Update app.py

app.py

@@ -43,6 +43,8 @@ def _build_vehicle_recognition_markdown(vision_results: dict[str, Any]) -> str:
         method_note = "\nThe classifier can only predict one of the trained vehicle brands."
     elif method == "fallback":
         method_note = "\nNo trained model found. Please train the model first."
+    elif method == "manual_input":
+        method_note = "\nCar brand/model entered manually without image analysis."
 
     return f"""
 ### Vehicle recognition
@@ -142,19 +144,32 @@ def _run_advisor(
     budget_chf,
     max_monthly_rate_chf,
 ):
-    if image is None:
+    # Allow either image OR manual make_model input, but require at least one
+    if image is None and not (make_model and make_model.strip()):
         return (
-            "Please upload a car image.",
+            "Please upload a car image OR enter a known make/model.",
             "No price estimate available yet.",
             "No budget assessment available yet.",
             "No financing orientation available yet.",
-            "Please upload a car image to start the analysis.",
+            "Please provide car details to start the analysis.",
             "No disclaimer available yet.",
             {},
         )
 
-    vision_results = analyze_car_image(image)
-    predicted_class = vision_results.get("predicted_class", "Unknown")
+    # If image is provided, use vision analyzer; otherwise use manual input
+    if image is not None:
+        vision_results = analyze_car_image(image)
+        predicted_class = vision_results.get("predicted_class", "Unknown")
+    else:
+        # No image, but make_model was entered manually
+        predicted_class = "Unknown"
+        vision_results = {
+            "predicted_class": "Unknown",
+            "confidence": 0.0,
+            "method": "manual_input",
+            "notes": ["Car brand/model entered manually without image."],
+        }
+
     age = _normalize_age(car_age_years, "Age")
     make_model_input = make_model.strip() if make_model and make_model.strip() else predicted_class