Delete Testapp.txt

Testapp.txt

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-import gradio as gr
-import torch
-import clip
-import numpy as np
-import random
-import os
-from PIL import Image
-from ultralytics import YOLO # Still needed for person detection
-from gtts import gTTS
-import uuid
-import time
-import tempfile
-
-# --- Configuration ---
-DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
-YOLO_PERSON_MODEL_PATH = 'yolov8n.pt'  # Standard YOLOv8 for person detection
-# YOLO_FASHION_MODEL_PATH = 'best.pt' # REMOVED - Not using fashion model anymore
-CLIP_MODEL_NAME = "ViT-B/32"
-
-# --- Load Models ---
-print(f"Using device: {DEVICE}")
-try:
-    clip_model, clip_preprocess = clip.load(CLIP_MODEL_NAME, device=DEVICE)
-    print(f"CLIP model ({CLIP_MODEL_NAME}) loaded successfully.")
-except Exception as e:
-    print(f"Error loading CLIP model: {e}")
-    # Handle error
-try:
-    yolo_person_model = YOLO(YOLO_PERSON_MODEL_PATH).to(DEVICE)
-    print(f"YOLO person detection model ({YOLO_PERSON_MODEL_PATH}) loaded successfully.")
-except Exception as e:
-    print(f"Error loading YOLO person model: {e}")
-    # Handle error
-
-# REMOVED Fashion Model Loading
-
-# --- Prompts and Responses ---
-style_prompts = {
-    'drippy': [
-        "avant-garde streetwear", "high-fashion designer outfit", "trendsetting urban attire",
-        "luxury sneakers and chic accessories", "cutting-edge, bold style"
-    ],
-    'mid': [
-        "casual everyday outfit", "modern minimalistic attire", "comfortable yet stylish look",
-        "simple, relaxed streetwear", "balanced, practical fashion"
-    ],
-    'not_drippy': [
-        "disheveled outfit", "poorly coordinated fashion", "unfashionable, outdated attire",
-        "tacky, mismatched ensemble", "sloppy, uninspired look"
-    ]
-}
-
-# --- REINSTATED: Clothing prompts for CLIP ---
-clothing_prompts = [
-    "t-shirt", "dress shirt", "blouse", "hoodie", "jacket", "sweater", "coat",
-    "dress", "skirt", "pants", "jeans", "trousers", "shorts",
-    "sneakers", "boots", "heels", "sandals",
-    "cap", "hat", "scarf", "gloves", "bag", "accessory", "tank-top", "haircut"
-]
-
-# --- REINSTATED: Combine all prompts for CLIP ---
-all_prompts = []
-for cat_prompts in style_prompts.values():
-    all_prompts.extend(cat_prompts)
-
-# Record end of style prompts before adding clothing prompts
-style_prompts_end_index = len(all_prompts)
-all_prompts.extend(clothing_prompts)
-print(f"Total prompts for CLIP: {len(all_prompts)}")
-
-response_templates = {
-    'drippy': [
-        "You're Drippy, bruh – fire {item}!", "{item} goes crazy, on god!", "Certified drippy with that {item}."
-    ],
-    'mid': [
-        "Drop the {item} and you might get a text back.", "It's alright, but I'd upgrade the {item}.",
-        "Mid fit alert. That {item} is holding you back."
-    ],
-    'not_drippy': [
-        "Bro thought that {item} was tuff!", "Oh hell nah! Burn that {item}!",
-        "Crimes against fashion, especially that {item}! Also… maybe get a haircut.",
-        "Never walk out the house again with that {item}."
-    ]
-}
-CATEGORY_LABEL_MAP = { "drippy": "drippy", "mid": "mid", "not_drippy": "trash" }
-
-# --- REINSTATED: Function to get top clothing items based on CLIP probabilities ---
-def get_top_clothing(probs, n=3):
-    """Gets the top N clothing items based on CLIP probabilities."""
