app.py

# ✅ Import required libraries
from PIL import Image
import torch
import torchvision.transforms as transforms
from torchvision import models
import gradio as gr
from rembg import remove  # Background removal
from transformers import pipeline  # For non-mushroom detection

# 🔧 Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# 📦 Load your fine-tuned model
model = models.resnet50(pretrained=False)
model.fc = torch.nn.Linear(model.fc.in_features, 2)  # 2 classes: Edible, Poisonous
model.load_state_dict(torch.load("resnet_mushroom_classifier.pth", map_location=device))
model = model.to(device)
model.eval()

# 🏷️ Class names and species mapping
class_names = ['Edible', 'Poisonous']
mushroom_species = {
    "Edible": "Possible species:\n• Amanita citrina\n• Russula delica\n• Phaeogyroporus portentosus",
    "Poisonous": "Possible species:\n• Amanita phalloides\n• Inocybe rimosa"
}

# 🎨 Image preprocessing
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406],
                         [0.229, 0.224, 0.225])
])

# 🔍 Pretrained image classifier for screening non-mushroom
label_detector = pipeline("image-classification", model="microsoft/resnet-50")
mushroom_keywords = ["mushroom", "agaric", "amanita", "fungus", "earthstar", "toadstool"]

def is_mushroom_image(image):
    try:
        result = label_detector(image)
        top_label = result[0]["label"].lower()
        return any(keyword in top_label for keyword in mushroom_keywords)
    except:
        return False

# 🧠 Classification function with validation
CONFIDENCE_THRESHOLD = 85.0  # Minimum confidence considered safe enough to show suggestion

def classify_mushroom(image: Image.Image):
    try:
        if not is_mushroom_image(image):
            return "Not a mushroom", "ไม่ใช่เห็ด", "0.00%", "Please upload a mushroom photo."

        image = image.convert("RGBA")
        image_no_bg = remove(image).convert("RGB")
        tensor = transform(image_no_bg).unsqueeze(0).to(device)

        with torch.no_grad():
            outputs = model(tensor)
            _, predicted = torch.max(outputs, 1)
            label = class_names[predicted.item()]
            score = torch.softmax(outputs, dim=1)[0][predicted.item()].item() * 100

        suggestion = mushroom_species[label] if score >= CONFIDENCE_THRESHOLD else "Confidence too low to suggest species."

        if score < CONFIDENCE_THRESHOLD:
            label = "Uncertain"

        return label, ("กินได้" if label == "Edible" else ("พิษ" if label == "Poisonous" else "ไม่มั่นใจ")), f"{score:.2f}%", suggestion

    except Exception as e:
        print(f"❌ Error: {e}")
        return "Error", "ผิดพลาด", "N/A", "Invalid image. Please upload a valid mushroom photo."

# 🔗 Open user manual (link version) 🆕
MANUAL_URL = "https://drive.google.com/drive/folders/19lUCEaLstrRjCzqpDlWErhRd1EXWUGbf?usp=sharing"
manual_link = f"<a href=\"{MANUAL_URL}\" target=\"_blank\">📄 Open User Manual</a>"
FEEDBACK_URL = "https://forms.gle/k5zE2xoUudzjqqS29"
feedback_link = f"<a href=\"{FEEDBACK_URL}\" target=\"_blank\">📄 If you need feedback</a>"

# 🎛️ Gradio UI
if __name__ == "__main__":
    with gr.Blocks() as demo:
        gr.Markdown("## 🍄 Mushroom Safety Classifier")
        gr.Markdown("Upload a mushroom photo or use your camera to check if it’s edible or poisonous.\nอัปโหลดรูปเห็ดหรือใช้กล้องเพื่อตรวจสอบว่าเห็ดกินได้หรือมีพิษ")

        with gr.Row():
            image_input = gr.Image(type="pil", label="📷 Upload or Capture Mushroom Image")
            with gr.Column():
                label_en = gr.Textbox(label="🧬 Prediction (English)")
                label_th = gr.Textbox(label="🔁 คำทำนาย (ภาษาไทย)")
                confidence = gr.Textbox(label="📶 Confidence Score")
                label_hint = gr.Textbox(label="🏷️ Likely Species (Based on Training Data)")

        classify_btn = gr.Button("🔍 Classify")

        classify_btn.click(
            fn=classify_mushroom,
            inputs=image_input,
            outputs=[label_en, label_th, confidence, label_hint]
        )

        gr.Markdown("---")
        gr.Markdown(manual_link)  # 🆕 Display clickable user manual
        gr.Markdown(feedback_link)  # 🆕 Display clickable user manual
        gr.Markdown("App version: 1.1.0 | Updated: August 2025")

    demo.launch()