app.py

import streamlit as st
import sys
import openai
import toml
from openai import OpenAI


import pandas as pd
import os
import random
import glob
import re
from io import BytesIO
from six import BytesIO
import cv2
import warnings
warnings.filterwarnings('ignore')

from io import BytesIO
import tempfile
import time
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import seaborn as sns
from PIL import Image
from PIL import ImageColor
from PIL import ImageDraw
from PIL import ImageFont
from PIL import ImageOps
import json

import numpy as np
np.random.seed(42)
import tensorflow as tf
tf.random.set_seed(42)

import tensorflow.keras as k
k.utils.set_random_seed(42) # idem keras

from keras.backend import manual_variable_initialization
manual_variable_initialization(True) # https://github.com/keras-team/keras/issues/4875#issuecomment-296696536

from tensorflow.keras.applications.xception import preprocess_input
from tensorflow.keras.applications.xception import Xception
from scipy.stats import mode
from tensorflow.keras.applications.mobilenet import MobileNet
from tensorflow.keras.applications.mobilenet import preprocess_input as mobilenet_preprocess
from tensorflow.keras.applications.xception import preprocess_input as xception_preprocess

import tensorflow_hub as hub


@st.cache_resource
def load_models():
    #OpenAI elements
    #secrets = toml.load(".vscode/streamlit/secrets.toml")
    #client_d = OpenAI(api_key = secrets["OPENAI_API_KEY"])
    client_d = OpenAI(api_key = st.secrets["OPENAI_API_KEY"])

    module_handle = "https://tfhub.dev/google/faster_rcnn/openimages_v4/inception_resnet_v2/1"
    detector_d = hub.load(module_handle).signatures['default'];

    file_path = '.vscode/inputs/' # folder with files
    Dis_percentage_d = pd.read_csv(os.path.join(file_path,'Spots_Percentage_results.csv'))
    Details_d = pd.read_csv(os.path.join(file_path,'Plant_details.csv'))

    # Load the TensorFlow Lite model
    #model_path = '.vscode/model/model.tflite'
    #interpreter = tf.lite.Interpreter(model_path=model_path)
    #interpreter.allocate_tensors()

    print("Loading CNN Model")
    model3_path = '.vscode/model/CNN_0424.keras'
    model3_weights_path = '.vscode/model/CNN_weights.hdf5'
    cnn_model_d = k.models.load_model(model3_weights_path)

    print("Loading Xception Model")
    model1_path = '.vscode/model/XCeption_weights.hdf5'
    xception_model_d = k.models.load_model(model1_path)

    print("Loading Mobilenet Model")
    model2_path = '.vscode/model/MobileNet_weights.hdf5'
    mobilenet_model_d = k.models.load_model(model2_path)
    print("finished loading models")

    with open('.vscode/inputs/Xception_0422_labels.json', 'r') as file:
        loaded_class_indices = {k: int(v) for k, v in json.load(file).items()}
        class_labels_d = {value: key for key, value in loaded_class_indices.items()} # Convert keys to int

    #xception_model.weights[-1]
    #mobilenet_model.weights[-1]
    #cnn_model.weights[-1]

    return client_d,detector_d,Dis_percentage_d,Details_d,cnn_model_d,xception_model_d,mobilenet_model_d,class_labels_d


# Loading the models. load_models() methos is cached and will be loaded only once during the initial boot.
client,detector,Dis_percentage,Details,cnn_model,xception_model,mobilenet_model,class_labels  = load_models()


# Identify extent of spot or lesion coverage on leaf
def identify_spots_or_lesions(img):
    
    cv_image = cv2.cvtColor(np.array(img), cv2.COLOR_RGB2BGR)
    lab_image = cv2.cvtColor(cv_image, cv2.COLOR_BGR2Lab)
    l_channel, a_channel, b_channel = cv2.split(lab_image)
    blur = cv2.GaussianBlur(a_channel,(3,3),0)
    thresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1]

    # Morphological clean-up
    kernel = np.ones((3,3), np.uint8)
    cleaned = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel, iterations=1) # Opening = erosion followed by dilation
    edges = cv2.Canny(cleaned,100,300)

    # Filter and contours
    contours, _ = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
    max_area = 18000
    filtered_contours = [cnt for cnt in contours if cv2.contourArea(cnt) < max_area]

    # Calculate the percentage of spots/lesions
    spot_pixels = sum(cv2.contourArea(cnt) for cnt in filtered_contours)
    total_pixels = edges.shape[0] * edges.shape[1]
    percentage_spots = (spot_pixels / total_pixels)*100
    st.write(f"Percentage of spots/lesions: {percentage_spots:.2f}%")