-    clothing_probs_start_index = style_prompts_end_index
-    clothing_probs = probs[clothing_probs_start_index:]
-    actual_n = min(n, len(clothing_prompts))
-    if actual_n <= 0:
-        return ["item"]
-    top_indices_in_slice = np.argsort(clothing_probs)[-actual_n:]
-    return [clothing_prompts[i] for i in reversed(top_indices_in_slice)]
-
-# --- Core Logic ---
-def analyze_outfit(input_img: Image.Image):
-    if input_img is None:
-        return ("<p style='color: #FF5555; text-align: center;'>Please upload an image.</p>",
-                None, "Error: No image provided.")
-
-    img = input_img.copy()
-    # 1) YOLO Person Detection
-    person_results = yolo_person_model(img, verbose=False)
-    boxes = person_results[0].boxes.xyxy.cpu().numpy()
-    classes = person_results[0].boxes.cls.cpu().numpy()
-    confidences = person_results[0].boxes.conf.cpu().numpy()
-    person_indices = np.where(classes == 0)[0]
-    cropped_img = img
-    if len(person_indices) > 0:
-        max_conf_person_idx = person_indices[np.argmax(confidences[person_indices])]
-        x1, y1, x2, y2 = map(int, boxes[max_conf_person_idx])
-        x1, y1 = max(0, x1), max(0, y1)
-        x2, y2 = min(img.width, x2), min(img.height, y2)
-        if x1 < x2 and y1 < y2:
-             cropped_img = img.crop((x1, y1, x2, y2))
-             print(f"Person detected and cropped: Box {x1, y1, x2, y2}")
-        else:
-            print("Warning: Invalid person bounding box after clipping. Using full image.")
-            cropped_img = img
-    else:
-        print("No person detected by yolo_person_model. Analyzing full image.")
-
-    # 2) CLIP Analysis
-    detected_clothing_item = "look"
-    try:
-        image_tensor = clip_preprocess(cropped_img).unsqueeze(0).to(DEVICE)
-        text_tokens = clip.tokenize(all_prompts).to(DEVICE)
-
-        with torch.no_grad():
-            logits, _ = clip_model(image_tensor, text_tokens)
-            all_probs = logits.softmax(dim=-1).cpu().numpy()[0]
-
-        drip_len = len(style_prompts['drippy'])
-        mid_len = len(style_prompts['mid'])
-        drip_score = np.mean(all_probs[0 : drip_len])
-        mid_score = np.mean(all_probs[drip_len : drip_len + mid_len])
-        not_score = np.mean(all_probs[drip_len + mid_len : style_prompts_end_index])
-
-        if drip_score > mid_score and drip_score > not_score:
-            category_key = 'drippy'
-            final_score = drip_score
-        elif mid_score > not_score:
-            category_key = 'mid'
-            final_score = mid_score
-        else:
-            category_key = 'not_drippy'
-            final_score = not_score
-
-        category_label = CATEGORY_LABEL_MAP[category_key]
-        final_score_str = f"{final_score:.2f}"
-        print(f"Style analysis: Category={category_label}, Score={final_score_str}")
-
-        clothing_items_detected_by_clip = get_top_clothing(all_probs, n=1)
-        if clothing_items_detected_by_clip:
-             detected_clothing_item = clothing_items_detected_by_clip[0]
-             print(f"Top clothing item identified by CLIP: {detected_clothing_item}")
-        else:
-             print("Warning: CLIP did not identify a top clothing item.")
-             detected_clothing_item = "fit"
-
-    except Exception as e:
-        print(f"Error during CLIP analysis or clothing selection: {e}")
-        return ("<p style='color: #FF5555;'>Error during analysis.</p>",
-                None, f"Analysis Error: {e}")
-
-    # 3) Generate Response and TTS
-    try:
-        response_text = random.choice(response_templates[category_key]).format(item=detected_clothing_item)
-        tts_path = os.path.join(tempfile.gettempdir(), f"drip_{uuid.uuid4().hex}.mp3")
-        tts = gTTS(text=response_text, lang='en', tld='com', slow=False)
-        tts.save(tts_path)
-        print(f"Generated TTS response: '{response_text}' saved to {tts_path}")
-
-        # --- Updated HTML Output ---
-        # Simpler structure, relies more on CSS for styling defined below
-        category_html = f"""
-            <div class='results-container'>
-                <h2 class='result-category'>RATING: {category_label.upper()}</h2>
-                <p class='result-score'>Style Score: {final_score_str}</p>
-            </div>
-        """
-        return category_html, tts_path, response_text
-
-    except Exception as e:
-        print(f"Error during response/TTS generation: {e}")
-        category_html = f"""
-            <div class='results-container'>
-                <h2 class='result-category'>Result: {category_label.upper()} (Score: {final_score_str})</h2>
-                <p class='result-score' style='color: #FFAAAA;'>Error generating audio/full response.</p>
-            </div>
-        """
-        return category_html, None, f"Analysis complete ({category_label}), but error generating audio/response."
