Create app.py

app.py

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+import gradio as gr
+import math
+import numpy as np
+from joblib import load
+
+# Load model once
+model = load('model2.joblib')
+
+# Prediction function
+def model2(data):
+    input_array = np.array([data]).reshape(1, -1)
+    prediction = model.predict(input_array)[0]
+    return prediction
+
+# Gradio interface function
+def process_input(num_str):
+    if len(num_str) != 6 or not num_str.isdigit():
+        return ["Error: Input must be a 6-digit number"]
+
+    windows = [int(num_str[i:i+2]) for i in range(len(num_str)-1)]  # [12, 23, 34, 45, 56]
+    divisor = 3 * math.pi
+    normalized = [x / divisor for x in windows]
+
+    # Get predictions
+    pred1 = model2(normalized[0])
+    pred2 = model2(normalized[1])
+    pred3 = model2(normalized[2])
+    pred4 = model2(normalized[3])
+    pred5 = model2(normalized[4])
+
+    # Errors
+    err1 = pred1 - windows[1]
+    err2 = pred2 - windows[2]
+    err3 = pred3 - windows[3]
+    err4 = pred4 - windows[4]
+    errors = [err1, err2, err3, err4]
+
+    combined_error = sum(errors)
+    avg_error = combined_error / len(errors)
+    distances = [abs(e - avg_error) for e in errors]
+    nearest_indices = sorted(range(len(distances)), key=lambda i: distances[i])[:2]
+    nearest_values = [errors[i] for i in nearest_indices]
+    all_three = nearest_values + [avg_error]
+    mean_val = np.mean(all_three)
+
+    # Adjust predictions
+    ads_list = [(pred4 + err if avg_error > 0 else pred4 - err) for err in errors]
+    ads_array = np.array(ads_list, dtype=np.float32)
+    ads2 = ads_array * (3 * math.pi)
+
+    # Extract digit before decimal
+    digit_before_decimal = [int(str(int(x))[-1]) for x in ads2]
+
+    return digit_before_decimal
+
+# Gradio UI
+iface = gr.Interface(
+    fn=process_input,
+    inputs=gr.Textbox(label="Enter a 6-digit number"),
+    outputs=gr.Textbox(label="Digit Before Decimal Array"),
+    title="ML Prediction Error Adjustment",
+    description="Input a 6-digit number. Returns processed digit array after model predictions and transformations."
+)
+
+iface.launch()