app.py

import os, json
from datetime import datetime

import numpy as np
from PIL import Image, ImageFilter, ImageDraw
import torch
from transformers import CLIPProcessor, CLIPModel

import gradio as gr

# ─── Config ─────────────────────────────
MODEL_NAME       = "openai/clip-vit-base-patch16"
HISTORY_FILE     = "waste_history.json"
FEEDBACK_FILE    = "user_feedback.json"
WASTE_CATEGORIES = ["General Waste", "Recyclable Waste"]

# ─── Load CLIP ────────────────────────────
processor = CLIPProcessor.from_pretrained(MODEL_NAME)  # tokenizer + vision featurizer :contentReference[oaicite:3]{index=3}
model     = CLIPModel.from_pretrained(MODEL_NAME)      # ViT-B/16 + text encoder :contentReference[oaicite:4]{index=4}

# ─── JSON Helpers ─────────────────────────
def load_json(path, default):
    try:
        with open(path, "r") as f: return json.load(f)
    except: return default

def save_json(path, data):
    with open(path, "w") as f: json.dump(data, f, indent=2)

# ─── Image Focus + Blur ───────────────────
def detect_and_focus(img_array):
    img = Image.fromarray(img_array)
    w, h = img.size
    bw, bh = int(w*0.6), int(h*0.6)
    left, top = (w-bw)//2, (h-bh)//2
    right, bottom = left + bw, top + bh
    boxed = img.copy()
    ImageDraw.Draw(boxed).rectangle((left, top, right, bottom), outline="green", width=3)
    blurred = img.filter(ImageFilter.GaussianBlur(radius=10))
    blurred.paste(img.crop((left, top, right, bottom)), (left, top))
    return np.array(blurred), np.array(boxed), (left, top, right, bottom)

# ─── Classification Logic ─────────────────
def classify_waste(image):
    if image is None:
        return "No image", [], None, "No data yet.", None
    focused, boxed, bbox = detect_and_focus(image)
    inputs = processor(text=WASTE_CATEGORIES,
                       images=Image.fromarray(focused),
                       return_tensors="pt", padding=True)  # :contentReference[oaicite:5]{index=5}
    with torch.no_grad():
        outputs = model(**inputs)
    probs = outputs.logits_per_image.softmax(dim=1).cpu().numpy()[0]  # :contentReference[oaicite:6]{index=6}
    idx      = int(probs.argmax())
    category = WASTE_CATEGORIES[idx]
    result   = f"**{category}** — {probs[idx]*100:.1f}%"
    bar_data = [{"category": c, "confidence": float(probs[i])}
                for i, c in enumerate(WASTE_CATEGORIES)]
    history  = load_json(HISTORY_FILE, {"classifications":[]})
    history["classifications"].append({
        "timestamp": datetime.now().isoformat(),
        "category": category,
        "confidence": float(probs[idx])
    })
    save_json(HISTORY_FILE, history)
    # Build summary
    counts = {c:0 for c in WASTE_CATEGORIES}
    for e in history["classifications"]:
        counts[e["category"]] += 1
    total = sum(counts.values())
    if total:
        summary = "### History\n" + "\n".join(
            f"- **{c}**: {cnt} ({cnt/total*100:.1f}%)" for c, cnt in counts.items()
        )
    else:
        summary = "No history yet."
    return result, bar_data, boxed, summary, bbox

# ─── Feedback Logic ───────────────────────
def handle_feedback(sketch, correct_cat, bbox):
    if sketch is None or correct_cat is None:
        return "Draw on the canvas and select a category."
    fb = load_json(FEEDBACK_FILE, {"feedback":[]})
    fb["feedback"].append({
        "timestamp": datetime.now().isoformat(),
        "correct_category": correct_cat,
        "bbox": bbox
    })
    save_json(FEEDBACK_FILE, fb)
    return f"Saved as **{correct_cat}**!"

# ─── Gradio Interface ─────────────────────
with gr.Blocks(css=".gradio-container{background:#F9FAFB}") as demo:
    gr.Markdown("# 🌱 EcoCan Waste Classifier")
    with gr.Tabs():
        with gr.Tab("Classify"):
            inp   = gr.Image(type="numpy", label="Upload Waste Image")
            btn   = gr.Button("🔍 Classify", variant="primary")
            out_t = gr.Textbox(label="Result")
            out_b = gr.BarPlot(x="category", y="confidence", y_lim=[0,1],
                               title="Confidence Scores")
            out_i = gr.Image(label="Detected Object", interactive=False)
            btn.click(classify_waste, inputs=inp,
                      outputs=[out_t, out_b, out_i, gr.State(), gr.State()])
        with gr.Tab("Feedback"):
            sketchpad = gr.Sketchpad(label="Circle Object")  # :contentReference[oaicite:7]{index=7}
            radio     = gr.Radio(WASTE_CATEGORIES, label="Correct Category")
            fb_btn    = gr.Button("Submit Feedback")
            fb_out    = gr.Textbox(label="Status")
            fb_btn.click(handle_feedback,
                         inputs=[sketchpad, radio, gr.State()],
                         outputs=fb_out)
        with gr.Tab("History"):
            gr.Markdown("No history yet.", elem_id="history-markdown")
    demo.launch()