+    Orchestrates extraction across multiple AI providers.
+    Order: Gemini (Flash/Pro) -> Claude (Haiku/Sonnet)
+    """
+    prompt = """
+    Extract technical inventory information from this label image.
+    
+    CRITICAL INSTRUCTIONS:
+    1. Look at the most prominent text (usually top 1-2 rows). This is the product NAME and MODEL.
+    2. Extract the PART NUMBER (P/N, Model No, Type). If no explicit Part Number is found, synthesize one from the most unique identifier in the header (e.g. 'OM4-MMF-DX').
+    3. Separate the COLOR (e.g. Turquoise, Yellow, Black).
+    4. Extract CATEGORY based on the item type (e.g. Patchcord, SFP, Connector).
+    5. Extract technical SPECS (e.g. '2.0mm', '10G', '850nm').
+    
+    Return ONLY a valid JSON object:
+    {
+      "name": "Full descriptive name from header",
+      "part_number": "Unique identifier for fast scanning",
+      "category": "Broad category",
+      "color": "Color if present",
+      "specs": "Brief tech specs list",
+      "barcode": "Barcode value if visible",
+      "quantity": 1
+    }
+    """
+    
+    # 1. Try Gemini
+    result = gemini.extract(image_bytes, prompt)
+    if result:
+        return result
+        
+    # 2. Try Claude (Fallback)
+    result = claude.extract(image_bytes, prompt)
+    if result:
+        return result
+        
+    return {"error": "All AI providers failed or no API keys configured. Check your .env file."}