app.py

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

from google import genai
from google.genai import types

# Import our new DINO processing function
from dino_processor import process_video_with_dino

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

# --- Main Gradio Function ---
def full_analysis(video_file_path):
    if not video_file_path:
        # Return empty values for all components
        return "Please upload a video first.", "", "", None, None

    # --- DINO Analysis ---
    print("--- Starting DINO Analysis ---")
    try:
        # This function will return a list of tuples: [(overlay1, attn1), (overlay2, attn2), ...]
        dino_results = process_video_with_dino(video_file_path)
        overlay_images = [res[0] for res in dino_results]
        attention_maps = [res[1] for res in dino_results]
        print("--- DINO Analysis Complete ---")
    except Exception as e:
        print(f"ERROR during DINO processing: {e}")
        # Return an error message and empty galleries
        return f"Error in DINO processing: {e}", "", "", None, None

    # --- Gemini Analysis ---
    print("--- Starting Gemini Analysis ---")
    uploaded_file = None
    try:
        # Wait for DINO to finish before starting the Gemini upload
        uploaded_file = client.files.upload(file=video_file_path)
        while uploaded_file.state.name == "PROCESSING":
            time.sleep(5)
            uploaded_file = client.files.get(name=uploaded_file.name)
        if uploaded_file.state.name != "ACTIVE":
            raise Exception(f"File processing failed for Gemini. State: {uploaded_file.state.name}")
        
        prompt = """
        Analyze the provided video. Respond ONLY with a valid JSON object with three keys:
        1. "device_type": A short string identifying the device.
        2. "condition": A single word: "Mint", "Excellent", "Good", "Fair", or "Poor".
        3. "reason": A brief string explaining the condition.
        """
        model_name = "gemini-1.5-flash-latest" # Using a reliable public model
        config = types.GenerateContentConfig(temperature=0.2, response_mime_type="application/json")
        contents = [uploaded_file, prompt]
        
        response = client.models.generate_content(model=f"models/{model_name}", contents=contents, config=config)
        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")
        print("--- Gemini Analysis Complete ---")
        
    except Exception as e:
        print(f"ERROR during Gemini processing: {e}")
        device_type, condition, reason = f"Error in Gemini processing: {e}", "", ""
    finally:
        if uploaded_file:
            client.files.delete(name=uploaded_file.name)

    # Return all the results to the Gradio UI
    return device_type, condition, reason, overlay_images, attention_maps

# --- Gradio Interface ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("# 📱 Advanced Device Condition Analyzer")
    gr.Markdown("Upload a video to get a condition analysis from Gemini and attention maps from DINO.")
    
    video_input = gr.Video(label="Upload or Record Video", sources=["upload", "webcam"], format="mp4")
    submit_button = gr.Button("Run Full Analysis", variant="primary")
    
    gr.Markdown("## Gemini Condition Analysis")
    with gr.Row():
        device_type_output = gr.Textbox(label="Device Type")
        condition_output = gr.Textbox(label="Condition")
        reason_output = gr.Textbox(label="Reason / Details")
        
    gr.Markdown("## DINO: Overlayed Heatmaps on Representative Frames")
    # Use gr.Gallery to display multiple images
    overlay_gallery = gr.Gallery(label="Overlayed Heatmaps", elem_id="gallery")
    
    gr.Markdown("## DINO: Self-Attention Maps")
    attention_gallery = gr.Gallery(label="Self-Attention Maps", elem_id="gallery")

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

demo.launch(debug=True)