-
-
-# --- Elite Fashion / Techno CSS ---
-custom_css = """
-:root {
-    --primary-bg-color: #000000;
-    --secondary-bg-color: #1A1A1A;
-    --text-color: #FFFFFF;
-    --accent-color: #1F04FF;
-    --border-color: #333333; /* Slightly lighter than secondary bg for subtle definition */
-    --input-bg-color: #1A1A1A;
-    --button-text-color: #FFFFFF;
-    --body-text-size: 16px; /* Base text size */
-}
-/* --- Global Styles --- */
-body, .gradio-container {
-    background-color: var(--primary-bg-color) !important;
-    color: var(--text-color) !important;
-    font-family: 'Inter', -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, Oxygen, Ubuntu, Cantarell, 'Open Sans', 'Helvetica Neue', sans-serif; /* Modern font stack */
-    font-size: var(--body-text-size);
-}
-/* Hide default Gradio footer */
-footer { display: none !important; }
-/* --- Component Styling --- */
-.gr-block { /* General block container */
-    background-color: var(--secondary-bg-color) !important;
-    border: 1px solid var(--border-color) !important;
-    border-radius: 8px !important; /* Slightly rounded corners */
-    padding: 15px !important;
-    box-shadow: none !important; /* Remove default shadows */
-}
-/* Input/Output Text Areas & General inputs */
-.gr-input, .gr-output, .gr-textbox textarea, .gr-dropdown select, .gr-checkboxgroup input {
-    background-color: var(--input-bg-color) !important;
-    color: var(--text-color) !important;
-    border: 1px solid var(--border-color) !important;
-    border-radius: 5px !important;
-}
-.gr-textbox textarea::placeholder { /* Style placeholder text if needed */
-    color: #888888 !important;
-}
-/* Component Labels */
-.gr-label span, .gr-label .label-text {
-    color: var(--text-color) !important;
-    font-weight: 500 !important; /* Slightly bolder labels */
-    font-size: 0.95em !important;
-    margin-bottom: 8px !important; /* Space below label */
-}
-/* Image Input/Output */
-.gr-image {
-   background-color: var(--primary-bg-color) !important; /* Match main background */
-   border: 1px dashed var(--border-color) !important; /* Dashed border for drop zone */
-   border-radius: 8px !important;
-   overflow: hidden; /* Ensure image stays within bounds */
-}
-.gr-image img {
-    border-radius: 6px !important; /* Slightly round image corners */
-    object-fit: contain; /* Ensure image fits well */
-}
-.gr-image .no-image, .gr-image .upload-button { /* Placeholder text/button inside image component */
-     color: #AAAAAA !important;
-}
-/* Audio Component */
-.gr-audio > div:first-of-type { /* Target the container around the audio player */
-    border: 1px solid var(--border-color) !important;
-    background-color: var(--secondary-bg-color) !important;
-    border-radius: 5px !important;
-    padding: 10px !important;
-}
-.gr-audio audio { /* Style the audio player itself */
-    width: 100%; /* Make player responsive */
-    filter: invert(1) hue-rotate(180deg); /* Basic dark theme for player controls */
-}
-/* --- Button Styling --- */
-.gr-button { /* General button style reset */
-    border: none !important;
-    border-radius: 5px !important;
-    transition: background-color 0.2s ease, transform 0.1s ease;
-    font-weight: 600 !important;
-}
-.gr-button-primary { /* Specific styling for the primary Analyze button */
-    background-color: var(--accent-color) !important;
-    color: var(--button-text-color) !important;
-    font-size: 1.1em !important; /* Make primary button slightly larger */
-    padding: 12px 20px !important; /* Adjust padding */
-}
-.gr-button-primary:hover {
-    background-color: #482FFF !important; /* Slightly lighter blue on hover */
