Create app_not_clean.py

app_not_clean.py

@@ -0,0 +1,172 @@
+import os
+import csv
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import pandas as pd
+from datetime import datetime
+import utils
+from huggingface_hub import Repository
+import itertools
+
+# Unique phase elements
+
+# Load access tokens
+WRITE_TOKEN = os.environ.get("WRITE_PER") # write
+
+# Logs repo path
+dataset_url = "https://huggingface.co/datasets/sandl/upload_alloy_hardness"
+dataset_path = "logs_alloy_hardness.csv"
+
+scaling_factors = {'PROPERTY: Calculated Density (g/cm$^3$)': (2.7, 13.7),
+                   'PROPERTY: Calculated Young modulus (GPa)': (66, 336),
+                   'PROPERTY: HV': (94.7, 1183.0)}
+
+input_mapping = {'PROPERTY: BCC/FCC/other': {'BCC': 0, 'FCC': 1, 'OTHER': 2},# 'nan': 2},
+                 'PROPERTY: Processing method': {'ANNEAL': 0, 'CAST': 1, 'OTHER': 2, 'POWDER': 3, 'WROUGHT': 4},# 'nan': 2},
+                 'PROPERTY: Microstructure': {'B2': 0, 'B2+BCC': 1, 'B2+Sec.': 2, 'BCC': 3, 'BCC+B2': 4, 'BCC+B2+Laves': 5,
+                                              'BCC+B2+Sec.': 6, 'BCC+BCC': 7, 'BCC+BCC+HCP': 8, 'BCC+BCC+Laves(C15)': 9,
+                                              'BCC+FCC': 10, 'BCC+HCP': 11, 'BCC+Laves': 12, 'BCC+Laves(C14)': 13,
+                                              'BCC+Laves(C15)': 14, 'BCC+Laves+Sec.': 15, 'BCC+Sec.': 16, 'FCC': 17,
+                                              'FCC+B2': 18, 'FCC+B2+Sec.': 19, 'FCC+BCC': 20, 'FCC+BCC+B2': 21, 'FCC+BCC+B2+Sec.': 22,
+                                              'FCC+BCC+Sec.': 23, 'FCC+FCC': 24, 'FCC+HCP': 25, 'FCC+L12': 26, 'FCC+L12+Sec.': 27,
+                                              'FCC+Sec.': 28, 'OTHER': 29}, #'nan': 29},
+                 'PROPERTY: Single/Multiphase': {'M': 0, 'S': 1, 'OTHER': 3}} #'nan': 3, 
+
+unique_phase_elements = ['B2', 'BCC', 'FCC', 'HCP', 'L12', 'Laves', 'Laves(C14)', 'Laves(C15)', 'Sec.', 'OTHER']
+
+input_cols = {
+    "PROPERTY: Alloy formula": "(PROPERTY: Alloy formula) "
+                               "Enter alloy formula using proportions representation (i.e. Al0.25 Co1 Fe1 Ni1)",
+    "PROPERTY: Single/Multiphase": "(PROPERTY: Single/Multiphase) "
+                                   "Choose between Single (S), Multiphase (M) and other (OTHER)",
+    "PROPERTY: BCC/FCC/other": "(PROPERTY: BCC/FCC/other) "
+                               "Choose between BCC, FCC and other ",
+    "PROPERTY: Processing method": "(PROPERTY: Processing method) "
+                                   "Choose your processing method (ANNEAL, CAST, POWDER, WROUGHT or OTHER)",
+    "PROPERTY: Microstructure": "(PROPERTY: Microstructure) "
+                                "Choose the microstructure (SEC means the secondary/tertiary microstructure is not one of FCC, BCC, HCP, L12, B2, Laves, Laves (C14), Laves (C15))",
+}
+
+def process_microstructure(list_phases):
+    permutations = list(itertools.permutations(list_phases))
+    permutations_strings = [str('+'.join(list(e))) for e in permutations]
+    for e in permutations_strings:
+        if e in list(input_mapping['PROPERTY: Microstructure'].keys()):
+            return e
+    return 'OTHER'
+    
+def write_logs(message, message_type="Prediction"):
+    """
+    Write logs
+    """
+    with Repository(local_dir="data", clone_from=dataset_url, use_auth_token=WRITE_TOKEN).commit(commit_message="from private", blocking=False):
+        with open(dataset_path, "a") as csvfile:
+                writer = csv.DictWriter(csvfile, fieldnames=["name", "message", "time"])
+                writer.writerow(
+                    {"name": message_type, "message": message, "time": str(datetime.now())}
+                )
+    return 
+    
+def predict(x, request: gr.Request):
+    """
+    Predict the hardness using the ML model. Input data is a dataframe
+    """
+    loaded_model = tf.keras.models.load_model("hardness.h5")
