Update app.py

app.py

@@ -1,96 +1,114 @@
 import streamlit as st
-import requests
+from dotenv import load_dotenv, find_dotenv
+import os
+import google.generativeai as genai
 from PIL import Image
 
-# API configurations
-GEMINI_API_URL = "https://api.gemini.google.com/v1/recognize"
-GEMINI_API_KEY = "AIzaSyB6z5crVaKSj1ct_gVsq6nrHtSGme_JmoY"  # Replace with your actual Gemini API key
-
-DOBBY_API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
-DOBBY_API_KEY = "fw_3ZjtsywUGddwa1wGY4VvB3eW"  # Replace with your valid Dobby API key
-DOBBY_LEASHED_MODEL = "accounts/sentientfoundation/models/dobby-mini-leashed-llama-3-1-8b#accounts/sentientfoundation/deployments/22e7b3fd"
-DOBBY_UNHINGED_MODEL = "accounts/sentientfoundation/models/dobby-mini-unhinged-llama-3-1-8b#accounts/sentientfoundation/deployments/81e155fc"
+# Load environment variables from .env file
+load_dotenv(find_dotenv())
+
+# Set API Key directly (if environment variables are not configured)
+API_KEY = "AIzaSyB6z5crVaKSj1ct_gVsq6nrHtSGme_JmoY"
+genai.configure(api_key=API_KEY)
+
+# Streamlit Page Configuration
+st.set_page_config(page_title="Nutrition Monitor", page_icon="🍎")
+
+# Custom CSS for Streamlit App
+st.markdown("""
+    <style>
+    .stApp {
+        background-color: #f5f5f5;        /* Light grey background */
+        font-family: Arial, sans-serif;   /* Clean font style */
+    }
+    .stButton>button {
+        background-color: #4CAF50;       /* Green button */
+        color: white;                    /* White text */
+        font-size: 16px;                 /* Readable font size */
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Define a function for Gemini API request
+def get_gemini_response(input_prompt, image_data):
+    try:
+        # Use Gemini's generative model
+        model = genai.GenerativeModel("gemini-1.5-pro-latest")
+        # Generate response using the prompt and image
+        response = model.generate_content([input_prompt, image_data[0]])
+        return response.text
+    except Exception as e:
+        st.error(f"Error generating response: {e}")
+        return None
+
+# Prepare uploaded image for Gemini API
+def prepare_image_data(uploaded_file):
+    if uploaded_file:
+        bytes_data = uploaded_file.getvalue()
+        return [
+            {
+                "mime_type": uploaded_file.type,
+                "data": bytes_data,
+            }
+        ]
+    else:
+        raise FileNotFoundError("No image uploaded")
 
-# Streamlit app setup
-st.title("AI-Powered Food Nutrition Analyzer with Dobby")
-st.write("Upload an image of your meal, and Dobby will analyze it and provide feedback!")
+# Sidebar for image upload
+st.sidebar.title("Upload Section")
+uploaded_file = st.sidebar.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
 
-# File upload
-uploaded_file = st.file_uploader("Upload your food image", type=["jpg", "jpeg", "png"])
+# Main page header
+st.title("AI-Powered Nutrition Monitor")
+st.write("Upload an image of your meal, and AI will analyze its nutritional content!")
 
-if uploaded_file is not None:
-    # Display uploaded image
+# Display uploaded image
+if uploaded_file:
     image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_column_width=True)
 
-    # Save the image temporarily
-    with open("temp_image.jpg", "wb") as temp_file:
-        temp_file.write(uploaded_file.getbuffer())
-
-    # Prepare the image for Gemini API request
-    with open("temp_image.jpg", "rb") as img_file:
-        files = {"file": img_file}
-        headers = {"Authorization": f"Bearer {GEMINI_API_KEY}"}
-
-        # Send request to Gemini API
-        response = requests.post(GEMINI_API_URL, headers=headers, files=files)
-
-    # Process the Gemini API response
-    if response.status_code == 200:
-        data = response.json()
-        st.write("**Detected Foods and Their Nutritional Information:**")
-        food_log = []
-        for item in data.get("ingredients", []):
-            food_name = item["name"]
-            calories = item.get("calories", "N/A")
-            protein = item.get("protein", "N/A")
-            carbs = item.get("carbohydrates", "N/A")
-            fat = item.get("fat", "N/A")
-
-            food_log.append(
-                f"{food_name}: {calories} kcal, Protein: {protein} g, Carbs: {carbs} g, Fat: {fat} g"
-            )
-
-            st.write(f"**{food_name}**")
-            st.write(f"- Calories: {calories} kcal")
-            st.write(f"- Protein: {protein} g")
-            st.write(f"- Carbohydrates: {carbs} g")
-            st.write(f"- Fat: {fat} g")
-
-        # Prepare Dobby's feedback
-        tone = st.radio("Choose Dobby's tone:", ["Motivational (Leashed)", "Sarcastic (Unhinged)"])
-        selected_model = DOBBY_LEASHED_MODEL if tone == "Motivational (Leashed)" else DOBBY_UNHINGED_MODEL
-
-        dobby_prompt = f"""
-        Here is the meal log:
-        {'; '.join(food_log)}.
-        Provide feedback on this meal in a {tone.lower()} tone.
-        """
-
-        # Send request to Dobby API
-        dobby_response = requests.post(
-            DOBBY_API_URL,
-            headers={
-                "Authorization": f"Bearer {DOBBY_API_KEY}",
-                "Content-Type": "application/json",
-            },
-            json={
-                "model": selected_model,
-                "messages": [{"role": "user", "content": dobby_prompt}],
-            },
-        )
-
-        # Process Dobby API response
-        if dobby_response.status_code == 200:
-            dobby_reply = dobby_response.json().get("choices", [{}])[0].get("message", {}).get("content", "No response")
-            st.write("**Dobby says:**")
-            st.success(dobby_reply)
-        else:
-            st.error("Failed to get a response from Dobby. Please try again later.")
+# Button to analyze food
+analyze_button = st.button("Analyze Food")
+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.
+"""
+
+# Process and analyze when button is clicked
+if analyze_button:
+    if uploaded_file:
+        with st.spinner("Analyzing the image..."):
+            # Prepare image data
+            image_data = prepare_image_data(uploaded_file)
+            # Get response from Gemini API
+            response = get_gemini_response(input_prompt, image_data)
+        if response:
+            st.success("Analysis Complete!")
+            st.subheader("Food Analysis")
+            st.write(response)
     else:
-        st.error(f"Error in processing the image: {response.status_code}")
-        st.write("Response:", response.json())
-
-# Footer
-st.write("---")
-st.write("Powered by [Gemini AI](https://www.google.com/ai/) and Dobby (Fireworks AI)")
+        st.error("Please upload an image before clicking Analyze Food.")