Update app.py

app.py

@@ -31,6 +31,12 @@ class Vehicle(BaseModel):
     Make: str = Field(..., examples=["Toyota", "Honda", "Ford", "Suzuki"], description="The Make of the vehicle.")
     Model: str = Field(..., examples=["Corolla", "Civic", "F-150"], description="The Model of the vehicle.")
     Color: str = Field(..., example=["Red", "Blue", "Black", "White"], description="Return the color of the vehicle.")
+    Year: str = Field(None, description="The year of the vehicle.")
+    Condition: str = Field(None, description="The condition of the vehicle.")
+    Logo: str = Field(None, description="The visible logo of the vehicle, if applicable.")
+    Damage: str = Field(None, description="Any visible damage or wear and tear on the vehicle.")
+    Region: str = Field(None, description="Region or country based on the license plate or clues from the image.")
+    PlateType: str = Field(None, description="Type of license plate, e.g., government, personal.")
 
 # Parser for vehicle details
 parser = JsonOutputParser(pydantic_object=Vehicle)
@@ -64,11 +70,22 @@ def display_image_grid(image_paths, rows=2, cols=3, figsize=(10, 7)):
 @chain
 def prompt(inputs):
     prompt = [
-        SystemMessage(content="""You are an AI assistant whose job is to inspect an image and provide the desired information from the image. If the desired field is not clear or not well detected, return None for this field. Do not try to guess."""), 
+        SystemMessage(content="""You are an AI assistant tasked with extracting detailed information from a vehicle image. Please extract the following details:
+        - Vehicle type (e.g., Car, Truck, Bus)
+        - License plate number and type (if identifiable, such as personal, commercial, government)
+        - Vehicle make, model, and year (e.g., 2020 Toyota Corolla)
+        - Vehicle color and condition (e.g., Red, well-maintained, damaged)
+        - Any visible brand logos or distinguishing marks (e.g., Tesla logo)
+        - Details of any visible damage (e.g., scratches, dents)
+        - Vehicle’s region or country (based on the license plate or other clues)
+        If some details are unclear or not visible, return `None` for those fields. Do not guess or provide inaccurate information."""
+        ),
         HumanMessage(
-            content=[{"type": "text", "text": "Examine the main vehicle type, license plate number, make, model and color."},
-                     {"type": "text", "text": instructions},
-                     {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{inputs['image']}", "detail": "low"}}]
+            content=[
+                {"type": "text", "text": "Analyze the vehicle in the image and extract as many details as possible, including type, license plate, make, model, year, condition, damage, etc."},
+                {"type": "text", "text": instructions},  # include any other format instructions here
+                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{inputs['image']}", "detail": "low"}}
+            ]
         )
     ]
     return prompt