Create app.py

app.py

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+import tensorflow as tf
+import numpy as np
+import gradio as gr
+
+# Load your trained TensorFlow model
+model = tf.keras.models.load_model("model_prediction_Yarn.h5")
+
+# Prediction function
+def predict(COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI):
+    features = np.array([[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")
+
+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
+        with gr.Column():
+            csp = slider_CSP
+            u_percent = slider_U_PERCENT
+            thin = slider_THIN
+        with gr.Column():
+            thick = slider_THICK
+            neps = slider_NEPS
+            ipi = slider_IPI
+
+    output = gr.Number(label="Predicted Output")
+
+    predict_btn = gr.Button("Predict")
+
+    inputs = [count, count_cv, strength, csp, u_percent, thin, thick, neps, ipi]
+    predict_btn.click(fn=predict, inputs=inputs, outputs=output)
+
+demo.launch()