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app.py

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+# Import necessary libraries
+import streamlit as st
+from dotenv import load_dotenv, find_dotenv
+import os
+from PIL import Image
+import openai
+import base64
+from io import BytesIO
+
+# Load environment variables from the .env file
+load_dotenv(find_dotenv())
+
+# Configure Streamlit page settings
+st.set_page_config(page_title="Nutrition Assistant", page_icon="🔮")
+
+# Configure Sambanova API with an API key from environment variables
+openai.api_key = os.getenv("SAMBANOVA_API_KEY")
+openai.api_base = "https://api.sambanova.ai/v1"
+
+# Define a function to encode an image to base64 format
+def image_to_base64(image):
+    buffered = BytesIO()
+    image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode("utf-8")
+
+# Define a function to handle the response from the Sambanova Llama model
+def get_llama_response(input, image):
+    # Convert image to base64 format
+    image_base64 = image_to_base64(image)
+    
+    # Prepend the base64 data with the appropriate format
+    image_data_url = f"data:image/png;base64,{image_base64}"
+    
+    try:
+        # Send input and image data to the model and get textual response
+        response = openai.ChatCompletion.create(
+            model="Llama-3.2-90B-Vision-Instruct",
+            messages=[{
+                "role": "user", 
+                "content": [
+                    {"type": "text", "text": input}, 
+                    {"type": "image_url", "image_url": {"url": image_data_url}}
+                ]
+            }],
+            temperature=0.1,
+            top_p=0.1
+        )
+        
+        # Print the entire response to debug
+        print(response)  # For debugging purposes, this will print to your server's log
+        
+        # Check if the response contains 'choices'
+        if 'choices' in response:
+            return response['choices'][0]['message']['content']
+        else:
+            # Handle the case where 'choices' is missing
+            return f"Error: Response structure is missing 'choices'. Full response: {response}"
+    
+    except Exception as e:
+        # Catch any exceptions (e.g., network issues, invalid API key, etc.)
+        return f"An error occurred: {str(e)}"
+
+# Define a function to set up image uploading and handle the image data
+def input_image_setup(uploaded_file):
+    if uploaded_file:
+        return Image.open(uploaded_file)
+    else:
+        raise FileNotFoundError("No image uploaded")
+
+# Sidebar configuration for navigation and file upload
+st.sidebar.title("Navigation")
+st.sidebar.header("Upload Section")
+uploaded_file = st.sidebar.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Display the main header of the application
+st.header("Nutrition Assistant")
+if uploaded_file:
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_container_width=True)
+
+# Create a button for triggering the food analysis
+submit = st.button("Analyze this Food")
+
+# Set the prompt for the AI model
+input_prompt = """
+You are an expert nutritionist analyzing the food items in the image.
+Start by determining if the image contains food items. 
+If the image does not contain any food items, 
+clearly state "No food items detected in the image." 
+and do not provide any calorie information. 
+If food items are detected, 
+start by naming the meal based on the image, 
+identify and list every ingredient you can find in the image, 
+and then estimate the total calories for each ingredient. 
+Summarize the total calories based on the identified ingredients. 
+Follow the format below:
+
+If no food items are detected:
+No food items detected in the image.
+
+If food items are detected:
+Meal Name: [Name of the meal]
+
+1. Ingredient 1 - estimated calories
+2. Ingredient 2 - estimated calories
+----
+Total estimated calories: X
+
+Finally, mention whether the food is healthy or not, 
+and provide the percentage split of protein, carbs, and fats in the food item. 
+Also, mention the total fiber content in the food item and any other important details.
+"""
+
+# Action to take when the 'Analyze this Food' button is clicked
+if submit:
+    if uploaded_file:
+        with st.spinner("Processing..."):
+            try:
+                image_data = input_image_setup(uploaded_file)
+                response = get_llama_response(input_prompt, image_data)
+                st.success("Analysis Complete!")
+                st.subheader("Food Analysis")
+                st.write(response)
+            except Exception as e:
+                st.error(f"Error: {str(e)}")
+    else:
+        st.warning("Please upload an image first.")

requirements.txt

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+streamlit==1.19.0
+python-dotenv==1.0.0
+Pillow==8.4.0
+openai==0.27.0