-    transform: scale(1.02); /* Subtle scale effect */
-    box-shadow: 0 0 10px var(--accent-color); /* Add a glow effect */
-}
-.gr-button-primary:active {
-    transform: scale(0.98); /* Press down effect */
-}
-/* --- Typography & Content --- */
-h1, h2, h3 {
-    color: var(--text-color) !important;
-    font-weight: 600; /* Bold headings */
-    letter-spacing: 0.5px; /* Add slight letter spacing */
-}
-.prose h1 { /* Target Markdown H1 specifically if needed */
-    text-align: center;
-    margin-bottom: 25px !important;
-    font-size: 2em !important; /* Larger title */
-    text-transform: uppercase; /* Uppercase for impact */
-    letter-spacing: 1.5px;
-}
-.prose p { /* Target Markdown Paragraph */
-     color: #CCCCCC !important; /* Slightly dimmer text for descriptions */
-     font-size: 0.95em;
-     text-align: center;
-}
-/* Custom styling for the results HTML block */
-.results-container {
-    text-align: center;
-    padding: 20px;
-    border: 1px solid var(--accent-color); /* Use accent color for border */
-    border-radius: 8px;
-    background: linear-gradient(145deg, var(--secondary-bg-color), #2a2a2a); /* Subtle gradient */
-}
-.result-category {
-    color: var(--accent-color) !important; /* Use accent color for category */
-    font-size: 1.5em;
-    margin-bottom: 5px;
-    font-weight: 700;
-    text-transform: uppercase;
-}
-.result-score {
-    color: var(--text-color) !important;
-    font-size: 1.1em;
-    margin-top: 0;
-}
-/* --- Layout Adjustments --- */
-.gradio-container {
-    max-width: 850px !important; /* Slightly wider max-width */
-    margin: auto !important;
-    padding-top: 30px; /* Add some space at the top */
-}
-.gr-row {
-    gap: 25px !important; /* Increase gap between columns */
-}
-"""
-
-
-# --- Gradio Interface (Now using the custom CSS) ---
-with gr.Blocks(css=custom_css, theme=gr.themes.Base(primary_hue="neutral", secondary_hue="neutral", text_size=gr.themes.sizes.text_lg)) as demo: # Use Base theme to minimize default styles
-    # Title using Markdown (styled by CSS)
-    gr.Markdown("<h1>💧 DripAI: Rate Your Fit 💧</h1>")
-
-    with gr.Row():
-        with gr.Column(scale=1, min_width=350): # Assign min width for better responsiveness
-            input_image = gr.Image(
-                type='pil',
-                label="Upload Your Outfit", # Simpler label
-                sources=['upload', 'webcam', 'clipboard'],
-                height=450 # Slightly taller image area
-            )
-            analyze_button = gr.Button(
-                "Analyze Outfit",
-                variant="primary",
-                # size="lg" removed, controlled by CSS
-            )
-
-        with gr.Column(scale=1, min_width=350): # Assign min width
-            gr.Markdown("### ANALYSIS RESULTS") # Simple heading
-            category_html = gr.HTML(label="Rating & Score") # Label for screen readers/context
-            response_box = gr.Textbox(
-                lines=3,
-                label="Verbal Feedback", # Updated label
-                interactive=False
-            )
-            audio_output = gr.Audio(
-                autoplay=True, # Changed default to false, user can click play
-                label="Audio Feedback",
-                streaming=False
-            )
-
-    # Bind the analysis function to the button click
-    analyze_button.click(
-        fn=analyze_outfit,
-        inputs=[input_image],
-        outputs=[category_html, audio_output, response_box]
-    )
-
-    # Footer description text
-    gr.Markdown("<p>Upload, paste, or use your webcam to capture your outfit. DripAI evaluates your style.</p>")
-
-# --- Launch App ---
-if __name__ == "__main__":
-    demo.launch(debug=True)