Update app.py

app.py

@@ -1,54 +1,58 @@
-from fastapi import FastAPI, File, UploadFile
-from fastapi.responses import JSONResponse
-# from tensorflow.keras.models import load_model
-# from tensorflow.keras.preprocessing import image
-import numpy as np
-from fastapi.middleware.cors import CORSMiddleware
-import io
-import os
-
-app = FastAPI()
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["http://localhost:3000"],  # Add your frontend URL
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-
-# Load your trained model
-# model = load_model('flower_species_model.h5')
-
-# def preprocess_image(img_file):
-#     img = image.load_img(img_file, target_size=(64, 64))
-#     img_array = image.img_to_array(img)
-#     img_array = np.expand_dims(img_array, axis=0)
-#     img_array /= 255.0
-#     return img_array
-
-@app.post("/predict")
-async def predict(files: list[UploadFile] = File(...)):
-    if not files:
-        return JSONResponse(content={"error": "No files uploaded"}, status_code=400)
-
-    predictions = []
-    for file in files:
-        contents = await file.read()
-        img = io.BytesIO(contents)
-        # preprocessed_img = preprocess_image(img)
-        # prediction = model.predict(preprocessed_img)
-        # predictions.append(prediction[0][0])
-        print("File uploaded")
-
-    threshold = 0.5
-    # predicted_classes = [1 if p > threshold else 0 for p in predictions]
-    # percentage_class_1 = (predicted_classes.count(1) / len(predicted_classes)) * 100
-
-    # return {"percentage_class_1": round(percentage_class_1, 2)}
-    return {"message": "Files uploaded", "percentage": 100}
-
-if __name__ == "__main__":
-    import uvicorn
+from fastapi import FastAPI, File, UploadFile
+from fastapi.responses import JSONResponse
+# from tensorflow.keras.models import load_model
+# from tensorflow.keras.preprocessing import image
+import numpy as np
+from fastapi.middleware.cors import CORSMiddleware
+import io
+import os
+
+app = FastAPI()
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Add your frontend URL
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Load your trained model
+# model = load_model('flower_species_model.h5')
+
+# def preprocess_image(img_file):
+#     img = image.load_img(img_file, target_size=(64, 64))
+#     img_array = image.img_to_array(img)
+#     img_array = np.expand_dims(img_array, axis=0)
+#     img_array /= 255.0
+#     return img_array
+
+@app.post("/predict")
+async def predict(files: list[UploadFile] = File(...)):
+    if not files:
+        return JSONResponse(content={"error": "No files uploaded"}, status_code=400)
+
+    predictions = []
+    for file in files:
+        contents = await file.read()
+        img = io.BytesIO(contents)
+        # preprocessed_img = preprocess_image(img)
+        # prediction = model.predict(preprocessed_img)
+        # predictions.append(prediction[0][0])
+        print("File uploaded")
+
+    threshold = 0.5
+    # predicted_classes = [1 if p > threshold else 0 for p in predictions]
+    # percentage_class_1 = (predicted_classes.count(1) / len(predicted_classes)) * 100
+
+    # return {"percentage_class_1": round(percentage_class_1, 2)}
+    return {"message": "Files uploaded", "percentage": 100}
+
+@app.get("/")
+async def main():
+    return {"message": "Hello World"}
+
+if __name__ == "__main__":
+    import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=8000)