Use Gemini list_models dynamically for chat/report model selection

backend/app.py

@@ -46,6 +46,40 @@ if GEMINI_AVAILABLE and GEMINI_API_KEY:
 elif GEMINI_AVAILABLE:
     print("⚠️  GEMINI_API_KEY not found in environment variables")
 
+
+def get_supported_gemini_models() -> List[str]:
+    """Return model names that support generateContent for this API key."""
+    if not GEMINI_AVAILABLE or not GEMINI_API_KEY:
+        return []
+
+    discovered: List[str] = []
+    try:
+        for model in genai.list_models():
+            methods = getattr(model, "supported_generation_methods", []) or []
+            if "generateContent" not in methods:
+                continue
+
+            raw_name = getattr(model, "name", "")
+            if not raw_name:
+                continue
+
+            discovered.append(raw_name)
+            # Some SDK calls accept short names while discovery returns models/<name>.
+            if raw_name.startswith("models/"):
+                discovered.append(raw_name[len("models/"):])
+    except Exception as e:
+        print(f"⚠️ Could not list Gemini models: {e}")
+        return []
+
+    # De-duplicate while preserving order.
+    unique_models: List[str] = []
+    seen = set()
+    for name in discovered:
+        if name not in seen:
+            unique_models.append(name)
+            seen.add(name)
+    return unique_models
+
 # Pydantic models for LLM endpoints
 class ChatMessage(BaseModel):
     role: str
@@ -74,15 +108,7 @@ class SPAStaticFiles(StaticFiles):
 @app.get("/health")
 async def health_check():
     """Health check endpoint"""
-    available_models = []
-    if GEMINI_AVAILABLE and GEMINI_API_KEY:
-        try:
-            # List available Gemini models
-            for model in genai.list_models():
-                if 'generateContent' in model.supported_generation_methods:
-                    available_models.append(model.name)
-        except Exception as e:
-            print(f"⚠️ Could not list models: {e}")
+    available_models = get_supported_gemini_models()
     
     return {
         "status": "healthy", 
@@ -128,17 +154,29 @@ async def chat_endpoint(request: ChatRequest):
 Provide expert guidance on examination techniques, findings interpretation, and management guidelines. \
 Be professional, evidence-based, and concise."""
         
-        # Try different model names for compatibility
-        model_names = [
-            "models/gemini-1.5-pro-latest",
+        # Prefer modern fast models, then fall back to any model exposed by this key.
+        available_models = get_supported_gemini_models()
+        if not available_models:
+            raise Exception(
+                "No Gemini models with generateContent are available for this API key. "
+                "Check API key permissions and Gemini API enablement."
+            )
+
+        preferred_models = [
+            "models/gemini-2.0-flash",
+            "gemini-2.0-flash",
+            "models/gemini-2.0-flash-lite",
+            "gemini-2.0-flash-lite",
+            "models/gemini-1.5-flash",
+            "gemini-1.5-flash",
             "models/gemini-1.5-pro",
-            "models/gemini-1.0-pro-latest",
-            "models/gemini-1.0-pro",
-            "models/gemini-pro",
-            "gemini-1.5-pro-latest",
             "gemini-1.5-pro",
+            "models/gemini-pro",
             "gemini-pro",
         ]
+        model_names = [m for m in preferred_models if m in available_models]
+        model_names.extend(m for m in available_models if m not in model_names)
+        print(f"✅ Chat available models: {available_models}")
         
         response_text = None
         used_model = None
@@ -246,17 +284,29 @@ Analyse ALL the clinical data and the attached colposcopy images to generate a p
         
         full_prompt = "\n".join(prompt_parts)
         
-        # Try different model names for compatibility
-        model_names = [
-            "models/gemini-1.5-pro-latest",
+        # Prefer modern fast models, then fall back to any model exposed by this key.
+        available_models = get_supported_gemini_models()
+        if not available_models:
+            raise Exception(
+                "No Gemini models with generateContent are available for this API key. "
+                "Check API key permissions and Gemini API enablement."
+            )
+
+        preferred_models = [
+            "models/gemini-2.0-flash",
+            "gemini-2.0-flash",
+            "models/gemini-2.0-flash-lite",
+            "gemini-2.0-flash-lite",
+            "models/gemini-1.5-flash",
+            "gemini-1.5-flash",
             "models/gemini-1.5-pro",
-            "models/gemini-1.0-pro-latest",
-            "models/gemini-1.0-pro",
-            "models/gemini-pro",
-            "gemini-1.5-pro-latest",
             "gemini-1.5-pro",
+            "models/gemini-pro",
             "gemini-pro",
         ]
+        model_names = [m for m in preferred_models if m in available_models]
+        model_names.extend(m for m in available_models if m not in model_names)
+        print(f"✅ Report available models: {available_models}")
         
         response_text = None
         used_model = None