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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) |