Update app.py

app.py

@@ -1,98 +1,94 @@
-from fastapi import FastAPI, File, UploadFile, Body
-from fastapi.responses import RedirectResponse
-from fastapi.middleware.cors import CORSMiddleware
-from PIL import Image
-import io
-import numpy as np
-from Apps.structure import ImagePredictionResponse, TextPredictionRequest, TextPredictionResponse, PredictionEntry
-from Apps.imagePreprocess import profile_image_for_cnn_predict, CNNPredict, ResnetPredict, clip_predict
-from Apps.textPreprocess import predict_text
-import tensorflow as tf
-
-origins=[
-    "http://localhost:5173",
-    "http://localhost",
-    "https://authentica-ai.vercel.app",
-]
-
-app = FastAPI(
-    title="Authentica API",
-    description=(
-        "Simple demo API for image and text prediction. "
-        "Upload an image to `/predict/image` or POST text to `/predict/text`."
-    ),
-    version="0.1.0",
-)
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-@app.get("/", include_in_schema=False)
-async def root():
-    # Redirect to the automatic Swagger UI provided by FastAPI
-    return RedirectResponse(url="/docs")
-
-@app.post(
-    "/predict/image",
-    response_model=ImagePredictionResponse,
-    summary="Predict image using all available models",
-    description="Upload an image file (jpg/png). It is evaluated on all 3 models and class index/confidence is returned.",
-)
-async def predict(image: UploadFile = File(...)):
-    """Accept an image upload and return a prediction using loaded model."""
-    
-    image_data = await image.read()
-    pil_img = Image.open(io.BytesIO(image_data)).convert("RGB")
-    profile_img = profile_image_for_cnn_predict(pil_img, crop_size=512)
-    if isinstance(profile_img, str):
-            return (f"Error processing image: {profile_img}")
-    else:
-        print(f"Profile image shape: {profile_img.shape}")
-        cnnPred = CNNPredict(profile_img)
-        resnetPred = ResnetPredict(profile_img)
-        clipPred = clip_predict(pil_img, crop_size=512)
-        #print(f"CNN Prediction (Real prob): {cnnPred:.4f}")
-        #print(f"ResNet Prediction (Real prob): {resnetPred:.4f}")
-        #print(f"CLIP Prediction (AI prob): {clipPred:.4f}")
-        resnet_class = 1 if resnetPred >= 0.5 else 0
-        cnn_class = 1 if cnnPred >= 0.5 else 0
-        clip_class = 0 if clipPred > 0.5 else 1
-        resnet_conf = resnetPred if resnetPred >= 0.5 else 1 - resnetPred
-        cnn_conf = cnnPred if cnnPred >= 0.5 else 1 - cnnPred
-        clip_conf = clipPred if clipPred > 0.5 else 1 - clipPred
-        #Predicted classes 1 is Real, 0 is AI
-        predictions = [
-            PredictionEntry(model="CNN", predicted_class=cnn_class, confidence=round(float(cnn_conf), 4)),
-            PredictionEntry(model="ResNet", predicted_class=resnet_class, confidence=round(float(resnet_conf), 4)),
-            PredictionEntry(model="CLIP", predicted_class=clip_class, confidence=round(float(clip_conf), 4)),
-        ]
-    return ImagePredictionResponse(predictions=predictions)
-
-@app.post(
-    "/predict/text",
-    response_model=TextPredictionResponse,
-    summary="Predict text",
-    description="POST a JSON body with `text` to get a predicted label and confidence.",
-)
-async def predict_text_endpoint(payload: TextPredictionRequest = Body(...)):
-    """Accept a text string and return a prediction of whether it's human or AI-generated."""
