app.py

# 1. Imports and API setup
from groq import Groq
import base64
import streamlit as st
import pandas as pd

client = Groq(
    api_key="",
)

llava_model = 'llava-v1.5-7b-4096-preview'
llama31_model = 'llama-3.1-70b-versatile'

# 2. Image encoding
def encode_image(image_path):
  with open(image_path, "rb") as image_file:
    return base64.b64encode(image_file.read()).decode('utf-8')

# 3. Image to text function
def image_to_text(client, model, base64_image, prompt):
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "user",
                "content": [
                    {"type": "text", "text": prompt},
                    {
                        "type": "image_url",
                        "image_url": {
                            "url": f"data:image/jpeg;base64,{base64_image}",
                        },
                    },
                ],
            }           
        ],
        model=model
    )

    return chat_completion.choices[0].message.content

# 4. Short story generation function
def analyzer_generation(client, image_description):
    chat_completion = client.chat.completions.create(
        messages=[
            {
                "role": "system",
                "content": f"You are a food and nutrition expert, you analyze Food by Photo: The user takes a photo of a plate of food, and the app describes the ingredients, possible calories, and offers suggestions on how to make the meal healthier or more balanced. Note: Write in Portuguese.",
            },
            {
                "role": "user",
                "content": image_description,
            }
        ],
        model=llama31_model
    )
    
    return chat_completion.choices[0].message.content

# 5. Streamlit app
def main():
    st.image("images.jpg", width=200)
    st.title("FoodBot - Análisador de Alimentos",  anchor="center")
    st.write("Conheça o FoodBot, um assistente inteligente que o usuário tira uma foto de um prato de comida, e o app descreve os ingredientes, possíveis calorias, e oferece sugestões de como tornar a refeição mais saudável ou equilibrada.")    

    
    uploaded_file = st.file_uploader("Carregue uma imagem (png ou jpg)", type=["png", "jpg"])
    if uploaded_file is not None:
        # To read file as bytes:
        bytes_data = uploaded_file.read()
        base64_image = base64.b64encode(bytes_data).decode('utf-8')
        prompt = '''
        Describe this image in detail, including the appearance of the object(s).  Note: Write in Portuguese.
        '''
        image_description = image_to_text(client, llava_model, base64_image, prompt)

        st.write("\n--- Image Description ---")
        st.write(image_description)

        st.write("\n--- Análise do Alimento ---")
        food_description = analyzer_generation(client, image_description)
        st.write(food_description)

       

if __name__ == "__main__":
    main()