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from dotenv import load_dotenv
load_dotenv() ## load all the environment variables
import streamlit as st
import os
import google.generativeai as genai
from PIL import Image
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
## Function to load Google Gemini Pro Vision API And get response
def get_gemini_repsonse(input,image,prompt):
model=genai.GenerativeModel('gemini-pro-vision')
response=model.generate_content([input,image[0],prompt])
return response.text
def input_image_setup(uploaded_file):
# Check if a file has been uploaded
if uploaded_file is not None:
# Read the file into bytes
bytes_data = uploaded_file.getvalue()
image_parts = [
{
"mime_type": uploaded_file.type, # Get the mime type of the uploaded file
"data": bytes_data
}
]
return image_parts
else:
raise FileNotFoundError("No file uploaded")
##initialize our streamlit app
st.set_page_config(page_title="Crop Disease Detection App")
st.header("Gemini Crop Disease App")
input=st.text_input("Input Prompt: ",key="input")
uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
image=""
if uploaded_file is not None:
image = Image.open(uploaded_file)
st.image(image, caption="Uploaded Image.", use_column_width=True)
submit=st.button("Predict Crop/Plant Health")
input_prompt="""
"You are an expert in computer vision and agriculture who can easily predict the disease of the plant. "
"Analyze the following image and provide 6 outputs in a structured table format: "
"1. Crop in the image, "
"2. Whether it is infected or healthy, "
"3. Type of disease (if any), "
"4. How confident out of 100% whether image is healthy or infected "
"5. Reason for the disease such as whether it is happening due to fungus, bacteria, insect bite, poor nutrition, etc., "
"6. Precautions for it."
"""
## If submit button is clicked
if submit:
image_data=input_image_setup(uploaded_file)
response=get_gemini_repsonse(input_prompt,image_data,input)
st.subheader("The Response is")
st.write(response)
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