-    try:
-        # Use the text prediction function from textPreprocess.py
-        result = predict_text(payload.text)
-        
-        return TextPredictionResponse(
-            predicted_class=result["predicted_class"], 
-            confidence=result["confidence"]
-        )
-    except Exception as e:
-        # Return a fallback response in case of error
-        print(f"Error in text prediction: {e}")
-        return TextPredictionResponse(predicted_class="Human", confidence=0.5)
-
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="127.0.0.1", port=8000)
+from fastapi import FastAPI, File, UploadFile, Body
+from fastapi.responses import RedirectResponse
+from fastapi.middleware.cors import CORSMiddleware
+from PIL import Image
+import io
+import numpy as np
+from Apps.structure import ImagePredictionResponse, TextPredictionRequest, TextPredictionResponse, PredictionEntry
+from Apps.imagePreprocess import profile_image_for_cnn_predict, CNNPredict, ResnetPredict, clip_predict
+from Apps.textPreprocess import predict_text
+import tensorflow as tf
+
+origins=[
+    "http://localhost:5173",
+    "http://localhost",
+    "https://authentica-ai.vercel.app",
+]
+
+app = FastAPI(
+    title="Authentica API",
+    description=(
+        "Simple demo API for image and text prediction. "
+        "Upload an image to `/predict/image` or POST text to `/predict/text`."
+    ),
+    version="0.1.0",
+)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.get("/", include_in_schema=False)
+async def root():
+    # Redirect to the automatic Swagger UI provided by FastAPI
+    return RedirectResponse(url="/docs")
+
+@app.post(
+    "/predict/image",
+    response_model=ImagePredictionResponse,
+    summary="Predict image using all available models",
+    description="Upload an image file (jpg/png). It is evaluated on all 3 models and class index/confidence is returned.",
+)
+async def predict(image: UploadFile = File(...)):
+    """Accept an image upload and return a prediction using loaded model."""
+    
+    image_data = await image.read()
+    pil_img = Image.open(io.BytesIO(image_data)).convert("RGB")
+    profile_img = profile_image_for_cnn_predict(pil_img, crop_size=512)
+    if isinstance(profile_img, str):
+            return (f"Error processing image: {profile_img}")
+    else:
+        print(f"Profile image shape: {profile_img.shape}")
+        cnnPred = CNNPredict(profile_img)
+        resnetPred = ResnetPredict(profile_img)
+        clipPred = clip_predict(pil_img, crop_size=512)
+        #print(f"CNN Prediction (Real prob): {cnnPred:.4f}")
+        #print(f"ResNet Prediction (Real prob): {resnetPred:.4f}")
+        #print(f"CLIP Prediction (AI prob): {clipPred:.4f}")
+        resnet_class = 1 if resnetPred >= 0.5 else 0
+        cnn_class = 1 if cnnPred >= 0.5 else 0
+        clip_class = 0 if clipPred > 0.5 else 1
+        resnet_conf = resnetPred if resnetPred >= 0.5 else 1 - resnetPred
+        cnn_conf = cnnPred if cnnPred >= 0.5 else 1 - cnnPred
+        clip_conf = clipPred if clipPred > 0.5 else 1 - clipPred
+        #Predicted classes 1 is Real, 0 is AI
+        predictions = [
+            PredictionEntry(model="CNN", predicted_class=cnn_class, confidence=round(float(cnn_conf), 4)),
+            PredictionEntry(model="ResNet", predicted_class=resnet_class, confidence=round(float(resnet_conf), 4)),
+            PredictionEntry(model="CLIP", predicted_class=clip_class, confidence=round(float(clip_conf), 4)),
+        ]
+    return ImagePredictionResponse(predictions=predictions)
+
+@app.post(
+    "/predict/text",
+    response_model=TextPredictionResponse,
+    summary="Predict text",
+    description="POST a JSON body with `text` to get a predicted label and confidence.",
+)
+async def predict_text_endpoint(payload: TextPredictionRequest = Body(...)):
+    """Accept a text string and return a prediction of whether it's human or AI-generated."""
+    try:
+        # Use the text prediction function from textPreprocess.py
+        result = predict_text(payload.text)
+        
+        return TextPredictionResponse(
+            predicted_class=result["predicted_class"], 
+            confidence=result["confidence"]
+        )
+    except Exception as e:
+        # Return a fallback response in case of error
+        print(f"Error in text prediction: {e}")
+        return TextPredictionResponse(predicted_class="Human", confidence=0.5)