Initial code upload

api_server.py

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+import os
+import torch
+import torch.nn as nn
+from torchvision import models, transforms
+from fastapi import FastAPI, Request
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import base64
+import requests  # هنستخدم دي عشان نكلم جوجل مباشرة
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# --- الموديل المحلي (DenseNet) ---
+device = torch.device("cpu")
+def load_medical_model():
+    model = models.densenet121(weights=None)
+    model.classifier = nn.Sequential(nn.Dropout(0.2), nn.Linear(model.classifier.in_features, 14))
+    if os.path.exists('best_densenet121.pth'):
+        try:
+            checkpoint = torch.load('best_densenet121.pth', map_location=device)
+            model.load_state_dict(checkpoint)
+            model.eval()
+            return model
+        except: return None
+    return None
+
+medical_model = load_medical_model()
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
+])
+class_names = ["Atelectasis", "Cardiomegaly", "Effusion", "Infiltration", "Mass", "Nodule", "Pneumonia", "Pneumothorax", "Consolidation", "Edema", "Emphysema", "Fibrosis", "Pleural_Thickening", "Hernia"]
+
+# دالة ذكية لإرسال النص لجوجل بدون مكتبة (عشان نتفادى الـ 404)
+def ask_gemini_direct(prompt, api_key):
+    url = f"https://generativelanguage.googleapis.com/v1/models/gemini-1.5-flash:generateContent?key={api_key}"
+    payload = {
+        "contents": [{"parts": [{"text": prompt}]}]
+    }
+    headers = {'Content-Type': 'application/json'}
+    response = requests.post(url, json=payload, headers=headers)
+    
+    if response.status_code == 200:
+        return response.json()['candidates'][0]['content']['parts'][0]['text']
+    else:
+        return f"Error from Google API: {response.status_code} - {response.text}"
+
+@app.post("/chat")
+async def chat_endpoint(request: Request):
+    try:
+        data = await request.json()
+        api_key = os.getenv("GEMINI_API_KEY")
+        
+        # التقاط الرسالة من Lovable
+        user_message = ""
+        if "messages" in data and len(data["messages"]) > 0:
+            user_message = data["messages"][-1].get("content", "")
+        elif "message" in data:
+            user_message = data["message"]
+            
+        image_data = data.get("image")
+
+        # 1. لو فيه صورة
+        if image_data and medical_model:
+            encoded = image_data.split(",", 1)[1] if "," in image_data else image_data
+            image = Image.open(io.BytesIO(base64.b64decode(encoded))).convert('RGB')
+            input_tensor = transform(image).unsqueeze(0).to(device)
+            with torch.no_grad():
+                outputs = medical_model(input_tensor)
+                probs = torch.sigmoid(outputs)
+                preds = (probs > 0.5).nonzero(as_tuple=True)[1]
+                diseases = [class_names[i] for i in preds]
+            
+            res_text = ", ".join(diseases) if diseases else "نتائج سليمة"
+            prompt = f"المريض رفع أشعة والتحليل الأولي: {res_text}. اشرح ده بالعربي باختصار وبأسلوب طبي."
+            ai_response = ask_gemini_direct(prompt, api_key)
+            return {"response": ai_response, "analysis": diseases}
+
+        # 2. لو نص بس (زي كلمة كحة)
+        if user_message:
+            ai_response = ask_gemini_direct(f"أنت مساعد طبي في Healytics. رد بالعربي على: {user_message}", api_key)
+            return {"response": ai_response}
+
+        return {"response": "أهلاً بك في Healytics! كيف يمكنني مساعدتك؟"}
+
+    except Exception as e:
+        return {"response": f"حدث خطأ في النظام: {str(e)}"}

requirements.txt

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+fastapi
+uvicorn
+requests
+torch
+torchvision
+pillow
+python-multipart