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main.py

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+from fastapi import FastAPI
+from typing import List
+import torch
+from PIL import Image
+import numpy as np
+import torch.nn as nn
+import matplotlib.pyplot as plt
+import matplotlib as mpl
+from fastapi.responses import FileResponse
+import shutil
+
+
+app = FastAPI()
+
+
+class CustomModel(nn.Module):
+    def __init__(self):
+        super(CustomModel, self).__init__()
+
+        self.fc_layers = nn.Sequential(
+            nn.Linear(3, 64), 
+            nn.ReLU(),
+            nn.Linear(64, 744 * 554)  
+        )
+
+    def forward(self, x_features):
+        x_features = self.fc_layers(x_features)
+        output = x_features.view(-1, 1, 744, 554)
+        return output
+
+
+def predict_image(parameters):
+
+    """Predicts an image based on parameters (feedrate, depth of cut, toolwear)"""
+    # Load the trained model
+    model = CustomModel() 
+    model.load_state_dict(torch.load('trained_model.pth'))
+    model.eval()  
+
+    with torch.no_grad():
+        input_features = torch.tensor(parameters, dtype=torch.float32)       
+        predicted_image = model(input_features.unsqueeze(0)) 
+        return predicted_image.numpy() 
+
+def image_to_ts(image):
+
+    """Transforms an image to a time series and returns the plot"""
+    
+    # Extract the pixel values from the image
+    z_values = image[0, :]
+    reversed_values = z_values
+    reversed_values = (reversed_values - 0.5) 
+    
+    x = np.arange(len(reversed_values)) / len(reversed_values) * 25 + 5
+
+    # Plot the time series
+    fig, ax = plt.subplots(figsize=(8, 5))
+    
+    ax.set_ylim(-0.2, 0.2)
+    ax.set_xlim(5, 30)
+    
+    mpl.rcParams['font.family'] = 'Arial'
+    mpl.rcParams['font.size'] = 30
+
+    ax.set_xlabel("Workpiece length", fontname="Arial", fontsize=16, labelpad=7)
+    ax.set_ylabel("Normalized surface height", fontname="Arial", fontsize=16, labelpad=7)
+    plt.yticks(fontname="Arial", fontsize=14, color="black")
+    plt.xticks(range(5, 31, 5), fontname="Arial", fontsize=14, color = "black")
+    #plt.title("Surface geometry",fontname="Arial", fontsize=18, color="black", weight="bold", pad=10)
+
+    xticks = ax.get_xticks()
+    xticklabels = [str(int(x)) if x != xticks[-2] else "mm" for x in xticks]
+    ax.set_xticklabels(xticklabels)
+
+
+    yticks = ax.get_yticks()
+    #yticklabels = [str(int(y)) if y != yticks[-2] else "" for y in yticks]
+    yticklabels = yticks = ["" for y in yticks]
+    ax.set_yticklabels(yticklabels)
+
+
+    gridwidth = 1.5
+    plt.grid(axis="y", linewidth=0.75, color="black")
+    plt.grid(axis="x", linewidth=0.75, color="black")
+
+    rand = ["top", "right", "bottom", "left"]
+    for i in rand:
+        plt.gca().spines[i].set_linewidth(gridwidth)
+        ax.spines[i].set_color('black')
+
+    plt.plot(x, reversed_values, color="#00509b", linewidth=1.5)
+
+    # Define the tolerance range
+    tolerance_lower = -0.085
+    tolerance_upper = 0.085
+
+    ax.fill_between(x, tolerance_lower, tolerance_upper, color='gray', alpha=0.2)
+
+    # Check if the plot is within tolerance
+    within_tolerance = all(tolerance_lower <= val <= tolerance_upper for val in reversed_values)
+
+    tolerance = None 
+    if within_tolerance:
+        tolerance = True
+    else:
+        tolerance = False
+
+    return fig, tolerance
+
+def save_figure(fig, filename):
+    """Save the figure as an image file."""
+    fig.savefig(filename)
+    plt.close(fig)
+
+
+
+@app.post("/predict")
+def prediction(feedrate: float, depth_of_cut: float, toolwear: float):
+    new_features = torch.tensor([[feedrate, depth_of_cut, toolwear]])
+    predicted_image = predict_image(new_features)
+    fig, tolerance = image_to_ts(predicted_image[0, 0])
+
+    figure_filename = "output_figure.png"
+    save_figure(fig, figure_filename)
+
+    figure_url = f"/get_figure/{figure_filename}"
+
+    #return {"figure": fig, "within_tolerance": tolerance}
+    return {"figure_url": figure_url, "within_tolerance": tolerance}
+
+@app.get("/get_figure/{filename}")
+def get_figure(filename: str):
+    return FileResponse(filename, media_type="image/png", filename=filename)

trained_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0e50218a972bb12b3b3dc1c1f17a231aad7e27d47f0c28d475a492cea9ab84a5
+size 107168579