    # Draw filtered contours
    contoured_image = cv2.drawContours(cv_image.copy(), filtered_contours, -1, (0, 255, 0), 1)

    # Visualization
    mfig = plt.figure(figsize=(25, 8))
    plt.subplot(1, 5, 1)
    plt.imshow(cv2.cvtColor(np.array(image), cv2.COLOR_BGR2RGB))
    plt.title('Original Image')

    plt.subplot(1, 5, 2)
    plt.imshow(a_channel, cmap='gray')
    plt.title('LAB - A channel')

    plt.subplot(1, 5, 3)
    plt.imshow(edges, cmap='gray')
    plt.title('Edge Detection')

    plt.subplot(1, 5, 4)
    plt.imshow(cleaned, cmap='gray')
    plt.title('Thresholded & Cleaned')

    plt.subplot(1, 5, 5)
    plt.imshow(cv2.cvtColor(contoured_image, cv2.COLOR_BGR2RGB))
    plt.title('Spots or Lesions Identified')
    #plt.show()
    st.pyplot(mfig)

    return(percentage_spots)


# Plot disease percentage
def plot_dis_percentage(row, percentage):
   # Determine the range category for the title
   if percentage < row['Q1']:
       category = 'Mild'
       color = 'yellow'
   elif row['Q1'] <= percentage <= row['Q3']:
       category = 'Moderate'
       color = 'orange'
   else:
       category = 'Severe'
       color = 'darkred'

   # Normalize the data to the range of [0, 1]
   min_val = row['min']
   max_val = row['max']
   range_val = max_val - min_val
   percentage_norm = (percentage - min_val) / range_val

   # Create a figure and a set of subplots
   fig, ax = plt.subplots(figsize=(6, 1))

   # Create the ranges for Low, Medium, and High
   ax.axhline(0, xmin=0, xmax=(row['Q1'] - min_val) / range_val, color='yellow', linewidth=4, label='Mild')
   ax.axhline(0, xmin=(row['Q1'] - min_val) / range_val, xmax=(row['Q3'] - min_val) / range_val, color='orange', linewidth=4, label='Moderate')
   ax.axhline(0, xmin=(row['Q3'] - min_val) / range_val, xmax=1, color='darkred', linewidth=4, label='Severe')

   # Plot the actual percentage as an arrow
   ax.annotate('', xy=(percentage_norm, 0.1), xytext=(percentage_norm, -0.1),
               arrowprops=dict(facecolor=color, shrink=0.05, width=1, headwidth=10))

   # Set display parameters
   ax.set_yticks([])  # No y-ticks
   ax.set_xticks([])  # Remove specific percentage figures from the x-axis
   ax.set_xlim([0, 1])  # Set x-limits to normalized range
   titlet = f'{category} - {row["Plant"]}'
   ax.set_title(titlet)
   ax.set_xlabel('Value (Normalized)')

   plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
   plt.tight_layout()
   st.pyplot(fig)
   return titlet



def resize_image(image, target_size=(224, 224)):
    return image.resize(target_size)



# Classify the image
def classify_image(image):
    # Convert PIL Image to a NumPy array
    image_np = np.array(image)

    # Preprocess the image as needed
    resized_image = cv2.resize(image_np, (224, 224), interpolation=cv2.INTER_LINEAR)

    img_array = np.array(resized_image, dtype='float32')
    img_array = np.expand_dims(img_array, axis=0)
    img_batch = np.tile(img_array, (32, 1, 1, 1))

    # preprocess_input from Xception to scale the image to -1 to +1
    #img_array = preprocess_input(img_array)

    mobilenet_input = mobilenet_preprocess(np.copy(img_batch))
    xception_input = xception_preprocess(np.copy(img_batch))
    cnn_input = img_batch / 255.0  # normalization for generic CNN model

    # Predict using the models
    mobilenet_preds = mobilenet_model(mobilenet_input, training = False)
    xception_preds = xception_model(xception_input, training = False)
    cnn_preds = cnn_model(cnn_input, training = False)

    # Get the most likely class index from predictions
    mobilenet_class = np.argmax(mobilenet_preds, axis=1)
    xception_class = np.argmax(xception_preds, axis=1)
    cnn_class = np.argmax(cnn_preds, axis=1)

    # --------------------------------
    # mean probabilities from each model
    averaged_probs = (mobilenet_preds + xception_preds + cnn_preds) / 3
    averaged_probs_np = averaged_probs.numpy()

