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30b48ba | 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 90 91 92 93 94 95 96 97 | import streamlit as st
import tensorflow as tf
from tensorflow.keras.preprocessing import image
import numpy as np
from PIL import Image
from dotenv import load_dotenv
import os
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain_groq import ChatGroq
# Load environment variables
load_dotenv()
os.environ['GROQ_API_KEY'] = os.getenv("GROQ_API_KEY")
groq_api_key = os.getenv("GROQ_API_KEY")
# Initialize Chatbot
chat = ChatGroq(groq_api_key=groq_api_key, model_name="llama-3.3-70b-versatile")
if 'flow_messages' not in st.session_state:
st.session_state['flow_messages'] = [
SystemMessage(content=(
"You are a highly intelligent assistant specializing in food safety and hygiene. "
"You help users interpret food contamination results, provide safe food-handling practices, "
"and answer questions related to food quality and safety."
))
]
# Function to get chatbot response
def get_response(question):
st.session_state['flow_messages'].append(HumanMessage(content=question))
answer = chat(st.session_state['flow_messages'])
st.session_state['flow_messages'].append(AIMessage(content=answer.content))
return answer.content
# Load the trained food contamination model
model = tf.keras.models.load_model('model.h5')
# Preprocess uploaded image
def preprocess_image(img, target_size=(224, 224)):
img = img.resize(target_size) # Resize to match the model's input size
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
img_array = img_array / 255.0 # Normalize to [0, 1]
return img_array
# Streamlit app setup
st.set_page_config(page_title="Food Safety Detection and Chatbot", layout="wide")
st.title("Food Safety Detection and AI Assistant")
st.write("Upload an image to determine if food is safe or contaminated.")
# Image classification section
st.header("Food Contamination Detection")
uploaded_file = st.file_uploader("Upload an image of food", type=["jpg", "jpeg", "png"])
if uploaded_file is not None:
try:
img = Image.open(uploaded_file)
st.image(img, caption="Uploaded Image")
img_array = preprocess_image(img)
prediction = model.predict(img_array)
if prediction[0] > 0.5:
st.success("Prediction: Good Food")
else:
st.error("Prediction: Contaminated Food")
except Exception as e:
st.error(f"An error occurred while processing the image: {e}")
# Sidebar Chatbot
st.sidebar.title("Chatbot Assistant")
with st.sidebar:
if 'chat_history' not in st.session_state:
st.session_state['chat_history'] = []
st.write("Ask me anything related to food safety and hygiene:")
user_input = st.text_input("Your Question", key="sidebar_input")
if st.button("Send", key="sidebar_send"):
if user_input.strip():
response = get_response(user_input)
st.session_state['chat_history'].append((user_input, response))
if st.button("Clear Chat"):
st.session_state['chat_history'] = []
st.session_state['flow_messages'] = [
SystemMessage(content=(
"You are a highly intelligent assistant specializing in food safety and hygiene. "
"You help users interpret food contamination results, provide safe food-handling practices, "
"and answer questions related to food quality and safety."
))
]
st.write("Chat History:")
for question, answer in st.session_state['chat_history']:
st.markdown(f"**You:** {question}")
st.markdown(f"**Bot:** {answer}")
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