Update app.py

app.py

@@ -1003,6 +1003,120 @@ def handsome_embeddings():
     except requests.exceptions.RequestException as e:
         return jsonify({"error": str(e)}), 500
 
+@app.route('/handsome/v1/images/generations', methods=['POST'])
+def handsome_images_generations():
+    if not check_authorization(request):
+        return jsonify({"error": "Unauthorized"}), 401
+
+    data = request.get_json()
+    if not data or 'model' not in data:
+        return jsonify({"error": "Invalid request data"}), 400
+
+    model_name = data.get('model')
+    
+    request_type = determine_request_type(
+        model_name,
+        image_models,
+        free_image_models
+    )
+    
+    api_key = select_key(request_type, model_name)
+
+    if not api_key:
+         return jsonify(
+             {
+                 "error": (
+                     "No available API key for this "
+                     "request type or all keys have "
+                     "reached their limits"
+                 )
+             }
+         ), 429
+    
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+
+    siliconflow_data = {
+        "model": model_name,
+        "prompt": data.get("prompt"),
+        "image_size": data.get("size", "1024x1024"),
+        "batch_size": data.get("n", 1),
+        "num_inference_steps": data.get("steps", 20),
+        "guidance_scale": data.get("guidance_scale", 7.5),
+        "negative_prompt": data.get("negative_prompt"),
+        "seed": data.get("seed"),
+        "prompt_enhancement": False,
+    }
+
+    if siliconflow_data["batch_size"] < 1:
+        siliconflow_data["batch_size"] = 1
+    if siliconflow_data["batch_size"] > 4:
+        siliconflow_data["batch_size"] = 4
+
+    if siliconflow_data["num_inference_steps"] < 1:
+        siliconflow_data["num_inference_steps"] = 1
+    if siliconflow_data["num_inference_steps"] > 50:
+        siliconflow_data["num_inference_steps"] = 50
+        
+    if siliconflow_data["guidance_scale"] < 0:
+         siliconflow_data["guidance_scale"] = 0
+    if siliconflow_data["guidance_scale"] > 100:
+         siliconflow_data["guidance_scale"] = 100
+    
+    if siliconflow_data["image_size"] not in ["1024x1024", "512x1024", "768x512", "768x1024", "1024x576", "576x1024"]:
+        siliconflow_data["image_size"] = "1024x1024"
+
+    try:
+        start_time = time.time()
+        response = requests.post(
+            TEST_MODEL_ENDPOINT,  # Assuming the same endpoint for image generation
+            headers=headers,
+            json=siliconflow_data,
+            timeout=120
+        )
+
+        if response.status_code == 429:
+            return jsonify(response.json()), 429
+
+        response.raise_for_status()
+        end_time = time.time()
+        response_json = response.json()
+        total_time = end_time - start_time
+        
+        try:
+            images = response_json.get("images", [])
+            if isinstance(images, list):
+                openai_images = [{"url": image} for image in images]
+            else:
+                openai_images = []
+        except (KeyError, ValueError, IndexError) as e:
+            logging.error(
+                f"解析响应 JSON 失败: {e}, "
+                f"完整内容: {response_json}"
+            )
+            openai_images = []
+
+        logging.info(
+            f"使用的key: {api_key}, "
+            f"总共用时: {total_time:.4f}秒, "
+            f"使用的模型: {model_name}"
+        )
+
+        with data_lock:
+            request_timestamps.append(time.time())
+            token_counts.append(0)  # Image generation doesn't use tokens
+
+        return jsonify({
+            "created": int(time.time()),
+            "data": openai_images
+        })
+
+    except requests.exceptions.RequestException as e:
+        logging.error(f"请求转发异常: {e}")
+        return jsonify({"error": str(e)}), 500
+
 if __name__ == '__main__':
     import json
     logging.info(f"环境变量：{os.environ}")