app.py

from dotenv import load_dotenv
import os
import google.generativeai as genai
from PIL import Image
import gradio as gr

# Load all the environment variables
load_dotenv()
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))

# Function to load Google Gemini Pro Vision API and get a response
def get_gemini_response(input_prompt, uploaded_image):
    model = genai.GenerativeModel('gemini-1.5-flash')

    # Convert the uploaded image to bytes
    bytes_data = uploaded_image.read()
    image_parts = [
        {
            "mime_type": uploaded_image.type,
            "data": bytes_data
        }
    ]
    
    # Generate the content
    response = model.generate_content([input_prompt, image_parts[0], ""])
    return response.text

# Input prompt for the model
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."
"""

# Define the Gradio interface
def predict_crop_health(uploaded_image):
    if uploaded_image is None:
        return "No image uploaded."
    
    response = get_gemini_response(input_prompt, uploaded_image)
    return response

# Create a Gradio interface
iface = gr.Interface(
    fn=predict_crop_health,
    inputs=gr.Image(type="file", label="Upload Crop Image"),
    outputs="text",
    title="Gemini Crop Disease Detection App",
    description="Upload an image of a crop to predict its health and identify any diseases."
)

# Launch the Gradio app
iface.launch()