Spaces:
Sleeping
Sleeping
File size: 2,917 Bytes
b42b45f c841775 b42b45f c841775 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 |
from keras.models import load_model
from fastapi import FastAPI
import gradio as gr
import requests
import numpy as np
from PIL import Image
from tensorflow import keras
from io import BytesIO
import uvicorn
#from pyModel import Item
app = FastAPI()
@app.post('/post')
def newPost(str):
input_image = url_to_img(str)
processed_image = process(input_image)
crop = crop_predict(processed_image)
disease = disease_predict(processed_image, crop)
return [crop,disease]
def url_to_img(image_url):
# Load the image from the URL
response = requests.get(image_url)
input_image = Image.open(BytesIO(response.content))
return input_image
def process(input_image):
# Preprocess the image to match model's input dimensions
input_image = input_image.resize((224, 224)) # Adjust the dimensions
input_image = np.array(input_image) / 255.0 # Normalize pixel values to [0, 1]
processed_image = np.expand_dims(input_image, axis=0) # Add batch dimension
return processed_image
def crop_predict(processed_image):
# Load the trained model
model = keras.models.load_model('./crop_model.h5')
# Make predictions
predictions = model.predict(processed_image)
# Process the predictions (e.g., get class labels)
class_labels = ["Corn", "Potato", "Rice", "Wheat"]
predicted_class = np.argmax(predictions, axis=1)
predicted_label = class_labels[predicted_class[0]]
return predicted_label
def disease_predict(processed_image, crop):
if crop == "Potato":
# Load the trained model
model = keras.models.load_model('./potato_model.h5')
# Process the predictions (e.g., get class labels)
class_labels = ["Potato_Early_Blight", "Potato_Healthy", "Potato_Late_Blight"]
elif crop == "Corn":
# Load the trained model
model = keras.models.load_model('./corn_model.h5')
# Process the predictions (e.g., get class labels)
class_labels = ["Corn_Common_Rust", "Corn_Gray_Leaf_Spot", "Corn_Healthy", "Corn_Northern_Leaf_Blight"]
elif crop == "Rice":
# Load the trained model
model = keras.models.load_model('./rice_model.h5')
# Process the predictions (e.g., get class labels)
class_labels = ["Rice_Brown_Spot", "Rice_Healthy", "Rice_Leaf_Blast", "Rice_Neck_Blast"]
elif crop == "Wheat":
# Load the trained model
model = keras.models.load_model('./wheat_model.h5')
# Process the predictions (e.g., get class labels)
class_labels = ["Wheat_Brown_Rust", "Wheat_Healthy", "Wheat_Yellow_Rust"]
# Make predictions
predictions = model.predict(processed_image)
predicted_class = np.argmax(predictions, axis=1)
predicted_label = class_labels[predicted_class[0]]
return predicted_label
iface = gr.Interface(fn = newPost , inputs=['text'] , outputs=['text'])
iface.launch(share=True) |