deployment

.gitattributes

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+*.safetensors filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use an official Python runtime as a parent image
+FROM python:3.9
+
+# Set the working directory in the container
+WORKDIR /app
+
+# Copy the requirements file into the container
+COPY requirements.txt .
+
+# Install dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the entire project into the container
+COPY . .
+
+# Expose the FastAPI port (7860 for Hugging Face Spaces)
+EXPOSE 7860
+
+# Command to run the application
+CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+title: AI Code Detector
+emoji: 🧠
+colorFrom: blue
+colorTo: purple
+sdk: docker
+sdk_version: 3.50.2
+app_file: app.py
+pinned: false

app.py

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+import os
+import uvicorn
+from fastapi import FastAPI
+from pydantic import BaseModel
+import torch
+from safetensors.torch import load_file
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+# Ensure CPU is always used
+device = torch.device('cpu')
+
+os.environ["HF_HOME"] = "/tmp/huggingface_cache"
+os.environ["TRANSFORMERS_CACHE"] = os.environ["HF_HOME"]
+os.makedirs(os.environ["HF_HOME"], exist_ok=True)
+
+app = FastAPI()
+
+class CodeBERTClassifier(torch.nn.Module):
+    def __init__(self):
+        super(CodeBERTClassifier, self).__init__()
+        self.model = RobertaForSequenceClassification.from_pretrained(
+            "microsoft/codebert-base",
+            num_labels=2,
+            cache_dir=os.environ["HF_HOME"]
+        ).to(device)  # Ensure model is on CPU
+
+    def forward(self, input_ids, attention_mask=None):
+        outputs = self.model(input_ids, attention_mask=attention_mask)
+        return outputs.logits
+
+
+def load_model():
+    model = CodeBERTClassifier()
+    model.load_state_dict(load_file('model.safetensors'), strict=False)
+    model.eval()
+    tokenizer = RobertaTokenizer.from_pretrained(
+        "microsoft/codebert-base",
+        cache_dir=os.environ["HF_HOME"]
+    )
+    return model, tokenizer
+
+model, tokenizer = load_model()
+
+
+class CodeRequest(BaseModel):
+    code_samples: list[str]
+
+
+def preprocess_input_code(code_samples):
+    inputs = tokenizer(code_samples, padding="max_length", truncation=True, max_length=512, return_tensors="pt")
+    return inputs["input_ids"].to(device), inputs["attention_mask"].to(device)  # Move tensors to CPU
+
+
+def predict(code_samples):
+    tokens, masks = preprocess_input_code(code_samples)
+    with torch.no_grad():
+        logits = model(tokens, attention_mask=masks)
+        probabilities = torch.nn.functional.softmax(logits, dim=1).numpy()  # Keep on CPU for processing
+    return probabilities
+
+
+@app.get("/")
+def home():
+    return {"message": "API is running!"}
+
+
+@app.post("/predict/")
+async def predict_code(request: CodeRequest):
+    probabilities = predict(request.code_samples)
+    prediction_labels = []
+    for prob in probabilities:
+        ai_generated_prob = prob[1] * 100
+        human_generated_prob = prob[0] * 100
+        if ai_generated_prob > human_generated_prob:
+            prediction_labels.append(f"{ai_generated_prob:.2f}% Of code similar to AI-generated code.")
+        else:
+            prediction_labels.append(f"{human_generated_prob:.2f}% Of code similar to Human-generated code.")
+    return {"predictions": prediction_labels}
+
+
+@app.post("/detect/")
+async def detect_code(request: CodeRequest):
+    probabilities = predict(request.code_samples)
+    results = [{"AI": f"{prob[1]*100:.2f}%", "Human": f"{prob[0]*100:.2f}%"} for prob in probabilities]
+    return {"predictions": results}
+
+
+if __name__ == "__main__":
+    port = int(os.environ.get("PORT", 7860))
+    uvicorn.run(app, host="0.0.0.0", port=port)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:77abc624b3b7f04a0ef3484d3ae5372178b1b669b338fc497da11975a4e3a4c0
+size 498614000

requirements.txt

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+fastapi
+pydantic
+torch
+transformers
+gdown
+uvicorn
+huggingface-hub