Initial commit

.gradio/flagged/dataset1.csv

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+Paste Buggy Code,Suggested Fixed Code,timestamp
+"def greet(name)
+    print(""Hello"", name)
+","'/*
+ * Copyright (c) 2008-2021, Hazelcast, Inc. All Rights Reserved.
+ *
+ * Licensed under the Apache License, Version 2.0 (the ""License"");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an ""AS IS"" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package com.hazelcast.internal.serialization;
+
+import com.hazelcast.internal.serialization.impl.AbstractSerializationService;
+import com.hazelcast.internal.serialization.impl.DefaultSerializationService;
+import com.hazelcast.internal.serialization.impl.DefaultSerializationServiceBuilder;
+import com.hazelcast.internal.serialization.",2025-06-10 10:18:50.796833

.huggingface

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+# .huggingface/config.yaml
+title: Bug Fixer
+emoji: 🛠️
+colorFrom: purple
+colorTo: pink
+sdk: gradio
+sdk_version: 4.17.0
+app_file: app.py
+pinned: false

README.md

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+# Bug Fixer
+
+Bug Fixer is a Gradio-based web tool that uses the CodeT5+ model to automatically detect and fix bugs in Python code. Built using Hugging Face Transformers and deployable via Hugging Face Spaces.

app.py

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+import gradio as gr
+from bug_detector import fix_code
+
+def run_bugfixer(code):
+    return fix_code(code)
+
+gr.Interface(
+    fn=run_bugfixer,
+    inputs=gr.Textbox(label="Paste Buggy Code", lines=15),
+    outputs=gr.Textbox(label="Suggested Fixed Code"),
+    title="Code Fixer using CodeT5+"
+).launch()

bug_detector.py

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+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+
+# Load CodeT5+ model for code fixing
+model_ckpt = "Salesforce/codet5p-220m"
+tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_ckpt)
+
+def fix_code(code):
+    prompt = f"fix: {code}"
+    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True)
+    output = model.generate(inputs["input_ids"], max_length=256)
+    fixed_code = tokenizer.decode(output[0], skip_special_tokens=True)
+    return fixed_code.strip()

requirments.txt

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+transformers
+torch
+gradio
+flask