+    x = x.replace("", 0)
+    x = np.asarray(x).astype("float32")
+    y = loaded_model.predict(x)[0][0]
+    minimum, maximum = scaling_factors['PROPERTY: HV']
+    print("Prediction is ", y)
+    if request is not None:   # Verify if request is not None (when building the app the first request is None)
+        message = f"{request.username}_{request.client.host}"
+        print("MESSAGE")
+        print(message)
+        res = write_logs(message)
+    interpret_fig = utils.interpret(x)
+    return round(y*(maximum-minimum)+minimum, 2), 12, interpret_fig
+
+
+def predict_from_tuple(in1, in2, in3, in4, in5, request: gr.Request):
+    """
+    Predict the hardness using the ML model. Input data is a tuple. Input order should be the same as the cols list
+    """
+    input_tuple = (in1, in2, in3, in4, in5)
+    formula = utils.normalize_and_alphabetize_formula(in1)
+    density = utils.calculate_density(formula)
+    young_modulus = utils.calculate_youngs_modulus(formula)
+    input_dict = {}
+
+    in2 = input_mapping['PROPERTY: Single/Multiphase'][str(in2)]
+    input_dict['PROPERTY: Single/Multiphase'] = [int(in2)]
+    
+    in3 = input_mapping['PROPERTY: BCC/FCC/other'][str(in3)]
+    input_dict['PROPERTY: BCC/FCC/other'] = [int(in3)]
+    
+    in4 = input_mapping['PROPERTY: Processing method'][str(in4)]
+    input_dict['PROPERTY: Processing method'] = [int(in4)]
+
+    in5 = process_microstructure(in5)
+    in5 = input_mapping['PROPERTY: Microstructure'][in5]
+    input_dict['PROPERTY: Microstructure'] = [int(in5)]
+    
+    density_scaling_factors = scaling_factors['PROPERTY: Calculated Density (g/cm$^3$)']
+    density = (density-density_scaling_factors[0])/(
+        density_scaling_factors[1]-density_scaling_factors[0])
+    input_dict['PROPERTY: Calculated Density (g/cm$^3$)'] = [float(density)]
+
+    
+    ym_scaling_factors = scaling_factors['PROPERTY: Calculated Young modulus (GPa)']
+    young_modulus = (young_modulus-ym_scaling_factors[0])/(
+        ym_scaling_factors[1]-ym_scaling_factors[0])
+    input_dict['PROPERTY: Calculated Young modulus (GPa)'] = [float(young_modulus)]
+
+    input_df = pd.DataFrame.from_dict(input_dict)
+    one_hot = utils.turn_into_one_hot(input_df, input_mapping)
+    print(one_hot.columns)
+    return predict(one_hot, request)
+
+
+input_formula = gr.Textbox(
+    lines=1, placeholder=input_cols["PROPERTY: Alloy formula"], label=input_cols["PROPERTY: Alloy formula"]
+)
+input_phase = gr.Dropdown(
+    choices=list(input_mapping["PROPERTY: Single/Multiphase"].keys()),
+    label=input_cols["PROPERTY: Single/Multiphase"],
+)
+input_bccfcc = gr.Dropdown(
+    choices=list(input_mapping["PROPERTY: BCC/FCC/other"].keys()),
+    label=input_cols["PROPERTY: BCC/FCC/other"],
+)
+input_processing = gr.Dropdown(
+    choices=list(input_mapping["PROPERTY: Processing method"].keys()),
+    label=input_cols["PROPERTY: Processing method"],
+)
+input_microstructure = gr.CheckboxGroup(
+    choices=unique_phase_elements, #list(input_mapping["PROPERTY: Microstructure"].keys()),
+    label=input_cols["PROPERTY: Microstructure"],
+)
+input_list = [input_formula, input_phase, input_bccfcc, input_processing, input_microstructure]
+
+
+examples_inputs = ['Al0.25 Co1 Fe1 Ni1', 'S', 'BCC', 'CAST', ['B2', 'BCC']]
+
+# Version where input is a DataFrame
+# demo = gr.Interface(fn=predict,
+#                     inputs=gr.DataFrame(headers=cols),
+#                     outputs=gr.Text(label="Hardness (in HV)"))
+
+
+demo = gr.Interface(
+    fn=predict_from_tuple,
+    inputs=input_list,
+    outputs=[gr.Text(label="Hardness (in HV)"), gr.Text(label="Uncertainty (%)"), gr.Plot(label="Interpretation")],
+    title="Predict your alloy's hardness",
+    description="This AI model provides the estimation of hardness based on the input alloy description",
+    examples=[examples_inputs],
+)
+
+
+
+if __name__ == "__main__":
+    demo.launch(show_error=True)