Update app.py

app.py

@@ -64,7 +64,75 @@ def classify_cat(image):
     # CLIP
     clip_labels = [f"a photo of a {label}" for label in CAT_LABELS]
     clip_results = clip_classifier(image, candidate_labels=clip_labels)
-
+def classify_with_openai(image_path):
+    base64_image = encode_image(image_path)
+
+    prompt = f"""
+You are a big cat classifier.
+
+Classify the image into exactly one of these labels:
+
+{CAT_LABELS}
+
+Return ONLY valid JSON.
+Do not use markdown.
+Do not use code fences.
+Do not add explanations.
+
+Required format:
+{{"label":"one_of_{CAT_LABELS}","confidence":0.0}}
+"""
+
+    try:
+        response = client.responses.create(
+            model="gpt-4.1-mini",
+            input=[
+                {
+                    "role": "user",
+                    "content": [
+                        {"type": "input_text", "text": prompt},
+                        {
+                            "type": "input_image",
+                            "image_url": f"data:image/jpeg;base64,{base64_image}"
+                        }
+                    ]
+                }
+            ]
+        )
+
+        text = response.output_text.strip()
+        text = text.replace("```json", "").replace("```", "").strip()
+
+        start = text.find("{")
+        end = text.rfind("}")
+        if start != -1 and end != -1 and end > start:
+            text = text[start:end+1]
+
+        result = json.loads(text)
+
+        label = str(result["label"]).strip().lower()
+        confidence = float(result["confidence"])
+
+        if label not in CAT_LABELS:
+            raise ValueError(f"Invalid label: {label}")
+
+        confidence = max(0.0, min(1.0, confidence))
+        remaining = 1.0 - confidence
+        num_other = len(CAT_LABELS) - 1
+
+        distribution = {}
+
+        for l in CAT_LABELS:
+            if l == label:
+                distribution[l] = confidence
+            else:
+                distribution[l] = remaining / num_other
+
+        return distribution
+
+    except Exception:
+        return {"unknown": 1.0}
+        
     clip_output = {}
     for r in clip_results:
         label = r["label"].replace("a photo of a ", "").lower()