Upload folder using huggingface_hub

README.md

@@ -1,12 +1,6 @@
 ---
-title: Tglang Programming Langugage Detection
-emoji: 📊
-colorFrom: gray
-colorTo: yellow
+title: Tglang_programming_langugage_detection
+app_file: app.py
 sdk: gradio
 sdk_version: 4.5.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,61 @@
+from pathlib import Path
+
+import gradio as gr
+import pyonmttok
+import torch.jit
+
+model = None
+tokenizer = None
+lang_enum = None
+
+TITLE = "Tglang: Programming Language Detection"
+DESCRIPTION = ("<h5 style=\"text-align:center\">"
+               "Enter a code snippet and the model will predict the programming language it is written in.\n\n"
+               "Alternatively, it's possible to select one example from the dropdown menu to see how the model works.<h5>")
+FOOTER = ("This is a solution for the "
+            "[Telegram ML competition 2023, Round 2](https://contest.com/docs/ML-Competition-2023-r2).\n\n"
+          "For more details, read [this article]() or check out [this repo]()")
+EXAMPLES = [
+    ["def foo():\n    print('Hello, world!')", "TGLANG_LANGUAGE_PYTHON"],
+    ["int main() {\n    printf(\"Hello, world!\");\n    return 0;\n}", "TGLANG_LANGUAGE_C"],
+    ["function foo() {\n    console.log('Hello, world!');\n}", "TGLANG_LANGUAGE_JAVASCRIPT"],
+    ["public class HelloWorld {\n    public static void main(String[] args) {\n        System.out.println(\"Hello, world!\");\n    }\n}", "TGLANG_LANGUAGE_JAVA"],
+    ["#include <iostream>\n\nint main() {\n    std::cout << \"Hello, world!\" << std::endl;\n}", "TGLANG_LANGUAGE_CPP"],
+    ["using System;\n\npublic class Program\n{\n    public static void Main()\n    {\n        Console.WriteLine(\"Hello, world!\");\n    }\n}", "TGLANG_LANGUAGE_CSHARP"],
+]
+
+
+def init_model():
+    global model, tokenizer, lang_enum
+    tokenizer = pyonmttok.Tokenizer("conservative")
+    model = torch.jit.load(Path(__file__).with_name("tglang.pt"))
+    lang_enum = Path(__file__).with_name("langs_enum_r2.txt").read_text().strip().split("\n")
+    lang_enum = [l.strip() for l in lang_enum if bool(l)]
+
+
+def predict(text):
+    global model, tokenizer, lang_enum
+    tokens = tokenizer(text)
+    lang_index, *_ = model([tokens])
+    return lang_enum[lang_index]
+
+
+def create_demo():
+    init_model()
+    demo = gr.Interface(fn=predict,
+                        inputs=gr.Textbox(label="Code snippet", placeholder="Enter code here..."),
+                        outputs=gr.Textbox(label="Model prediction"),
+                        title=TITLE,
+                        description=DESCRIPTION,
+                        examples=EXAMPLES,
+                    theme=gr.themes.Monochrome(),
+                        article=FOOTER,
+                        )
+    return demo
+
+
+demo = create_demo()
+
+
+if __name__ == "__main__":
+    demo.launch(show_api=False)

langs_enum_r2.txt

@@ -0,0 +1,29 @@
+  TGLANG_LANGUAGE_OTHER
+  TGLANG_LANGUAGE_C
+  TGLANG_LANGUAGE_CPLUSPLUS
+  TGLANG_LANGUAGE_CSHARP
+  TGLANG_LANGUAGE_CSS
+  TGLANG_LANGUAGE_DART
+  TGLANG_LANGUAGE_DOCKER
+  TGLANG_LANGUAGE_FUNC
+  TGLANG_LANGUAGE_GO
+  TGLANG_LANGUAGE_HTML
+  TGLANG_LANGUAGE_JAVA
+  TGLANG_LANGUAGE_JAVASCRIPT
+  TGLANG_LANGUAGE_JSON
+  TGLANG_LANGUAGE_KOTLIN
+  TGLANG_LANGUAGE_LUA
+  TGLANG_LANGUAGE_NGINX
+  TGLANG_LANGUAGE_OBJECTIVE_C
+  TGLANG_LANGUAGE_PHP
+  TGLANG_LANGUAGE_POWERSHELL
+  TGLANG_LANGUAGE_PYTHON
+  TGLANG_LANGUAGE_RUBY
+  TGLANG_LANGUAGE_RUST
+  TGLANG_LANGUAGE_SHELL
+  TGLANG_LANGUAGE_SOLIDITY
+  TGLANG_LANGUAGE_SQL
+  TGLANG_LANGUAGE_SWIFT
+  TGLANG_LANGUAGE_TL
+  TGLANG_LANGUAGE_TYPESCRIPT
+  TGLANG_LANGUAGE_XML

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+torch
+pyonmttok

tglang.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:60d7452b5fcf381deee08bf9dcf5387cf20236d809d0a044dbde827bf18ab07c
+size 630285