app.py

import gradio as gr
import os
import json
import mimetypes
from dotenv import load_dotenv

# Your exact requested imports
from google import genai
from google.genai import types

# --- Configuration and Client Initialization ---
load_dotenv()

# Initializing the client exactly as in your code
try:
    client = genai.Client(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 Gradio Function ---

def analyze_device_condition(video_file_path):
    if not video_file_path:
        return "Please upload video", "", ""

    try:
        print(f"Log: Starting analysis for video: {video_file_path}")

        # 1. Prepare video file for the client API
        mime_type, _ = mimetypes.guess_type(video_file_path)
        if not mime_type or not mime_type.startswith("video"):
            raise ValueError("Unsupported file type. Please upload a valid video.")
        
        with open(video_file_path, "rb") as video:
            video_part = types.Part(
                inline_data=types.Blob(mime_type=mime_type, data=video.read())
            )
        
        # 2. Prepare the prompt and model settings from your code
        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.
        2. "condition": A single word describing its condition. Choose from: "Mint", "Excellent", "Good", "Fair", "Poor".
        3. "reason": A brief string explaining the condition rating.
        """
        
        # USING YOUR EXACT REQUESTED MODEL NAME
        model_name = "gemini-2.5-flash"
        
        generate_content_config = types.GenerateContentConfig(
            temperature=0.2,
            response_mime_type="application/json"
        )
        
        # The contents list must contain both the text prompt and the video part
        contents = [prompt, video_part]

        # 3. Call the Gemini API using the client.generate_content method
        print(f"Log: Sending request to model: {model_name}...")
        response = client.generate_content(
            model=f"models/{model_name}",
            contents=contents,
            generation_config=generate_content_config,
        )

        print("Log: Analysis received.")

        # 4. Parse the JSON response
        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")

        return device_type, condition, reason

    except Exception as e:
        print(f"!!!!!!!! AN ERROR OCCURRED !!!!!!!!\n{e}")
        error_message = f"An error occurred: {e}"
        return error_message, "", ""

# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📱 Device Condition Analyzer")
    video_input = gr.Video(label="Upload or Record Video", sources=["upload", "webcam"], format="mp4")
    submit_button = gr.Button("Analyze Device", variant="primary")
    with gr.Row():
        device_type_output = gr.Textbox(label="Device Type")
        condition_output = gr.Textbox(label="Condition")
        reason_output = gr.Textbox(label="Reason / Details")

    submit_button.click(
        fn=analyze_device_condition,
        inputs=video_input,
        outputs=[device_type_output, condition_output, reason_output],
        show_progress='full'
    )

demo.launch(debug=True)