Update app.py

app.py

@@ -1,45 +1,85 @@
 import os
-import sys
-import logging
-from datetime import datetime
+import requests
+import json
+from flask import Flask, request, jsonify, send_from_directory
 
-# 设置日志
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-logger = logging.getLogger(__name__)
+app = Flask(__name__, static_folder='static', static_url_path='')
 
-logger.info(f"Starting application at {datetime.now()}")
+GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
+GEMINI_API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent"
 
-from flask import Flask, jsonify
-
-app = Flask(__name__)
-
-logger.info("Flask app created")
+SYSTEM_PROMPT = """
+你是一个世界顶级的皮肤科医生AI助手。你的任务是基于用户上传的皮肤图像和病历文本，进行专业的临床诊疗辅助分析。
+你的输出必须是一个单一、有效的JSON对象，不包含任何JSON格式之外的文字或标记（例如不要使用 "```json"）。
+请严格遵循以下JSON结构：
+{
+  "image_features": ["从图像中识别出的关键皮损形态学特征列表", "例如：色素网络不规则", "例如：蓝白结构"],
+  "differential_diagnosis": [
+    { "diagnosis": "可能性最高的诊断名称", "likelihood": "高", "evidence": "支持该诊断的关键图像和文本依据" },
+    { "diagnosis": "可能性次之的诊断名称", "likelihood": "中", "evidence": "支持该诊断的关键依据" }
+  ],
+  "comprehensive_analysis": "一段对整个病情的全面、综合性分析和总结。",
+  "recommendations": {
+    "further_examinations": ["建议的下一步检查列表", "例如：皮肤镜检查", "例如：皮肤活检"],
+    "treatment_suggestions": ["初步的、符合临床指南的治疗方向建议列表", "例如：外用糖皮质激素", "例如：建议手术切除"]
+  }
+}
+"""
 
 @app.route('/healthz')
 def health_check():
-    logger.info("Health check called")
+    """专门用于平台健康检查的路由，确保100%稳定"""
     return "OK", 200
 
 @app.route('/')
 def index():
-    logger.info("Root endpoint called")
-    return jsonify({
-        "message": "皮肤科AI诊断助手",
-        "status": "running",
-        "timestamp": datetime.now().isoformat()
-    })
-
-@app.route('/test')
-def test():
-    return jsonify({
-        "test": "success",
-        "env_vars": list(os.environ.keys())[:10],  # 只显示前10个环境变量
-        "working_dir": os.getcwd(),
-        "python_version": sys.version
-    })
-
-logger.info("All routes registered")
+    """提供前端HTML页面"""
+    return send_from_directory(app.static_folder, 'index.html')
+
+@app.route('/api/analyze', methods=['POST'])
+def analyze_skin():
+    """处理分析请求的核心API端点"""
+    if not GEMINI_API_KEY:
+        return jsonify({"error": "服务端未配置GEMINI_API_KEY"}), 500
+
+    data = request.json
+    if not data or 'image' not in data or 'text' not in data:
+        return jsonify({"error": "请求中缺少图像或文本信息"}), 400
+
+    image_base64 = data.get('image')
+    text_prompt = data.get('text')
+
+    headers = {'Content-Type': 'application/json'}
+    
+    payload = {
+        "contents": [{"parts": [
+            {"text": SYSTEM_PROMPT},
+            {"text": f"临床病历信息如下：\n{text_prompt}"},
+            {"inline_data": {"mime_type": "image/jpeg", "data": image_base64}}
+        ]}]
+    }
+
+    try:
+        response = requests.post(f"{GEMINI_API_URL}?key={GEMINI_API_KEY}", headers=headers, json=payload, timeout=60)
+        response.raise_for_status()
+        
+        gemini_result = response.json()
+        
+        # 稳健地提取内容
+        analysis_content_string = gemini_result['candidates']['content']['parts']['text']
+        parsed_json = json.loads(analysis_content_string)
+        
+        return jsonify(parsed_json)
+
+    except requests.exceptions.RequestException as e:
+        return jsonify({"error": f"调用Gemini API时发生网络错误: {e}"}), 500
+    except (KeyError, IndexError, TypeError):
+        error_details = response.json() if 'response' in locals() else "No response from API"
+        return jsonify({"error": "解析Gemini API响应失败，格式不正确", "details": error_details}), 500
+    except json.JSONDecodeError:
+        return jsonify({"error": "Gemini API未返回有效的JSON格式", "details": analysis_content_string}), 500
+    except Exception as e:
+        return jsonify({"error": f"发生未知错误: {e}"}), 500
 
 if __name__ == '__main__':
-    logger.info("Starting Flask app...")
     app.run(host='0.0.0.0', port=8080)