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Create app.py
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app.py
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import os
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import numpy as np
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import cv2
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from fastapi import FastAPI
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from fastapi.responses import HTMLResponse
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from pydantic import BaseModel
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from tensorflow import keras
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from starlette.responses import FileResponse
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from starlette.middleware.cors import CORSMiddleware
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# Define the FastAPI app
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app = FastAPI()
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# Add CORS middleware
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# Load the model
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model_path = 'sketch2draw_model.h5' # Update with your model path
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model = keras.models.load_model(model_path)
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# Define the request body
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class TextureRequest(BaseModel):
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texture: str
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# Load class names for predictions
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class_names = ['grass', 'dirt', 'wood', 'water', 'sky', 'clouds']
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@app.get("/", response_class=HTMLResponse)
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async def read_root():
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return """
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<html>
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<head>
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<title>Sketch to Draw</title>
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</head>
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<body>
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<h1>Sketch to Draw Model</h1>
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<form action="/predict" method="post">
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<input type="text" name="texture" placeholder="Enter texture name (grass, dirt, wood, water, sky, clouds)">
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<button type="submit">Predict</button>
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</form>
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</body>
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</html>
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"""
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@app.post("/predict")
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async def predict_texture(request: TextureRequest):
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texture_name = request.texture
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# Process the input texture (you can modify this part)
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# Example: Load image and preprocess it
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# image = cv2.imread(f'path_to_your_texture_images/{texture_name}.png')
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# image = cv2.resize(image, (128, 128)) # Resize as per your model's input
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# image = np.expand_dims(image, axis=0) / 255.0 # Normalize if needed
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# Make prediction
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predictions = model.predict(image) # Add your processed image here
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predicted_class = class_names[np.argmax(predictions)]
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return {"predicted_texture": predicted_class}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8080)
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