Update app.py

app.py

@@ -8,87 +8,149 @@ 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 valid Dobby API key
+DOBBY_API_KEY = "fw_3ZjtsywUGddwa1wGY4VvB3eW"
 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"
 
-# Streamlit app setup
-st.title("AI-Powered Nutrition Monitor with Dobby")
-st.write("Upload an image of your meal, and Dobby will analyze and comment on it!")
-
-# File upload
-uploaded_file = st.file_uploader("Upload your food image", type=["jpg", "jpeg", "png"])
-tone = st.radio("Select Dobby's tone:", ["Motivational (Leashed)", "Sarcastic (Unhinged)"])
-
-# Function to analyze food image using Gemini API
-def analyze_food_with_gemini(input_prompt, image_data):
-    model = genai.GenerativeModel("gemini-1.5-pro-latest")
-    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}.
-    Nutritional data: {total_calories} kcal, {total_protein} g protein, {total_carbs} g carbs, {total_fat} g fat.
-    Comment on this meal.
-    """
-    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}],
-        },
-    )
-    return response.json().get("choices", [{}])[0].get("message", {}).get("content", "No response")
-
-# Main app logic
-if uploaded_file:
-    st.image(uploaded_file, caption="Uploaded Image", use_container_width=True)  # Display uploaded image
-    with st.spinner("Analyzing the image..."):
-        try:
-            # 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
-            say nothing else
-            """
-            # Get food analysis from Gemini API
-            analysis = analyze_food_with_gemini(input_prompt, image_data)
-
-            # Extract analysis results from Gemini response
-            #st.subheader("Results:")
-            st.write(analysis)
-
-            # 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)
-
-            st.subheader("Dobby's Comment:")
-            st.write(dobby_comment)
-        except Exception as e:
-            st.error(f"Error processing the image: {e}")
+# Initialize session state for tracking meals
+if "meal_data" not in st.session_state:
+    st.session_state.meal_data = {"Breakfast": {}, "Lunch": {}, "Dinner": {}}
+if "daily_totals" not in st.session_state:
+    st.session_state.daily_totals = {"Calories": 0, "Protein": 0, "Carbs": 0, "Fat": 0}
+
+# App setup
+st.title("AI-Powered Nutrition Tracker with Dobby")
+st.write("Track your meals and get daily feedback based on your nutrition goals!")
+
+# Step 1: User inputs weight and goal
+if "user_info" not in st.session_state:
+    st.subheader("Step 1: Set Your Goal")
+    weight = st.number_input("Enter your weight (kg):", min_value=30, max_value=300, step=1)
+    goal = st.selectbox("Select your goal:", ["Lose Weight", "Maintain Weight", "Gain Weight"])
+    if st.button("Submit"):
+        st.session_state.user_info = {"Weight": weight, "Goal": goal}
+        st.success("Your information has been saved! Proceed to meal tracking.")
+
+# Step 2: Meal tracking
+if "user_info" in st.session_state:
+    meal = st.selectbox("Select Meal:", ["Breakfast", "Lunch", "Dinner"])
+    uploaded_file = st.file_uploader(f"Upload your {meal} image", type=["jpg", "jpeg", "png"])
+    tone = st.radio("Select Dobby's tone:", ["Motivational (Leashed)", "Sarcastic (Unhinged)"])
+
+    # Functions for analysis and comments
+    def analyze_food_with_gemini(input_prompt, image_data):
+        model = genai.GenerativeModel("gemini-1.5-pro-latest")
+        response = model.generate_content([input_prompt, image_data[0]])
+        return response.text
+
+    def prepare_image_data(uploaded_file):
+        bytes_data = uploaded_file.getvalue()
+        return [{"mime_type": uploaded_file.type, "data": bytes_data}]
+
+    def get_dobby_comment(food_name, total_calories, total_protein, total_carbs, total_fat, meal, goal):
+        selected_model = DOBBY_LEASHED_MODEL if tone == "Motivational (Leashed)" else DOBBY_UNHINGED_MODEL
+        dobby_prompt = f"""
+        The user had {meal}: {food_name}.
+        Nutritional data: {total_calories} kcal, {total_protein} g protein, {total_carbs} g carbs, {total_fat} g fat.
+        The user's goal is to {goal.lower()}. Provide feedback for this meal.
+        """
+        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}],
+            },
