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import gradio as gr
import numpy as np
import cv2
import torch
import torchvision.transforms as transforms
from PIL import Image
import matplotlib.pyplot as plt
# Load Pretrained AI Model (You can replace this with your custom model)
model = torch.hub.load("pytorch/vision:v0.10.0", "resnet18", pretrained=True)
model.eval()
# Function to analyze deforestation using AI model
def analyze_deforestation(image):
image = Image.fromarray(image).convert("RGB")
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
])
img_tensor = transform(image).unsqueeze(0)
# AI Prediction
with torch.no_grad():
output = model(img_tensor)
# Simulated deforestation risk (random value for demo)
deforestation_risk = np.random.uniform(0.3, 0.9)
# Identify locations for seed-dropping (For simplicity, we use random dots)
h, w = image.size
num_seeds = int(deforestation_risk * 20) # More risk = More seeds
seed_locations = np.random.randint(0, min(h, w), (num_seeds, 2))
# Plot Results
plt.figure(figsize=(6, 6))
plt.imshow(image)
for loc in seed_locations:
plt.scatter(loc[0], loc[1], color="red", s=20, marker="o") # Mark seed locations
plt.title(f"Deforestation Risk: {deforestation_risk:.2f} | Seeds: {num_seeds}")
plt.axis("off")
plt.savefig("output.png")
return "output.png", f"Risk Level: {deforestation_risk:.2f} | Seeds Dropped: {num_seeds}"
# Create Gradio UI
interface = gr.Interface(
fn=analyze_deforestation,
inputs=gr.Image(type="numpy"),
outputs=["image", "text"],
title="🌱 Plant Me AI - Drone Seed Dispersal 🌍",
description="Upload a satellite image of a forest area. The AI will analyze deforestation risks and predict seed-dropping locations for drone-based planting."
)
# Launch the web app
interface.launch(debug=True)
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