Update app.py

app.py

@@ -11,42 +11,34 @@ def predict(COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI):
     prediction = model.predict(features)[0][0]
     return round(float(prediction), 2)
 
-# Define input sliders
-slider_COUNT = gr.Slider(minimum=7, maximum=50, step=0.5, label="COUNT")
-slider_COUNT_CV = gr.Slider(minimum=0.48, maximum=1.87, step=0.1, label="COUNT_CV")
-slider_STRENGTH = gr.Slider(minimum=62.71, maximum=384.2, step=0.1, label="STRENGTH")
-
-slider_CSP = gr.Slider(minimum=1500, maximum=4755, step=1, label="CSP")
-slider_U_PERCENT = gr.Slider(minimum=6.38, maximum=12.12, step=0.1, label="U_PERCENT")
-slider_THIN = gr.Slider(minimum=0.0, maximum=19, step=0.1, label="THIN")
-
-slider_THICK = gr.Slider(minimum=2.0, maximum=150, step=1, label="THICK")
-slider_NEPS = gr.Slider(minimum=1, maximum=494, step=1, label="NEPS")
-slider_IPI = gr.Slider(minimum=6.0, maximum=646, step=1, label="IPI")
-
+# Gradio app with custom sliders
 with gr.Blocks() as demo:
     gr.Markdown("## 🧵 Textile Mixing Predictor (TensorFlow Model)")
     gr.Markdown("Adjust the sliders for each feature and get the predicted output.")
 
     with gr.Row():
         with gr.Column():
-            count = slider_COUNT
-            count_cv = slider_COUNT_CV
-            strength = slider_STRENGTH
+            COUNT = gr.Slider(minimum=7, maximum=50, step=0.5, label="COUNT")
+            COUNT_CV = gr.Slider(minimum=0.48, maximum=1.87, step=0.1, label="COUNT_CV")
+            STRENGTH = gr.Slider(minimum=62.71, maximum=384.2, step=0.1, label="STRENGTH")
         with gr.Column():
-            csp = slider_CSP
-            u_percent = slider_U_PERCENT
-            thin = slider_THIN
+            CSP = gr.Slider(minimum=1500, maximum=4755, step=1, label="CSP")
+            U_PERCENT = gr.Slider(minimum=6.38, maximum=12.12, step=0.1, label="U_PERCENT")
+            THIN = gr.Slider(minimum=0.0, maximum=19, step=0.1, label="THIN")
         with gr.Column():
-            thick = slider_THICK
-            neps = slider_NEPS
-            ipi = slider_IPI
+            THICK = gr.Slider(minimum=2.0, maximum=150, step=1, label="THICK")
+            NEPS = gr.Slider(minimum=1, maximum=494, step=1, label="NEPS")
+            IPI = gr.Slider(minimum=6.0, maximum=646, step=1, label="IPI")
 
     output = gr.Number(label="Predicted Output")
 
-    predict_btn = gr.Button("Predict")
+    predict_button = gr.Button("Predict")
 
-    inputs = [count, count_cv, strength, csp, u_percent, thin, thick, neps, ipi]
-    predict_btn.click(fn=predict, inputs=inputs, outputs=output)
+    # Bind inputs to function
+    predict_button.click(
+        fn=predict,
+        inputs=[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI],
+        outputs=output
+    )
 
 demo.launch()