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app.py

@@ -1,10 +1,10 @@
 """
 Netra AI - Construction Material Classifier
-MVP Demo using Gemini Vision API
+MVP Demo using Claude Vision API
 """
 
 import gradio as gr
-import google.generativeai as genai
+import anthropic
 import os
 from PIL import Image
 import base64
@@ -36,22 +36,45 @@ def classify_material(image):
         return "Please upload an image", "", ""
 
     # Get API key from environment
-    api_key = os.getenv("GOOGLE_API_KEY", "")
+    api_key = os.getenv("ANTHROPIC_API_KEY", "")
     if not api_key:
         return "❌ System Error", "", "AI service not configured. Please contact support."
 
     try:
-        # Configure Gemini
-        genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-2.5-flash')
+        # Configure Claude
+        client = anthropic.Anthropic(api_key=api_key)
 
         # Convert to PIL Image if needed
         if not isinstance(image, Image.Image):
             image = Image.fromarray(image)
 
+        # Convert image to base64
+        buffered = BytesIO()
+        image.save(buffered, format="JPEG")
+        image_data = base64.b64encode(buffered.getvalue()).decode("utf-8")
+
         # Generate classification
-        response = model.generate_content([CLASSIFICATION_PROMPT, image])
-        result_text = response.text.strip()
+        response = client.messages.create(
+            model="claude-opus-4-6",
+            max_tokens=150,
+            messages=[
+                {
+                    "role": "user",
+                    "content": [
+                        {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": "image/jpeg",
+                                "data": image_data,
+                            },
+                        },
+                        {"type": "text", "text": CLASSIFICATION_PROMPT}
+                    ],
+                }
+            ],
+        )
+        result_text = response.content[0].text.strip()
 
         # Parse response
         lines = result_text.split('\n')