Duplicate from panditamey/flowerClassification

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
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+*.pickle filter=lfs diff=lfs merge=lfs -text
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+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
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+*tfevents* filter=lfs diff=lfs merge=lfs -text

Dockerfile

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+# Use the official Python 3.9 image
+FROM python:3.9
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "api:app", "--host", "0.0.0.0", "--port", "7860"]

README.md

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+---
+title: FlowerClassification
+emoji: 📉
+colorFrom: red
+colorTo: blue
+sdk: docker
+pinned: false
+duplicated_from: panditamey/flowerClassification
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

api.py

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+from fastapi import FastAPI,UploadFile,File
+from pydantic import BaseModel
+import pickle
+import json
+import pandas as pd
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing import image
+from tensorflow.keras.applications.inception_v3 import preprocess_input
+import numpy as np
+import os
+import gdown
+import lightgbm as lgb
+from PIL import Image
+
+CHUNK_SIZE = 1024
+
+app = FastAPI(
+    title='Flower Classification API',
+    description='API for Flower Classification',
+)
+
+id = "1ry4L9L1-kyc79F1MnYMemJ5P81Gr_mHP"
+output = "model_flowers_classification.h5"
+gdown.download(id=id, output=output, quiet=False)
+# from zipfile import ZipFile
+# with ZipFile("modelcrops.zip", 'r') as zObject:
+#     zObject.extractall(
+#         path="")
+    
+
+predict_ml=load_model('model_flowers_classification.h5')
+
+
+@app.post('/predict')
+async def flowerpredict(file: UploadFile = File(...)):
+    try:
+        contents = file.file.read()
+        with open(file.filename, 'wb') as f:
+            f.write(contents)
+    except Exception:
+        return {"message": "There was an error uploading the file"}
+    finally:
+        file.file.close()
+    classes = ['Lilly','Lotus','Orchid','Sunflower', 'Tulip']
+    img=image.load_img(str(file.filename),target_size=(224,224))
+    x=image.img_to_array(img)
+    x=x/255
+    img_data=np.expand_dims(x,axis=0)
+    prediction = predict_ml.predict(img_data)
+    predictions = list(prediction[0])
+    max_num = max(predictions)
+    index = predictions.index(max_num)
+    print(classes[index])
+    os.remove(str(file.filename))
+    return {"output":classes[index]}

requirements.txt

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+fastapi
+pydantic
+pandas
+tensorflow
+numpy
+uvicorn
+lightgbm
+gdown
+python-multipart
+Pillow