Update app.py

app.py

@@ -1,15 +1,13 @@
 import streamlit as st
-import requests
-import json
-from PIL import Image
 import google.generativeai as genai
+from PIL import Image
 
-# API configurations
-GENAI_API_KEY = "AIzaSyB6z5crVaKSj1ct_gVsq6nrHtSGme_JmoY"  # Replace with your Gemini API key
-genai.configure(api_key=GENAI_API_KEY)
+# Configure Google Generative AI library with an API key
+genai.configure(api_key="AIzaSyB6z5crVaKSj1ct_gVsq6nrHtSGme_JmoY")
 
+# API configurations for Dobby
 DOBBY_API_URL = "https://api.fireworks.ai/inference/v1/chat/completions"
-DOBBY_API_KEY = "fw_3ZjtsywUGddwa1wGY4VvB3eW"  # Replace with your Dobby API key
+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"
 
@@ -22,28 +20,21 @@ uploaded_file = st.file_uploader("Upload your food image", type=["jpg", "jpeg",
 tone = st.radio("Select Dobby's tone:", ["Motivational (Leashed)", "Sarcastic (Unhinged)"])
 
 # Function to analyze food image using Gemini API
-def analyze_food(image_data):
-    prompt = """
-    You are an expert nutritionist analyzing the food items in the image.
-    Start by naming the meal based on the image, identify all ingredients, and estimate calories for each ingredient.
-    Summarize the total calories, protein, carbs, and fat. Provide data in JSON format:
-    {
-        "meal_name": "Meal Name",
-        "total_calories": X,
-        "total_protein": X,
-        "total_carbs": X,
-        "total_fat": X
-    }
-    """
+def analyze_food_with_gemini(input_prompt, image_data):
     model = genai.GenerativeModel("gemini-1.5-pro-latest")
-    response = model.generate_content([prompt, image_data[0]])
+    response = model.generate_content([input_prompt, image_data[0]])
     return response.text
 
+# Function to prepare image data for Gemini API
+def prepare_image_data(uploaded_file):
+    bytes_data = uploaded_file.getvalue()
+    return [{"mime_type": uploaded_file.type, "data": bytes_data}]
+
 # Function to get Dobby's comment
 def get_dobby_comment(food_name, total_calories, total_protein, total_carbs, total_fat, tone):
     selected_model = DOBBY_LEASHED_MODEL if tone == "Motivational (Leashed)" else DOBBY_UNHINGED_MODEL
     dobby_prompt = f"""
-    The user had a meal: {food_name}. 
+    The user had a meal: {food_name}.
     Nutritional data: {total_calories} kcal, {total_protein} g protein, {total_carbs} g carbs, {total_fat} g fat.
     Comment on this meal in a {tone.lower()} tone.
     """
@@ -62,39 +53,40 @@ def get_dobby_comment(food_name, total_calories, total_protein, total_carbs, tot
 
 # Main app logic
 if uploaded_file:
-    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)  # Display uploaded image
+    st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)  # Display uploaded image
     with st.spinner("Analyzing the image..."):
         try:
-            # Prepare image data
-            bytes_data = uploaded_file.getvalue()
-            image_data = [{"mime_type": uploaded_file.type, "data": bytes_data}]
-            # Get nutritional analysis from Gemini
-            analysis = analyze_food(image_data)
-            analysis_data = json.loads(analysis)  # Parse JSON response safely
+            # Prepare image data for Gemini API
+            image_data = prepare_image_data(uploaded_file)
+            input_prompt = """
+            You are an expert nutritionist analyzing the food items in the image.
+            Start by naming the meal based on the image, identify all ingredients, and estimate calories for each ingredient.
+            Summarize the total calories, protein, carbs, and fat. Provide data in this format:
+            Meal Name: [Name of the meal]
+            Total Calories: X kcal
+            Protein: X g
+            Carbs: X g
+            Fat: X g
+            """
+            # Get food analysis from Gemini API
+            analysis = analyze_food_with_gemini(input_prompt, image_data)
 
-            # Extract data for display
-            food_name = analysis_data["meal_name"]
-            total_calories = analysis_data["total_calories"]
-            total_protein = analysis_data["total_protein"]
-            total_carbs = analysis_data["total_carbs"]
-            total_fat = analysis_data["total_fat"]
+            # Extract analysis results from Gemini response
+            st.subheader("Results:")
+            st.write(analysis)
 
-            # Get Dobby's comment
-            dobby_comment = get_dobby_comment(
-                food_name, total_calories, total_protein, total_carbs, total_fat, tone
-            )
+            # Parse relevant data for Dobby
+            lines = analysis.split("\n")
+            food_name = lines[0].replace("Meal Name: ", "").strip()
+            total_calories = lines[1].replace("Total Calories: ", "").replace("kcal", "").strip()
+            total_protein = lines[2].replace("Protein: ", "").replace("g", "").strip()
+            total_carbs = lines[3].replace("Carbs: ", "").replace("g", "").strip()
+            total_fat = lines[4].replace("Fat: ", "").replace("g", "").strip()
+
+            # Get Dobby's feedback
+            dobby_comment = get_dobby_comment(food_name, total_calories, total_protein, total_carbs, total_fat, tone)
 
-            # Display results
-            st.success("Analysis Complete!")
-            st.subheader("Results:")
-            st.write(f"**Food Name:** {food_name}")
-            st.write(f"**Total Calories:** {total_calories} kcal")
-            st.write(f"**Protein:** {total_protein} g")
-            st.write(f"**Carbohydrates:** {total_carbs} g")
-            st.write(f"**Fat:** {total_fat} g")
             st.subheader("Dobby's Comment:")
             st.write(dobby_comment)
-        except json.JSONDecodeError:
-            st.error("Error parsing Gemini API response. Ensure the response is valid JSON.")
         except Exception as e:
             st.error(f"Error processing the image: {e}")