Update app/main.py

app/main.py

@@ -1,132 +1,3 @@
-# from fastapi import FastAPI, UploadFile, File, HTTPException
-# from fastapi.responses import JSONResponse
-# from PIL import Image
-# import numpy as np
-# import pickle
-# import tensorflow as tf
-# from keras.preprocessing import image
-# from keras.layers import GlobalMaxPooling2D
-# from keras.applications.resnet50 import ResNet50, preprocess_input
-# from sklearn.neighbors import NearestNeighbors
-# from numpy.linalg import norm
-# import io
-
-# # Initialize FastAPI app
-# app = FastAPI()
-
-# # Load feature list and filenames
-# feature_list = np.array(pickle.load(open('/Users/aryansingh/e-commerce_recommendations/Embeddings.pkl', 'rb')))
-# filenames = pickle.load(open('/Users/aryansingh/e-commerce_recommendations/filenames.pkl', 'rb'))
-
-# # Load the ResNet50 model
-# model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-# model.trainable = False
-# model = tf.keras.Sequential([
-#     model,
-#     GlobalMaxPooling2D()
-# ])
-
-# def feature_extraction(img, model):
-#     img = img.resize((224, 224))
-#     img_array = image.img_to_array(img)
-#     expanded_img_array = np.expand_dims(img_array, axis=0)
-#     preprocessed_img = preprocess_input(expanded_img_array)
-#     result = model.predict(preprocessed_img).flatten()
-#     normalized_result = result / norm(result)
-#     return normalized_result
-
-# def recommend(features, feature_list):
-#     neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-#     neighbors.fit(feature_list)
-#     distances, indices = neighbors.kneighbors([features])
-#     return indices
-
-# @app.post("/recommend")
-# async def recommend_fashion(file: UploadFile = File(...)):
-#     try:
-#         contents = await file.read()
-#         img = Image.open(io.BytesIO(contents))
-#         features = feature_extraction(img, model)
-#         indices = recommend(features, feature_list)
-#         recommendations = [filenames[idx] for idx in indices[0]]
-#         return JSONResponse(content={"recommendations": recommendations})
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=str(e))
-
-# # Run the app with: uvicorn script_name:app --reload
-# # Example: uvicorn main:app --reload if the script is named main.py
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
-
-# from fastapi import FastAPI, UploadFile, File, HTTPException, Request
-# from fastapi.responses import HTMLResponse
-# from fastapi.templating import Jinja2Templates
-# from fastapi.staticfiles import StaticFiles
-# from PIL import Image
-# import numpy as np
-# import pickle
-# import tensorflow as tf
-# from keras.preprocessing import image
-# from keras.layers import GlobalMaxPooling2D
-# from keras.applications.resnet50 import ResNet50, preprocess_input
-# from sklearn.neighbors import NearestNeighbors
-# from numpy.linalg import norm
-# import io
-# import os
-
-# app = FastAPI()
-
-# # Load feature list and filenames
-# feature_list = np.array(pickle.load(open('Embeddings.pkl', 'rb')))
-# filenames = pickle.load(open('filenames.pkl', 'rb'))
-
-# # Load the ResNet50 model
-# model = ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
-# model.trainable = False
-# model = tf.keras.Sequential([
-#     model,
-#     GlobalMaxPooling2D()
-# ])
-
-# # Initialize templates
-# templates = Jinja2Templates(directory="templates")
-
-# # Mount the directory containing images
-# app.mount("/archive", StaticFiles(directory="archive (4)"), name="archive")
-
-# def feature_extraction(img, model):
-#     img = img.resize((224, 224))
-#     img_array = image.img_to_array(img)
-#     expanded_img_array = np.expand_dims(img_array, axis=0)
-#     preprocessed_img = preprocess_input(expanded_img_array)
-#     result = model.predict(preprocessed_img).flatten()
-#     normalized_result = result / norm(result)
-#     return normalized_result
-
-# def recommend(features, feature_list):
-#     neighbors = NearestNeighbors(n_neighbors=6, algorithm='brute', metric='euclidean')
-#     neighbors.fit(feature_list)
-#     distances, indices = neighbors.kneighbors([features])
-#     return indices
-
-# @app.post("/recommend", response_class=HTMLResponse)
-# async def recommend_fashion(request: Request, file: UploadFile = File(...)):
-#     try:
-#         contents = await file.read()
-#         img = Image.open(io.BytesIO(contents))
-#         features = feature_extraction(img, model)
-#         indices = recommend(features, feature_list)
-#         recommendations = ["/archive (4)/images/" + os.path.basename(filenames[idx]) for idx in indices[0]]
-#         return templates.TemplateResponse("recommendation.html", {"request": request, "recommendations": recommendations})
-#     except Exception as e:
-#         raise HTTPException(status_code=400, detail=str(e))
-
-# # Run the app with: uvicorn main:app --reload
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=8000)
-
 from fastapi import FastAPI, UploadFile, File, HTTPException, Request
 from fastapi.responses import HTMLResponse
 from fastapi.templating import Jinja2Templates
@@ -210,9 +81,3 @@ async def recommend_fashion(request: Request, file: UploadFile = File(...)):
         return templates.TemplateResponse("recommendation.html", {"request": request, "recommendations": recommendations})
     except Exception as e:
         raise HTTPException(status_code=400, detail=str(e))
-
-# Run the app with: uvicorn main:app --reload
-if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-