Update app.py

app.py

@@ -1,7 +1,37 @@
 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
 
+# ------------------------
+# 1. Plant Disease Detection Model
+# ------------------------
+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}"
+
+# ------------------------
+# 2. Hugging Face Chatbot / Voice Assistant
+# ------------------------
 def respond(
     message,
     history: list[dict[str, str]],
@@ -11,19 +41,13 @@ def respond(
     top_p,
     hf_token: gr.OAuthToken,
 ):
-    """
-    For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-    """
     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,
@@ -35,36 +59,37 @@ def respond(
         token = ""
         if len(choices) and choices[0].delta.content:
             token = choices[0].delta.content
-
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
 chatbot = gr.ChatInterface(
     respond,
     type="messages",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        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)",
-        ),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
 )
 
+# ------------------------
+# 3. RootNet Gradio App Layout
+# ------------------------
 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()
-    chatbot.render()
+    
+    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()