app.py

import streamlit as st
from transformers import pipeline
from PIL import Image
from huggingface_hub import InferenceClient
import os
import openai  # Added import
from openai.error import OpenAIError  # For specific exception handling

# Set page configuration
st.set_page_config(
    page_title="Plate Mate - Your Culinary Assistant",
    page_icon="🍽️",
    layout="centered",
    initial_sidebar_state="expanded",
)

def local_css():
    st.markdown(
        """
        <style>
        /* Your existing CSS styles here */
        </style>
        """, unsafe_allow_html=True
    )

local_css()  # Apply the CSS

# Hugging Face API key
API_KEY = st.secrets["HF_API_KEY"]

# Initialize the Hugging Face Inference Client
client = InferenceClient(api_key=API_KEY)

# Load the image classification pipeline
@st.cache_resource
def load_image_classification_pipeline():
    """ Load the image classification pipeline using a pretrained model. """
    return pipeline("image-classification", model="Shresthadev403/food-image-classification")

pipe_classification = load_image_classification_pipeline()

# Function to generate ingredients using Hugging Face Inference Client
def get_ingredients_qwen(food_name):
    """ Generate a list of ingredients for the given food item using Qwen NLP model. Returns a clean, comma-separated list of ingredients. """
    messages = [
        {
            "role": "user",
            "content": f"List only the main ingredients for {food_name}. "
                       f"Respond in a concise, comma-separated list without any extra text or explanations."
        }
    ]
    try:
        completion = client.chat.completions.create(
            model="Qwen/Qwen2.5-Coder-32B-Instruct", messages=messages, max_tokens=50
        )
        generated_text = completion.choices[0]['message']['content'].strip()
        return generated_text
    except Exception as e:
        return f"Error generating ingredients: {e}"

# **Set OpenAI API Key**
openai.api_key = st.secrets["openai"]  # Ensure you have this in your secrets

# Main content
st.markdown('<div class="title"><h1>PlateMate - Your Culinary Assistant</h1></div>', unsafe_allow_html=True)

# Add banner image with existence check
banner_image_path = "IR_IMAGE.png"
if os.path.exists(banner_image_path):
    st.image(banner_image_path, use_container_width=True)
else:
    st.warning(f"Banner image '{banner_image_path}' not found.")

# Sidebar for model information (hidden on small screens)
with st.sidebar:
    st.title("Model Information")
    st.write("**Image Classification Model**")
    st.write("Shresthadev403/food-image-classification")
    st.write("**LLM for Ingredients**")
    st.write("Qwen/Qwen2.5-Coder-32B-Instruct")
    st.markdown("---")
    st.markdown("<p style='text-align: center;'>Developed by Muhammad Hassan Butt.</p>", unsafe_allow_html=True)


# File uploader
st.subheader("Upload a food image:")
uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])

if uploaded_file is not None:
    # Display the uploaded image
    if isinstance(uploaded_file, str):  # Sample image selected
        if os.path.exists(uploaded_file):
            image = Image.open(uploaded_file)
        else:
            st.error(f"Sample image '{uploaded_file}' not found.")
            image = None
    else:  # User uploaded image
        image = Image.open(uploaded_file)

    if image:
        st.image(image, caption="Uploaded Image", use_container_width=True)

        # Classification button
        if st.button("Classify"):
            with st.spinner("Classifying..."):
                try:
                    # Make predictions
                    predictions = pipe_classification(image)
                    if predictions:
                        # Display only the top prediction
                        top_food = predictions[0]['label']
                        confidence = predictions[0]['score']
                        st.header(f"🍽️ Food: {top_food} ({confidence*100:.2f}% confidence)")

                        # Generate and display ingredients for the top prediction
                        st.subheader("📝 Ingredients")
                        try:
                            ingredients = get_ingredients_qwen(top_food)
                            st.write(ingredients)
                        except Exception as e:
                            st.error(f"Error generating ingredients: {e}")

                        # **Healthier Alternatives using OpenAI API**
                        st.subheader("💡 Healthier Alternatives")
                        try:
                            response = openai.ChatCompletion.create(
                                model="gpt-4",  # You can choose the model you prefer
                                messages=[
                                    {
                                        "role": "system",
                                        "content": "You are a helpful assistant specializing in providing healthy alternatives to various dishes."
                                    },
                                    {
                                        "role": "user",
                                        "content": f"What's a healthy {top_food} recipe, and why is it healthy?"
                                    }
                                ],
                                max_tokens=200,  # Adjust as needed
                                temperature=0.7,  # Adjust creativity level as needed
                            )
                            # Corrected access to 'content'
                            result = response['choices'][0]['message']['content'].strip()
                            st.write(result)
                        except OpenAIError as e:
                            st.error(f"OpenAI API error: {e}")
                        except Exception as e:
                            st.error(f"Unable to generate healthier alternatives: {e}")
                    else:
                        st.error("No predictions returned from the classification model.")
                except Exception as e:
                    st.error(f"Error during classification: {e}")
else:
    st.info("Please select or upload an image to get started.")