Upload demo.py

demo.py

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+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import gradio as gr
+
+model = nn.Sequential(
+            nn.Linear(11, 20),
+            nn.ReLU(),
+            nn.Linear(20, 5, bias=True))
+
+PATH = "wine_model.pth"
+
+model.load_state_dict(torch.load(PATH, weights_only=False))
+
+def forward(model, input):
+  preds = model(input)
+  predicted_class = torch.argmax(preds, dim=-1) + 4
+  return predicted_class
+
+with gr.Blocks() as demo:
+    gr.Markdown("Enter your wine data below:")
+    input_df = gr.Dataframe(
+        row_count=(2, "dynamic"),  # Allows adding/removing rows
+        col_count=(11, "dynamic"),  # Allows adding/removing columns
+        headers=list(df.columns)[:-1],
+        label="Input Data",
+        interactive=True,
+        type="pandas" # Specify the desired input type for your function
+    )
+    output_text = gr.Textbox(label="Processed Output")
+
+def process_data(input_dataframe):
+    # Perform operations on the input_dataframe
+    if isinstance(input_dataframe, pd.DataFrame):
+        return forward(model, input_dataframe)
+    return "Invalid input type"
+
+input_df.change(fn=process_data, inputs=input_df, outputs=output_text)
+
+demo.launch()