Update app.py

app.py

@@ -1,25 +1,43 @@
 import gradio as gr
 import joblib
-from huggingface_hub import hf_hub_download
 
-# Load model & vectorizer from your Hugging Face repo
-model_path = hf_hub_download("PopeJohn/agriculture-model", "agriculture_model.pkl")
-vectorizer_path = hf_hub_download("PopeJohn/agriculture-model", "vectorizer.pkl")
+# Load your trained artifacts
+model = joblib.load("model.pkl")
+vectorizer = joblib.load("vectorizer.pkl")
 
-model = joblib.load(model_path)
-vectorizer = joblib.load(vectorizer_path)
+labels = {0: "Negative", 1: "Positive"}  # adjust to your classes
 
-def classify_text(text):
-    X_new = vectorizer.transform([text])
-    label = model.predict(X_new)[0]
-    return label
+def classify(text):
+    probs = model.predict_proba(vectorizer.transform([text]))[0]
+    pred_idx = probs.argmax()
+    confidence = float(probs[pred_idx])
+    label = labels[pred_idx]
 
-iface = gr.Interface(
-    fn=classify_text,
-    inputs=gr.Textbox(lines=3, placeholder="Describe the crop situation..."),
-    outputs="label",
-    title="🌱 Agriculture Text Classifier",
-    description="Type in a description of a crop or farming situation and get a Positive/Negative classification."
-)
+    color = "#4CAF50" if pred_idx == 1 else "#F44336"  # green/red
+    styled_label = f"<span style='color:{color}; font-weight:bold'>{label}</span>"
 
-iface.launch()
+    return styled_label, f"{confidence:.2%}"
+
+with gr.Blocks() as demo:
+    gr.Markdown("## 🌱 Agricultural Text Classifier")
+    gr.Markdown("Enter a description and see the prediction with confidence.")
+
+    with gr.Row():
+        with gr.Column():
+            text_in = gr.Textbox(lines=3, placeholder="Type here...")
+            btn = gr.Button("Classify")
+            samples = gr.Row()
+            for sample in [
+                "Healthy maize after seasonal rains",
+                "Coffee plants showing signs of leaf rust",
+                "Pest infestation on cassava leaves"
+            ]:
+                gr.Button(sample).click(fn=lambda s=sample: s, outputs=text_in)
+
+        with gr.Column():
+            label_out = gr.HTML()
+            conf_out = gr.Textbox(label="Confidence", interactive=False)
+
+    btn.click(classify, inputs=text_in, outputs=[label_out, conf_out])
+
+demo.launch()