Switch to akshayvkt/detect-ai-text to fix model_type error

Dockerfile

@@ -20,7 +20,7 @@ RUN pip install --no-cache-dir -r requirements.txt
 RUN pip install --no-cache-dir uvicorn
 
 # Clear cache and pre-download model
-RUN rm -rf /app/.cache/huggingface/* && python -c "from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline; tokenizer = AutoTokenizer.from_pretrained('SuperAnnotate/ai-detector'); model = AutoModelForSequenceClassification.from_pretrained('SuperAnnotate/ai-detector'); pipeline('text-classification', model=model, tokenizer=tokenizer)"
+RUN rm -rf /app/.cache/huggingface/* && python -c "from transformers import pipeline; pipeline('text-classification', model='akshayvkt/detect-ai-text')"
 
 # Copy the application code
 COPY --chown=myuser:myuser . .

README.md

@@ -6,4 +6,4 @@ colorTo: green
 sdk: docker
 app_port: 7860
 ---
-A FastAPI app using roberta-base-openai-detector to classify text as AI-generated or real.
+A FastAPI app using akshayvkt/detect-ai-text to classify text as AI-generated or human-written.

app.py

@@ -1,14 +1,11 @@
 from fastapi import FastAPI
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
+from transformers import pipeline
 from pydantic import BaseModel
 import torch
 
 app = FastAPI()
-model_name = "SuperAnnotate/ai-detector"
 torch.manual_seed(42)  # For reproducibility
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-detector = pipeline("text-classification", model=model, tokenizer=tokenizer)
+detector = pipeline("text-classification", model="akshayvkt/detect-ai-text")
 
 class TextInput(BaseModel):
     text: str

requirements.txt

@@ -2,4 +2,5 @@ transformers==4.44.2
 torch==2.4.1
 fastapi==0.115.2
 uvicorn==0.32.0
-pydantic==2.9.2
+pydantic==2.9.2
+numpy==2.0.2