Update app.py

app.py

@@ -1,48 +1,114 @@
-# import tensorflow as tf
-
 import gradio as gr
 import numpy as np
+import pandas as pd
 import joblib
 
-# Load your trained model
+# Load model and preprocessing objects
 model = joblib.load("mixing_prediction_model.pkl")
+scaler = joblib.load("scaler.pkl")
+label_encoder = joblib.load("label_encoder.pkl")
 
-# 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]])
-    # pred = model.predict(features)[0]
-    # mixing_name = le.inverse_transform([pred])[0]
-    prediction = model.predict(features)[0][0]
-    return prediction
+# Define prediction function
+def predict_class(COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI):
+    # Input as a DataFrame with correct feature names
+    input_df = pd.DataFrame([{
+        "COUNT": COUNT,
+        "COUNT_CV": COUNT_CV,
+        "STRENGTH": STRENGTH,
+        "CSP": CSP,
+        "U_PERCENT": U_PERCENT,
+        "THIN": THIN,
+        "THICK": THICK,
+        "NEPS": NEPS,
+        "IPI": IPI
+    }])
+    
+    # Scale input
+    scaled_input = scaler.transform(input_df)
+    
+    # Predict
+    pred_index = model.predict(scaled_input)[0]
+    
+    # Decode class label
+    predicted_label = label_encoder.inverse_transform([pred_index])[0]
+    
+    return predicted_label
 
-# Gradio app with custom sliders
+# Build Gradio Interface
 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.")
+    gr.Markdown("## 🧵 Textile Mixing Classifier (Scikit-learn Model)")
+    gr.Markdown("Use the sliders to set values and click **Predict** to classify the textile mix.")
 
     with gr.Row():
         with gr.Column():
-            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 = 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")
+            COUNT = gr.Slider(7, 50, step=0.5, label="COUNT")
+            COUNT_CV = gr.Slider(0.48, 1.87, step=0.1, label="COUNT_CV")
+            STRENGTH = gr.Slider(62.71, 384.2, step=0.1, label="STRENGTH")
+            CSP = gr.Slider(1500, 4755, step=1, label="CSP")
         with gr.Column():
-            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")
+            U_PERCENT = gr.Slider(6.38, 12.12, step=0.1, label="U_PERCENT")
+            THIN = gr.Slider(0.0, 19, step=0.1, label="THIN")
+            THICK = gr.Slider(2.0, 150, step=1, label="THICK")
+            NEPS = gr.Slider(1, 494, step=1, label="NEPS")
+            IPI = gr.Slider(6.0, 646, step=1, label="IPI")
 
-    output = gr.Number(label="Predicted Output")
+    output = gr.Textbox(label="Predicted Class")
 
-    predict_button = gr.Button("Predict")
-
-    # Bind inputs to function
-    predict_button.click(
-        fn=predict,
-        inputs=[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI],
-        outputs=output
-    )
+    btn = gr.Button("Predict")
+    btn.click(fn=predict_class,
+              inputs=[COUNT, COUNT_CV, STRENGTH, CSP, U_PERCENT, THIN, THICK, NEPS, IPI],
+              outputs=output)
 
 demo.launch()
+
+
+
+
+# import tensorflow as tf
+
+# import gradio as gr
+# import numpy as np
+# import joblib
+
+# # Load your trained model
+# model = joblib.load("mixing_prediction_model.pkl")
+
+# # 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]])
+#     # pred = model.predict(features)[0]
+#     # mixing_name = le.inverse_transform([pred])[0]
+#     prediction = model.predict(features)[0][0]
+#     return prediction
+
+# # 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 = 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 = 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 = 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_button = gr.Button("Predict")
+
+#     # 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()