florascanner / app.py
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import gradio as gr
import requests
import base64
import json
API_KEY = "GCT8F6KhPYNUvBvh7X617OzRF9zXUNhwvWu4NyWxhklqU75S8d"
API_URL = "https://plant.id/api/v3/identification"
# Tracks the last prediction to detect repetition
last_prediction = {"name": None}
def identify_plant(image):
global last_prediction
try:
with open(image, "rb") as img_file:
img_data = base64.b64encode(img_file.read()).decode("utf-8")
payload = {
"images": [img_data],
"similar_images": True
}
headers = {
"Content-Type": "application/json",
"Api-Key": API_KEY
}
response = requests.post(API_URL, headers=headers, data=json.dumps(payload))
if response.status_code != 200:
return f"⚠️ Error {response.status_code}: {response.text}"
result = response.json()
suggestions = result.get("result", {}).get("classification", {}).get("suggestions", [])
if not suggestions:
return "❌ No plant match found."
top = suggestions[0]
name = top.get("name", "Unknown")
prob = top.get("probability", 0.0)
desc = top.get("details", {}).get("description", {}).get("value", "No description available.")
warning = ""
if last_prediction["name"] == name:
warning = "\n⚠️ Same result as last time. This may indicate repetition. Try a different photo or angle."
last_prediction["name"] = name
result = response.json()
suggestions = result.get("result", {}).get("classification", {}).get("suggestions", [])
if not suggestions:
return "❌ No plant match found."
top = suggestions[0]
name = top.get("name", "Unknown")
prob = top.get("probability", 0.0)
return f"""
🌿 **Plant:** {name}
📊 **Confidence:** {prob * 100:.2f}%
"""
iface = gr.Interface(
fn=identify_plant,
inputs=gr.Image(type="filepath", label="Upload UAE Plant Image"),
outputs="text",
title="🌿 UAE Plant Identifier",
description="Upload a photo of a UAE plant (leaf preferred). We'll identify it using the Plant.id API.",
)
if __name__ == "__main__":
iface.launch()