app.py

import gradio as gr
from PIL import Image
import torch
import torchvision.models as models
import torchvision.transforms as transforms
import json

# ---------------------------
# Load model
# ---------------------------
def load_model(model_path="fine_tuned_resnet50.pth"):
    model = models.resnet50(pretrained=False)
    model.fc = torch.nn.Linear(in_features=2048, out_features=102)
    model.load_state_dict(torch.load(model_path, map_location=torch.device("cpu")))
    model.eval()
    return model

model = load_model("fine_tuned_resnet50.pth")

# ---------------------------
# Load flower info
# ---------------------------
with open("flower with discription.json", "r") as f:
    flower_info = {flower["id"]: flower for flower in json.load(f)}

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

# ---------------------------
# Inference function
# ---------------------------
def classify_image(image):
    image = image.convert("RGB")
    image_tensor = transform(image).unsqueeze(0)
    with torch.no_grad():
        output = model(image_tensor)
    predicted_class = torch.argmax(output, dim=1).item()
    info = flower_info.get(predicted_class, None)

    if info:
        return [
            info["name"].title(),
            info["scientific_name"],
            info["genus"],
            info["fun_fact"],
            info["where_found"],
            info.get("description", "No description available.")
        ]
    else:
        return ["Unknown"] * 6

# ---------------------------
# Gradio interface
# ---------------------------
iface = gr.Interface(
    fn=classify_image,
    inputs=gr.Image(type="pil"),
    outputs=[
        gr.Textbox(label="Flower Name"),
        gr.Textbox(label="Scientific Name"),
        gr.Textbox(label="Genus"),
        gr.Textbox(label="Fun Fact"),
        gr.Textbox(label="Where Found"),
        gr.Textbox(label="Description")
    ],
    title="Flower Classification",
    description="🌸 Upload a flower image to get its name, genus, scientific name, fun fact, and more.",
    allow_flagging="never"
)

if __name__ == "__main__":
    iface.launch()