    # top two most likely class indices
    top_two_probs_indices = np.argsort(-averaged_probs_np, axis=1)[:, :2]
    top_class_index = top_two_probs_indices[:, 0]
    second_class_index = top_two_probs_indices[:, 1]
    top_class_prob = np.max(averaged_probs_np, axis=1)
    second_class_prob = averaged_probs_np[np.arange(top_class_index.size), second_class_index]
    predicted_class_name = class_labels[top_class_index[0]]
    second_class_name = class_labels[second_class_index[0]]
    # --------------------------------

    st.write("Image class:", predicted_class_name)
    st.write(f"Confidence: {top_class_prob[0]:.2%}")

    if "healthy" in predicted_class_name:
        st.write(f"{predicted_class_name} is healthy, skipping further analysis.")
        return
    else:
        if "Background_without_leaves" in predicted_class_name:
            st.write(f"{predicted_class_name} is not recognized as a plant image, skipping further analysis.")
            return
        else:
            spots_percentage = identify_spots_or_lesions(image)
    
    if predicted_class_name in Dis_percentage['Plant'].values:
            row = Dis_percentage.loc[Dis_percentage['Plant'] == predicted_class_name].iloc[0]
            severity_disease = plot_dis_percentage(row, spots_percentage)

            if predicted_class_name in Details['Plant'].values:
                row = Details.loc[Details['Plant'] == predicted_class_name].iloc[0]
                #st.write("Disease Identification:", row[4])
                st.write("----------------------------------")
                #st.write("Management:", row[5])
                #st.markdown(severity_disease)
            return severity_disease, top_class_prob[0], second_class_name
    else:
        st.write("No data available for this plant disease in DataFrame.")

    if top_class_prob[0] < 0.999:  # threshold close to 1 to handle floating-point precision issues
        st.write("Second predicted class:", second_class_name)
        st.write(f"Second class confidence: {second_class_prob[0]:.3%}")
    else:
        st.write("Second predicted class: None")
    
    return


def display_image(image):
    fig = plt.figure(figsize=(12, 6))
    plt.grid(False)
    plt.imshow(image)
    plt.show()



def draw_bounding_box_on_image(image, ymin, xmin, ymax, xmax, color, font, thickness=4, display_str_list=()):
  """Adds a bounding box to an image."""
  draw = ImageDraw.Draw(image)
  im_width, im_height = image.size
  (left, right, top, bottom) = (xmin * im_width, xmax * im_width, ymin * im_height, ymax * im_height)
  draw.line([(left, top), (left, bottom), (right, bottom), (right, top), (left, top)], width=thickness, fill=color)

  # height of the display strings added to the top of the bounding
  # box exceeds the top of the image - stack below:
  display_str_heights = [font.getbbox(ds)[3] for ds in display_str_list]
  total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights)

  if top > total_display_str_height:
    text_bottom = top
  else:
    text_bottom = top + total_display_str_height
  # Reverse list and print from bottom to top.
  for display_str in display_str_list[::-1]:
    bbox = font.getbbox(display_str)
    text_width, text_height = bbox[2], bbox[3]
    margin = np.ceil(0.05 * text_height)
    draw.rectangle([(left, text_bottom - text_height - 2 * margin),  (left + text_width, text_bottom)], fill=color)
    draw.text((left + margin, text_bottom - text_height - margin), display_str, fill="black", font=font)
    text_bottom -= text_height - 2 * margin



def draw_boxes(image, boxes, class_names, scores, max_boxes=3, min_score=0.1):
  #"""Overlay labeled boxes on an image with formatted scores and label names."""
  colors = list(ImageColor.colormap.values())

  font = ImageFont.load_default()

  # Prepare a list of all detections that meet the score threshold
  filtered_boxes = [(boxes[i], scores[i], class_names[i]) for i in range(len(scores)) if scores[i] >= min_score]

  # Sort detections based on scores in descending order
  filtered_boxes.sort(key=lambda x: x[1], reverse=False)

  # Process each box to draw (limited by max_boxes)
  for i, (box, score, class_name) in enumerate(filtered_boxes[:max_boxes]):
    ymin, xmin, ymax, xmax = tuple(box)
    display_str = "{}: {:.2f}%".format(class_name.decode("ascii"), score * 100)
    color = colors[hash(class_name) % len(colors)]
    draw_bounding_box_on_image( image, ymin, xmin, ymax, xmax, color, font, display_str_list=[display_str])

  # Convert PIL Image back to numpy array for display (if necessary)
  return np.array(image) if isinstance(image, Image.Image) else image




# ----------------------------------------------------------------------------------------------------//
# Streamlit app


def openai_remedy(searchval):
        completion = client.chat.completions.create(
                            model="gpt-4-turbo",
                            messages=[
                            {"role": "user", "content": "List out the most relevant remediation steps for " + searchval + " in 7 bullet points"}  
                            ],
                            temperature=0.1,
                            max_tokens=2000,
                            top_p=0.1
                        )
        st.markdown(completion.choices[0].message.content)
        #st.markdown(completion.choices[0].delta.content)
        return


tab1, tab2, tab3 = st.tabs(["Home", "Solution", "Team"])


