baseline

app.py

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+import gradio as gr
+import torch
+
+
+EXAMPLE_MD = """
+```python
+import torch 
+
+t1 = torch.arange({n1}).view({dim1})
+
+t2 = torch.arange({n2}).view({dim2})
+
+(t1 @ t2).shape = {out_shape} 
+
+```
+"""
+
+
+def generate_example(dim1: list, dim2: list):
+    n1 = 1
+    n2 = 1
+    for i in dim1:
+        n1 *= i
+    for i in dim2:
+        n2 *= i
+
+    t1 = torch.arange(n1).view(dim1)
+    t2 = torch.arange(n2).view(dim2)
+    try:
+        out_shape = list((t1 @ t2).shape)
+    except RuntimeError:
+        out_shape = "error"
+
+    return n1,dim1,n2,dim2,out_shape
+
+
+def sanitize_dimention(dim):
+    if dim is None:
+        gr.Error("one of the dimentions is empty, please fill it")
+    if "[" in dim:
+        dim = dim.replace("[", "")
+    if "]" in dim:
+        dim = dim.replace("]", "")
+    if "," in dim:
+        dim = dim.replace(",", " ").strip()
+        out = [int(i.strip()) for i in dim.split()]
+    else:
+        out = [int(dim.strip())]
+    if 0 in out:
+        gr.Error(
+            "Found the number 0 in one of the dimensions which is not allowed, consider using 1 instead"
+        )
+    return out
+
+
+def predict(dim1, dim2):
+    dim1 = sanitize_dimention(dim1)
+    dim2 = sanitize_dimention(dim2)
+    n1,dim1,n2,dim2,out_shape =  generate_example(dim1, dim2)
+
+    # TODO 
+    # add code exmplanation here
+    
+    return EXAMPLE_MD.format(
+        n1=str(n1), dim1=str(dim1), s2=str(n2), dim2=str(dim2), out_shape=str(out_shape)
+    )
+
+
+demo = gr.Interface(
+    predict,
+    inputs=["text", "text"],
+    outputs=["markdown"],
+    examples=[["1,2,3", "5,3,7"], ["1,2,3", "5,2,7"]],
+)
+
+demo.launch(debug=True)

requirements.txt

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+torch