Rename main.py to app.py

main.py → app.py

@@ -1,59 +1,55 @@
-from fastapi import FastAPI,Header,HTTPException,Depends,WebSocket,WebSocketDisconnect
-from fastapi.middleware.cors import CORSMiddleware
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Allow all origins
-    allow_methods=["GET", "POST"],  # Allow only GET and POST methods
-    allow_headers=["*"],  # Allow all headers
-)
-
-
-from fastapi import FastAPI, HTTPException
-from pydantic import BaseModel
-import torch
-from transformers import RobertaTokenizer, RobertaForSequenceClassification
-
-app = FastAPI()
-
-# Load the tokenizer
-tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
-
-# Load the model
-model_path="model_ai_detection"
-model = RobertaForSequenceClassification.from_pretrained(model_path)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model.to(device)
-model.eval()
-
-class TextData(BaseModel):
-    text: str
-
-@app.post("/predict")
-async def predict(data: TextData):
-    inputs = tokenizer(data.text, return_tensors="pt", padding=True, truncation=True)
-    inputs = {k: v.to(device) for k, v in inputs.items()}
-    
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        ai_prob = probs[0][1].item() * 100  # Probability of the text being AI-generated
-    
-    message = "The text is likely generated by AI." if ai_prob > 50 else "The text is likely generated by a human."
-    
-    return {
-        "score": ai_prob,
-        "message": message
-    }
-
-
-
-@app.get("/")
-async def read_root():
-    return {"message": "Ready to go"}
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
+from fastapi import FastAPI,Header,HTTPException,Depends,WebSocket,WebSocketDisconnect
+from fastapi.middleware.cors import CORSMiddleware
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Allow all origins
+    allow_methods=["GET", "POST"],  # Allow only GET and POST methods
+    allow_headers=["*"],  # Allow all headers
+)
+
+
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
+import torch
+from transformers import RobertaTokenizer, RobertaForSequenceClassification
+
+app = FastAPI()
+
+# Load the tokenizer
+tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
+
+# Load the model
+model_path="model_ai_detection"
+model = RobertaForSequenceClassification.from_pretrained(model_path)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+model.eval()
+
+class TextData(BaseModel):
+    text: str
+
+@app.post("/predict")
+async def predict(data: TextData):
+    inputs = tokenizer(data.text, return_tensors="pt", padding=True, truncation=True)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
+        ai_prob = probs[0][1].item() * 100  # Probability of the text being AI-generated
+    
+    message = "The text is likely generated by AI." if ai_prob > 50 else "The text is likely generated by a human."
+    
+    return {
+        "score": ai_prob,
+        "message": message
+    }
+
+
+
+@app.get("/")
+async def read_root():
+    return {"message": "Ready to go"}