import os
import gradio as gr
import google.generativeai as genai

# Configure Gemini API
GEMINI_API_KEY =  os.environ["GEMINI_API_KEY"]
genai.configure(api_key=GEMINI_API_KEY)

# Create the model with the same configuration as the sample
generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 40,
    "max_output_tokens": 8192,
    "response_mime_type": "text/plain",
}

model = genai.GenerativeModel(
    model_name="gemini-1.5-pro",
    generation_config=generation_config,
    system_instruction="You are an expert in detecting objects from xray image. Your job is to detect the objects from x-ray images.",
)

def analyze_image(image, instruction):
    """Analyze the uploaded image using Gemini model"""
    try:
        if image is None:
            return "Please upload an image to analyze."
            
        # Use default instruction if none provided
        if not instruction:
            instruction = "Here is an xray image, describe all objects you can see in this image and their relative positions to each other."
            
        # Start a new chat session
        chat = model.start_chat()
        
        # Upload and analyze the image
        image_file = genai.upload_file(image)
        
        # Send the image with the provided instruction
        response = chat.send_message([
            image_file,
            instruction
        ])
        
        return response.text
    except Exception as e:
        return f"Error analyzing image: {str(e)}\nPlease make sure you've uploaded a valid image file."

# Get example images from the examples directory
examples_dir = os.path.join(os.path.dirname(__file__), "examples")
example_images = []
if os.path.exists(examples_dir):
    example_images = [
        [os.path.join(examples_dir, f), "Here is an xray image, do you see keys in this image, if yes then describe its nature in few words and location relative to the other objects."]
        for f in os.listdir(examples_dir) 
        if f.lower().endswith(('.png', '.jpg', '.jpeg'))
    ]

# Create Gradio interface
iface = gr.Interface(
    fn=analyze_image,
    inputs=[
        gr.Image(type="filepath", label="Upload X-ray Image"),
        gr.Textbox(
            label="Instruction",
            placeholder="Enter your instruction (e.g., 'do you see keys in this image? If yes, describe their location')",
            value="Here is an xray image, do you see keys in this image, if yes then describe its nature in few words and location relative to the other objects."
        )
    ],
    outputs=gr.Textbox(label="Analysis Result", lines=10),
    title="X-ray Image Object Detection",
    description="Upload an X-ray image and provide instructions for analysis. The model will detect and describe objects based on your instructions.",
    examples=example_images,
    cache_examples=True
)

# Launch the app
if __name__ == "__main__":
    iface.launch()