#First Tab: Title of Application and description
with tab1:
    st.title("Plant Disease Identification")
    # Display Plant Care Icon
    st.image(".vscode/inputs/plantIcon.jpg", width=100)
    st.markdown("Plant diseases are a significant threat to agricultural productivity worldwide, causing substantial crop losses and economic damage. These diseases can be caused by various factors, including fungi, bacteria, viruses, and environmental stressors. Recognizing the symptoms of plant diseases early is crucial for implementing effective management strategies and minimizing the impact on crop yield and quality.")
    # Importance of Early Detection
    st.write("""
### Importance of Early Detection

Early detection of plant diseases is paramount for farmers to protect their crops and livelihoods. By identifying diseases at their onset, farmers can implement timely interventions, such as targeted pesticide applications or cultural practices, to prevent the spread of diseases and reduce crop losses. Early detection also reduces the need for excessive chemical inputs, promoting sustainable agriculture practices and environmental stewardship.
""")
    
    # Types of Plant Diseases Detected
    st.image(".vscode/inputs/Plant-disease-classifier-with-ai-blog-banner.jpg", width=700)

    st.write("With more than 50% of the population in India still relying on agriculture and with the average farm sizes and incomes being very small, we believe that cost effective solutions for early detection and treatment solutions for disease could significantly improve the quality of produce and lives of farmers. With smartphones being ubiquitous, we believe providing solutions to farmers over a smartphone is the most penetrative form.")




#Second Tab: Image upload and disease detection and remidy susgestions
with tab2: 
    st.title("Plant classification, Disease detection and management")
    # Load and display the image
    uploaded_file = st.file_uploader("Upload Leaf Image...", type=["jpg", "jpeg", "png"], key="uploader")

    if uploaded_file is not None:
        print("Image successfully uploaded!")
        # Read the uploaded image file
        #st.image(uploaded_file, caption='Uploaded Image', use_column_width=True,width=100)
        st.image(uploaded_file, caption='Uploaded Image', width=300)
        image = Image.open(uploaded_file)

        image_for_drawing = image.copy()

        # convert PIL format to TensorFlow format
        img = tf.convert_to_tensor(image)
        converted_img = tf.image.convert_image_dtype(img, tf.float32)[tf.newaxis, ...] #scales 0-1

        start_time = time.time()
        result = detector(converted_img)
        end_time = time.time()

        result = {key: value.numpy() for key, value in result.items()}

        #st.write("Found %d objects." % len(result["detection_scores"]))
        #st.write("Inference time: ", end_time - start_time)

        detection_scores = result["detection_scores"]
        detection_class_entities = result["detection_class_entities"]

        # Class Detections displays
        image_with_boxes = draw_boxes(image_for_drawing, result["detection_boxes"],detection_class_entities, detection_scores)
        #display_image(image_with_boxes)
        st.image(image_with_boxes, caption='Uploaded Image', width=300)

        top_3_idx = np.argsort(-detection_scores)[:3]

        for idx in top_3_idx:
            entity = detection_class_entities[idx].decode('utf-8')

            if "Plant" == entity:
                plant_score = detection_scores[idx]
                st.write(f"Plant Probability score using Faster R-CNN Inception Resnet V2 Object detection model :  {plant_score:.2%}")
                result1 = classify_image(image)

                if result1 is not None:
                    #st.markdown("Result " + result)
                    new1 = result1[0] + ""
                    newresult = new1.replace("_"," ")
                    newresult2 = newresult.replace("-"," ")
                    st.markdown("Fetching disease management steps for " + ":red[" + newresult2 + "]... :eyes:")
                    openai_remedy(newresult2)
                    
    else:
        print("No file uploaded.")
    
    # Disclaimer
    st.write("""
### Disclaimer

While our disease identification system strives for accuracy and reliability, it is essential to note its limitations. False positives or false negatives may occur, and users are encouraged to consult with agricultural experts for professional advice and decision-making.
""")
    




# Third Tab
with tab3:
    st.title("CDS Batch 6 - Group 2:")
    st.divider()

    st.write("Abhinav Singh")
    st.divider()  
    
    st.write("Ankit Kourav")
    st.divider()  

    st.write("Challoju Anurag.")
    st.divider()  

    st.write("Madhucchand Darbha")
    st.divider()  

    st.write("Neha Gupta")
    st.divider()  

    st.write("Pradeep Rajagopal")
    st.divider()  

    st.write("Rakesh Vegesana")
    st.divider()  

    st.write("Sachin Sharma")
    st.divider()  

    st.write("Shashank Srivastava")
    st.divider()