import gradio as gr
import google.generativeai as genai
import os
import time
import json # We need this library to parse the JSON output
from dotenv import load_dotenv

# --- Configuration ---
load_dotenv()

try:
    genai.configure(api_key=os.environ["GEMINI_API_KEY"])
except KeyError:
    raise gr.Error("FATAL: GEMINI_API_KEY not found. Please set it in your Hugging Face Space secrets.")

# --- Core Function with JSON Parsing ---

def analyze_device_condition(video_file_path):
    """
    Analyzes a video and returns structured JSON data with three fields.
    """
    if not video_file_path:
        return "Please upload video", "", "" # Return empty strings for the other fields

    try:
        # 1. Upload the file (same as before)
        print("Log: Uploading file to Google...")
        video_file = genai.upload_file(path=video_file_path)

        while video_file.state.name == "PROCESSING":
            print("Log: Waiting for video processing...")
            time.sleep(5)
            video_file = genai.get_file(video_file.name)

        if video_file.state.name == "FAILED":
            error_message = "Error: Video processing failed."
            return error_message, "", ""
            
        print(f"Log: File processed successfully.")

        # 2. ** NEW: Update the prompt to request JSON output **
        prompt = """
        Analyze the provided video of a device. Respond ONLY with a valid JSON object.
        The JSON object must have the following three keys and nothing else:
        1. "device_type": A short string identifying the device (e.g., "iPhone 14 Pro", "Washing Machine", "Laptop").
        2. "condition": A single word describing its condition. Choose from: "Mint", "Excellent", "Good", "Fair", "Poor".
        3. "reason": A brief string explaining the condition rating, mentioning specific defects like "minor screen scratches", "dents on corner", or "clean".

        Example JSON output:
        {
          "device_type": "Samsung Galaxy S22",
          "condition": "Fair",
          "reason": "Visible cracks on the screen and scratches on the back panel."
        }
        """

        # 3. Call the Gemini Model
        model = genai.GenerativeModel(model_name="gemini-1.5-pro-latest")
        
        print("Log: Sending prompt and video to Gemini...")
        response = model.generate_content(
            [prompt, video_file],
            # Ask the model specifically for a JSON response
            generation_config=genai.types.GenerationConfig(response_mime_type="application/json")
        )
        
        print("Log: Analysis received from Gemini.")
        genai.delete_file(video_file.name)
        print(f"Log: Uploaded file deleted.")

        # 4. ** NEW: Parse the JSON response **
        print(f"Raw model response: {response.text}")
        parsed_json = json.loads(response.text)
        
        device_type = parsed_json.get("device_type", "N/A")
        condition = parsed_json.get("condition", "N/A")
        reason = parsed_json.get("reason", "N/A")

        # The function now returns three separate values
        return device_type, condition, reason

    except Exception as e:
        print(f"!!!!!!!! AN ERROR OCCURRED !!!!!!!!\n{e}")
        error_message = f"An error occurred: {e}"
        # Return the error message in the first field and empty strings for the others
        return error_message, "", ""


# --- ** NEW: Gradio Interface with Multiple Output Fields ** ---

with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 📱 Device Condition Analyzer
        Upload or record a short video of an electronic device to get a structured analysis of its condition.
        """
    )
    
    video_input = gr.Video(
        label="Upload or Record Video", 
        sources=["upload", "webcam"],
        format="mp4"
    )
    
    submit_button = gr.Button("Analyze Device", variant="primary")
    
    # Create a row layout for the output fields
    with gr.Row():
        # Create three separate Textbox outputs
        device_type_output = gr.Textbox(label="Device Type")
        condition_output = gr.Textbox(label="Condition")
        reason_output = gr.Textbox(label="Reason / Details")

    # The click function now maps to three outputs instead of one
    submit_button.click(
        fn=analyze_device_condition,
        inputs=video_input,
        # The list of outputs must match the order of the return values in the function
        outputs=[device_type_output, condition_output, reason_output],
        show_progress='full'
    )

demo.launch(debug=True)