Add model and app files

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import joblib
+import os
+
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained("microsoft/codebert-base")
+
+# Load label encoder
+le = joblib.load("label_encoder.pkl")
+
+# Load model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = AutoModelForSequenceClassification.from_pretrained(
+    "microsoft/codebert-base",
+    num_labels=7
+)
+model.load_state_dict(torch.load("best_model.pt", map_location=device))
+model.to(device)
+model.eval()
+
+# Complexity descriptions
+DESCRIPTIONS = {
+    "constant":  ("O(1)", "⚡ Constant Time", "Executes in the same time regardless of input size. Very fast!"),
+    "linear":    ("O(n)", "📈 Linear Time", "Execution time grows linearly with input size."),
+    "logn":      ("O(log n)", "🔍 Logarithmic Time", "Very efficient! Common in binary search algorithms."),
+    "nlogn":     ("O(n log n)", "⚙️ Linearithmic Time", "Common in efficient sorting algorithms like merge sort."),
+    "quadratic": ("O(n²)", "🐢 Quadratic Time", "Execution time grows quadratically. Common in nested loops."),
+    "cubic":     ("O(n³)", "🦕 Cubic Time", "Triple nested loops. Avoid for large inputs."),
+    "np":        ("O(2ⁿ)", "💀 Exponential Time", "NP-Hard complexity. Only feasible for very small inputs."),
+}
+
+def predict(code):
+    if not code.strip():
+        return "⚠️ Please paste some code first!", "", ""
+
+    inputs = tokenizer(
+        code,
+        truncation=True,
+        max_length=512,
+        padding='max_length',
+        return_tensors='pt'
+    )
+
+    input_ids = inputs['input_ids'].to(device)
+    attention_mask = inputs['attention_mask'].to(device)
+
+    with torch.no_grad():
+        outputs = model(input_ids=input_ids, attention_mask=attention_mask)
+        pred = torch.argmax(outputs.logits, dim=1).item()
+
+    label = le.inverse_transform([pred])[0]
+    notation, title, description = DESCRIPTIONS.get(label, (label, label, ""))
+
+    return notation, title, description
+
+
+# Custom CSS
+css = """
+@import url('https://fonts.googleapis.com/css2?family=JetBrains+Mono:wght@400;700&family=Syne:wght@400;700;800&display=swap');
+
+* { box-sizing: border-box; }
+
+body, .gradio-container {
+    background: #0a0a0f !important;
+    font-family: 'Syne', sans-serif !important;
+}
+
+.gradio-container {
+    max-width: 900px !important;
+    margin: 0 auto !important;
+}
+
+#header {
+    text-align: center;
+    padding: 40px 20px 20px;
+}
+
+#header h1 {
+    font-size: 2.8em;
+    font-weight: 800;
+    background: linear-gradient(135deg, #00ff88, #00cfff);
+    -webkit-background-clip: text;
+    -webkit-text-fill-color: transparent;
+    margin-bottom: 8px;
+    letter-spacing: -1px;
+}
+
+#header p {
+    color: #888;
+    font-size: 1em;
+    font-family: 'JetBrains Mono', monospace;
+}
+
+.gr-textbox textarea {
+    background: #111118 !important;
+    border: 1px solid #222 !important;
+    color: #e0e0e0 !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 0.85em !important;
+    border-radius: 12px !important;
+    padding: 16px !important;
+}
+
+.gr-button-primary {
+    background: linear-gradient(135deg, #00ff88, #00cfff) !important;
+    color: #000 !important;
+    font-weight: 700 !important;
+    font-family: 'Syne', sans-serif !important;
+    border: none !important;
+    border-radius: 10px !important;
+    font-size: 1em !important;
+    letter-spacing: 0.5px !important;
+}
+
+.gr-button-primary:hover {
+    opacity: 0.9 !important;
+    transform: translateY(-1px) !important;
+}
+
+.result-box {
+    background: #111118;
+    border: 1px solid #222;
+    border-radius: 12px;
+    padding: 20px;
+    color: #e0e0e0;
+}
+
+label {
+    color: #666 !important;
+    font-family: 'JetBrains Mono', monospace !important;
+    font-size: 0.75em !important;
+    letter-spacing: 1px !important;
+    text-transform: uppercase !important;
+}
+
+.gr-textbox {
+    border-radius: 12px !important;
+}
+"""
+
+# Examples
+examples = [
+    ["def get_first(arr):\n    return arr[0]"],
+    ["def linear_search(arr, target):\n    for i in range(len(arr)):\n        if arr[i] == target:\n            return i\n    return -1"],
+    ["def binary_search(arr, target):\n    left, right = 0, len(arr) - 1\n    while left <= right:\n        mid = (left + right) // 2\n        if arr[mid] == target:\n            return mid\n        elif arr[mid] < target:\n            left = mid + 1\n        else:\n            right = mid - 1\n    return -1"],
+    ["def bubble_sort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(0, n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]"],
+]
+
+with gr.Blocks(css=css, title="Code Complexity Predictor") as demo:
+    gr.HTML("""
+        <div id="header">
+            <h1>⚙️ Code Complexity Predictor</h1>
+            <p>// powered by CodeBERT — paste your code, get instant Big-O analysis</p>
+        </div>
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=3):
+            code_input = gr.Textbox(
+                label="YOUR CODE",
+                placeholder="# Paste your Python or Java code here...",
+                lines=14,
+                max_lines=20
+            )
+            predict_btn = gr.Button("⚡ Analyze Complexity", variant="primary")
+
+        with gr.Column(scale=2):
+            notation_out = gr.Textbox(label="BIG-O NOTATION", interactive=False)
+            title_out = gr.Textbox(label="COMPLEXITY CLASS", interactive=False)
+            desc_out = gr.Textbox(label="EXPLANATION", interactive=False, lines=3)
+
+    gr.Examples(
+        examples=examples,
+        inputs=code_input,
+        label="Try these examples"
+    )
+
+    predict_btn.click(
+        fn=predict,
+        inputs=code_input,
+        outputs=[notation_out, title_out, desc_out]
+    )
+
+demo.launch()

best_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:68768fd3e53895c8b107b2b012af92acae29bfd281bf88b1d2427572a02e7b59
+size 498687962

label_encoder.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7594bbda10b0e8aec7a8c30ad8eb5324954b2bbc2b1c60f982817806cef66cad
+size 533

requirements.txt

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+transformers==4.40.0
+torch==2.2.0
+gradio==4.44.0
+joblib==1.3.2
+scikit-learn==1.4.0