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import gradio as gr
from huggingface_hub import InferenceClient
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import torch
import requests
import io
import numpy as np
processor = AutoImageProcessor.from_pretrained(
"linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
)
model = AutoModelForImageClassification.from_pretrained(
"linkanjarad/mobilenet_v2_1.0_224-plant-disease-identification"
)
model.eval()
def predict_disease(img):
img = img.convert("RGB")
inputs = processor(images=img, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
pred_idx = logits.argmax(-1).item()
label = model.config.id2label[pred_idx]
confidence = torch.softmax(logits, dim=1)[0, pred_idx].item()
return f"Disease: {label}\nConfidence: {confidence:.2f}"
def respond(
message,
history: list[dict[str, str]],
system_message,
max_tokens,
temperature,
top_p,
hf_token: gr.OAuthToken,
):
client = InferenceClient(token=hf_token.token, model="openai/gpt-oss-20b")
messages = [{"role": "system", "content": system_message}]
messages.extend(history)
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
choices = message.choices
token = ""
if len(choices) and choices[0].delta.content:
token = choices[0].delta.content
response += token
yield response
chatbot = gr.ChatInterface(
respond,
type="messages",
additional_inputs=[
gr.Textbox(value="You are a friendly agricultural assistant.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
],
)
with gr.Blocks() as demo:
with gr.Sidebar():
gr.Markdown("# RootNet AI Dashboard")
gr.Markdown("Sign in with your Hugging Face account to use the Chatbot API.")
gr.LoginButton()
with gr.Tab("Plant Disease Detection"):
gr.Markdown("Upload a leaf image to predict disease:")
image_input = gr.Image(type="pil")
disease_output = gr.Textbox(label="Prediction")
image_input.change(predict_disease, inputs=image_input, outputs=disease_output)
with gr.Tab("Voice Assistant / Chatbot"):
chatbot.render()
if __name__ == "__main__":
demo.launch()
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