Update app.py

app.py

@@ -0,0 +1,48 @@
+import gradio as gr 
+from tensorflow.keras.models import load_model 
+from tensorflow.keras.preprocessing.image import img_to_array 
+from PIL import Image 
+import numpy as np 
+import requests 
+ 
+# Load your trained .h5 model 
+model = load_model("model.h5") 
+ 
+# Hugging Face LLM (e.g., Mistral) 
+HF_TOKEN = "hf_your_token_here" 
+LLM_API = 
+"https://api-inference.huggingface.co/models/mistralai/Mistral-7B-In
+ struct-v0.1" 
+headers = {"Authorization": f"Bearer {HF_TOKEN}"} 
+ 
+# Prediction and explanation function 
+def classify_and_explain(image): 
+    image = image.resize((225, 225)) 
+    img_array = img_to_array(image) / 255.0 
+    img_array = np.expand_dims(img_array, axis=0) 
+ 
+    prediction = model.predict(img_array) 
+    predicted_class = int(np.argmax(prediction, axis=1)[0]) 
+    class_label = f"Class_{predicted_class}"  # Or use actual class 
+names if available 
+ 
+    # Get explanation from LLM 
+    prompt = f"Explain the plant disease {class_label} and how to 
+treat it." 
+    response = requests.post(LLM_API, headers=headers, 
+json={"inputs": prompt}) 
+    llm_text = response.json()[0]['generated_text'] if 
+isinstance(response.json(), list) else response.json() 
+ 
+    return class_label, llm_text 
+ 
+# Gradio UI 
+interface = gr.Interface( 
+    fn=classify_and_explain, 
+    inputs=gr.Image(type="pil"), 
+    outputs=["text", "text"], 
+    title="Plant Disease Predictor & Explainer", 
+description="Upload a leaf image to detect plant disease and get 
+treatment advice using LLM" 
+) 
+interface.launch()