Create app.py

app.py

@@ -0,0 +1,60 @@
+import streamlit as st
+import torch
+from transformers import AutoModelForCausalLM
+
+def get_model_structure(model_id):
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        torch_dtype=torch.bfloat16,
+        device_map="cpu",
+    )
+    structure = {k: v.shape for k, v in model.state_dict().items()}
+    return structure
+
+def compare_structures(struct1, struct2):
+    keys1 = set(struct1.keys())
+    keys2 = set(struct2.keys())
+    all_keys = keys1.union(keys2)
+    
+    diff = []
+    for key in all_keys:
+        shape1 = struct1.get(key)
+        shape2 = struct2.get(key)
+        if shape1 != shape2:
+            diff.append((key, shape1, shape2))
+    return diff
+
+def display_diff(diff):
+    left_lines = []
+    right_lines = []
+    
+    for key, shape1, shape2 in diff:
+        left_lines.append(f"{key}: {shape1}")
+        right_lines.append(f"{key}: {shape2}")
+    
+    left_html = "<br>".join(left_lines)
+    right_html = "<br>".join(right_lines)
+    
+    return left_html, right_html
+
+st.title("Model Structure Comparison Tool")
+model_id1 = st.text_input("Enter the first HuggingFace Model ID")
+model_id2 = st.text_input("Enter the second HuggingFace Model ID")
+
+if model_id1 and model_id2:
+    struct1 = get_model_structure(model_id1)
+    struct2 = get_model_structure(model_id2)
+    
+    diff = compare_structures(struct1, struct2)
+    left_html, right_html = display_diff(diff)
+    
+    st.write("### Comparison Result")
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.write("### Model 1")
+        st.markdown(left_html, unsafe_allow_html=True)
+    
+    with col2:
+        st.write("### Model 2")
+        st.markdown(right_html, unsafe_allow_html=True)