+        )
+        return response.json().get("choices", [{}])[0].get("message", {}).get("content", "No response")
+
+    # Process uploaded meal image
+    if uploaded_file:
+        st.image(uploaded_file, caption=f"Uploaded {meal} Image", use_container_width=True)
+        with st.spinner("Analyzing the image..."):
+            try:
+                # Prepare image data and analyze with Gemini
+                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
+                say nothing else
+                """
+                analysis = analyze_food_with_gemini(input_prompt, image_data)
+
+                # Extract analysis data
+                lines = analysis.split("\n")
+                food_name = lines[0].replace("Meal Name: ", "").strip()
+                total_calories = int(lines[1].replace("Total Calories: ", "").replace("kcal", "").strip())
+                total_protein = int(lines[2].replace("Protein: ", "").replace("g", "").strip())
+                total_carbs = int(lines[3].replace("Carbs: ", "").replace("g", "").strip())
+                total_fat = int(lines[4].replace("Fat: ", "").replace("g", "").strip())
+
+                # Save data for this meal
+                st.session_state.meal_data[meal] = {
+                    "Food Name": food_name,
+                    "Calories": total_calories,
+                    "Protein": total_protein,
+                    "Carbs": total_carbs,
+                    "Fat": total_fat,
+                }
+
+                # Update daily totals
+                st.session_state.daily_totals["Calories"] += total_calories
+                st.session_state.daily_totals["Protein"] += total_protein
+                st.session_state.daily_totals["Carbs"] += total_carbs
+                st.session_state.daily_totals["Fat"] += total_fat
+
+                # Get Dobby's feedback
+                dobby_comment = get_dobby_comment(
+                    food_name, total_calories, total_protein, total_carbs, total_fat, meal, st.session_state.user_info["Goal"]
+                )
+
+                # Display results
+                st.success(f"{meal} Analysis Complete!")
+                st.write(f"**Food Name:** {food_name}")
+                st.write(f"**Calories:** {total_calories} kcal")
+                st.write(f"**Protein:** {total_protein} g")
+                st.write(f"**Carbs:** {total_carbs} g")
+                st.write(f"**Fat:** {total_fat} g")
+                st.subheader(f"Dobby's {meal} Comment:")
+                st.write(dobby_comment)
+
+            except Exception as e:
+                st.error(f"Error processing the image: {e}")
+
+    # Step 3: Daily Summary
+    if all(st.session_state.meal_data[meal] for meal in ["Breakfast", "Lunch", "Dinner"]):
+        st.subheader("Daily Summary")
+        st.write(f"**Total Calories:** {st.session_state.daily_totals['Calories']} kcal")
+        st.write(f"**Total Protein:** {st.session_state.daily_totals['Protein']} g")
+        st.write(f"**Total Carbs:** {st.session_state.daily_totals['Carbs']} g")
+        st.write(f"**Total Fat:** {st.session_state.daily_totals['Fat']} g")
+
+        # Final Dobby comment
+        final_prompt = f"""
+        The user had the following meals today:
+        Breakfast: {st.session_state.meal_data['Breakfast']['Food Name']}
+        Lunch: {st.session_state.meal_data['Lunch']['Food Name']}
+        Dinner: {st.session_state.meal_data['Dinner']['Food Name']}
+        Total daily intake: {st.session_state.daily_totals['Calories']} kcal, 
+        {st.session_state.daily_totals['Protein']} g protein, {st.session_state.daily_totals['Carbs']} g carbs, 
+        {st.session_state.daily_totals['Fat']} g fat.
+        The user's goal is to {st.session_state.user_info['Goal'].lower()}. Provide a summary and feedback for the day.
+        """
+        final_comment = get_dobby_comment(
+            "", st.session_state.daily_totals["Calories"], st.session_state.daily_totals["Protein"],
+            st.session_state.daily_totals["Carbs"], st.session_state.daily_totals["Fat"], "Daily Summary",
+            st.session_state.user_info["Goal"]
+        )
+        st.subheader("Dobby's Daily Summary Comment:")
+        st.write(final_comment)