Upload 3 files

app.py

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+import streamlit as st
+import pandas as pd
+from autosklearn.regression import AutoSklearnRegressor
+import base64
+import json
+import pickle
+import uuid
+import re
+from io import BytesIO
+import numpy as np
+
+def to_excel(df:pd.DataFrame):
+    output = BytesIO()
+    writer = pd.ExcelWriter(output, engine='xlsxwriter')
+    df.to_excel(writer, index=False)
+    writer.save()
+    processed_data = output.getvalue()
+    return processed_data
+
+def download_button(object_to_download, download_filename, button_text, file_extension,pickle_it=False):
+    """
+    Generates a link to download the given object_to_download.
+
+    Params:
+    ------
+    object_to_download:  The object to be downloaded.
+    download_filename (str): filename and extension of file. e.g. mydata.csv,
+    some_txt_output.txt download_link_text (str): Text to display for download
+    link.
+    button_text (str): Text to display on download button (e.g. 'click here to download file')
+    pickle_it (bool): If True, pickle file.
+
+    Returns:
+    -------
+    (str): the anchor tag to download object_to_download
+
+    Examples:
+    --------
+    download_link(your_df, 'YOUR_DF.csv', 'Click to download data!')
+    download_link(your_str, 'YOUR_STRING.txt', 'Click to download text!')
+
+    """
+    if pickle_it:
+        try:
+            object_to_download = pickle.dumps(object_to_download)
+        except pickle.PicklingError as e:
+            st.write(e)
+            return None
+
+    else:
+        if isinstance(object_to_download, bytes):
+            pass
+
+        elif isinstance(object_to_download, pd.DataFrame):
+            if file_extension == ".csv":
+                object_to_download = object_to_download.to_csv(index=False)
+            else:
+                object_to_download = to_excel(object_to_download)
+        # Try JSON encode for everything else
+        else:
+            object_to_download = json.dumps(object_to_download)
+
+    try:
+        # some strings <-> bytes conversions necessary here
+        b64 = base64.b64encode(object_to_download.encode()).decode()
+
+    except AttributeError as e:
+        b64 = base64.b64encode(object_to_download).decode()
+
+    button_uuid = str(uuid.uuid4()).replace('-', '')
+    button_id = re.sub('\d+', '', button_uuid)
+
+    custom_css = f""" 
+        <style>
+            #{button_id} {{
+                display: inline-flex;
+                align-items: center;
+                justify-content: center;
+                background-color: rgb(255, 255, 255);
+                color: rgb(38, 39, 48);
+                padding: .5rem .75rem;
+                position: relative;
+                text-decoration: none;
+                border-radius: 4px;
+                border-width: 1px;
+                border-style: solid;
+                border-color: rgb(230, 234, 241);
+                border-image: initial;
+            }} 
+            #{button_id}:hover {{
+                border-color: rgb(246, 51, 102);
+                color: rgb(246, 51, 102);
+            }}
+            #{button_id}:active {{
+                box-shadow: none;
+                background-color: rgb(246, 51, 102);
+                color: white;
+                }}
+        </style> """
+        
+    dl_link = custom_css + f'<a download="{download_filename+file_extension}" id="{button_id}" href="data:file/txt;base64,{b64}">{button_text}</a><br></br>'
+    #dl_link = custom_css + f'<a download="{download_filename+file_extension}" id="{button_id}" data:application/octet-stream;base64,{b64}">{button_text}</a><br></br>'
+
+    return dl_link
+
+
+file_upload = st.file_uploader("Upload a csv file", type="csv")
+if file_upload is not None:
+    data = pd.read_csv(file_upload)
+    column = data["S11"].iloc[1:].values
+    with open("automl.pkl", "rb") as f:
+        model = pickle.load(f)
+    pred_clip = model.predict([column])
+    pred_clip = np.clip(pred_clip, [0.2,0.4,3.9,0.2,13.9,13.8,13.2],[1.01,1.21,4.71,0.8,14.701,14.201,14.001])
+    predictions = pd.DataFrame(pred_clip.tolist(), columns = ["w1","w2","w3","s1","l1","l2","l3"])
+    
+    is_download = st.checkbox("Download predictions", value=False)
+    if is_download:
+        href = download_button(predictions, "predictions", "Download", ".csv")
+        st.markdown(href, unsafe_allow_html=True)
+        
+
+
+        
+    

automl.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8804c4c6a5f5877ad53a98764007086727157d0bf7d1b85e284643b9ae9cd88e
+size 157995814

requirements.txt

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+streamlit
+auto-sklearn