Update main.py

main.py

@@ -1,63 +1,60 @@
-from fastapi import FastAPI, File, UploadFile
+from fastapi import FastAPI, File, UploadFile, Request, Form
 from fastapi.responses import JSONResponse
 from fastapi.middleware.cors import CORSMiddleware
-import numpy as np
-import tensorflow as tf
-from PIL import Image
-from io import BytesIO
+import uvicorn
+import pandas as pd
+from projects.DL_CatDog.DL_CatDog import preprocess_image, read_image, model_DL_CatDog
+from projects.ML_StudentPerformance.ML_StudentPerformace import predict_student_performance, create_custom_data, form1
 
 app = FastAPI()
 
 # Add CORS middleware
 app.add_middleware(
-  CORSMiddleware,
-  allow_origins=["*"],  # You can restrict this to specific origins if needed
-  allow_credentials=True,
-  allow_methods=["*"],
-  allow_headers=["*"],
+    CORSMiddleware,
+    allow_origins=["*"],  # You can restrict this to specific origins if needed
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
 )
 
-
-# Load your pre-trained model
-MODEL_PATH = "./models/model_catdog1.h5"
-model = tf.keras.models.load_model(MODEL_PATH)
-
-@app.get("/")
-def home():
-  return {"message": "FastAPI server is running on Hugging Face Spaces!"}
-
+# Health check route
 @app.get("/api/working")
 def home():
-  return {"message": "FastAPI server is running on Hugging Face Spaces!"}
-  
-# Helper function to read and convert the uploaded image
-def read_image(file: UploadFile) -> Image.Image:
-  image = Image.open(BytesIO(file.file.read())).convert('RGB')
-  return image
+    return {"message": "FastAPI server is running on Hugging Face Spaces!"}
 
-# Helper function to preprocess the image
-def preprocess_image(image: Image.Image):
-  image = image.resize((128, 128))  # Adjust to the size expected by your model
-  image = np.array(image) / 255.0  # Normalize the image
-  image = np.expand_dims(image, axis=0)  # Add batch dimension
-  return image
-
-# Route for classifying image
+# # Prediction route for DL_CatDog
 @app.post("/api/predict1")
-async def predict(file: UploadFile = File(...)):
-  try:
-    # Read and preprocess the image
-    image = read_image(file)
-    preprocessed_image = preprocess_image(image)
-
-    # Perform prediction
-    prediction = model.predict(preprocessed_image)
-    predicted_class = "Dog" if np.round(prediction[0][0]) == 1 else "Cat"
-
-    # Return the prediction result
-    return JSONResponse(content={"ok": 1, "prediction": predicted_class})
-  except Exception as e:
-    return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)
+async def predict_DL_CatDog(file: UploadFile = File(...)):
+    try:
+        image = read_image(file)
+        preprocessed_image = preprocess_image(image)
+        prediction = model_DL_CatDog.predict(preprocessed_image)
+        predicted_class = "Dog" if np.round(prediction[0][0]) == 1 else "Cat"
+        return JSONResponse(content={"ok": 1, "prediction": predicted_class})
+    except Exception as e:
+        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)
+
+# New Prediction route for ML_StudentPerformance
+@app.post("/api/predict2")
+async def predict_student_performance_api(request: form1):
+    print(request, end='\n\n\n\n')
+    try:
+        # Create the CustomData object
+        custom_data = create_custom_data(
+            gender= request.gender,
+            ethnicity= request.ethnicity,
+            parental_level_of_education= request.parental_level_of_education,
+            lunch= request.lunch,
+            test_preparation_course= request.test_preparation_course,
+            reading_score= request.reading_score,
+            writing_score= request.writing_score
+        )
+        # Perform the prediction
+        result = predict_student_performance(custom_data)
+        return JSONResponse(content={"ok": 1, "prediction": result})
+    except Exception as e:
+        return JSONResponse(content={"ok": -1, "message": f"Something went wrong! {str(e)}"}, status_code=500)
+
+# Main function to run the FastAPI server
 if __name__ == "__main__":
-  import uvicorn
-  uvicorn.run(app, host="0.0.0.0", port=7860)
+    uvicorn.run(app, host="0.0.0.0", port=7860)