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magic-recipe

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Ashhar commited on 28 days ago

Commit

726017c

1 Parent(s): 4a694a1

visual improvements

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Files changed (2) hide show

app.py +209 -112
utils.py +29 -1

app.py CHANGED Viewed

@@ -1,8 +1,11 @@
 import base64
 import streamlit as st
 from openai import OpenAI
 import os
-from utils import pprint
 # Load environment variables
 from dotenv import load_dotenv
@@ -21,43 +24,53 @@ st.set_page_config(
     initial_sidebar_state="expanded"
 )
 # Custom CSS for styling
-st.markdown("""
-<style>
-.big-font {
-    font-size:20px !important;
-    color: #2C3E50;
-}
-.highlight-box {
-    background-color: #F5F5F5;  /* Soft light gray */
-    border-radius: 10px;
-    padding: 20px;
-    margin-bottom: 20px;
-    color: #333333;  /* Very dark gray for text */
-    border: 1px solid #E0E0E0;  /* Subtle border */
-}
-.highlight-box h1 {
-    color: #1A5F7A;  /* Deep teal for main heading */
-    font-size: 24px;
-    margin-bottom: 15px;
-}
-.highlight-box h2 {
-    color: #2C7DA0;  /* Slightly lighter teal for subheadings */
-    font-size: 20px;
-    margin-top: 15px;
-    margin-bottom: 10px;
-}
-.highlight-box h3 {
-    color: #468FAF;  /* Even lighter teal for smaller headings */
-    font-size: 18px;
-    margin-top: 10px;
-    margin-bottom: 8px;
-}
-.highlight-box p {
-    color: #333333;  /* Dark gray for paragraphs */
-    line-height: 1.6;
-}
-</style>
-""", unsafe_allow_html=True)
 # Initialize session state
 if "ipAddress" not in st.session_state:
@@ -84,75 +97,150 @@ def google_image_search(query):
     return "https://via.placeholder.com/300x200.png?text=" + query.replace(" ", "+")
 def analyze_and_generate_recipe(uploaded_image, available_equipment=None, language='English'):
     """Analyze food image and generate recipe in a single LLM call"""
-    with st.spinner('🍳 Analyzing your delicious image and generating recipe...'):
-        try:
-            # Prepare the system and user messages
-            messages = [
-                {
-                    "role": "system",
-                    "content": f"""You are a professional chef and food analyst.
-                    When analyzing a food image, provide a comprehensive recipe in {language} that considers:
-                    1. Detailed food description
-                    2. Complete ingredient list
-                    3. Cooking method
-                    4. Step-by-step instructions"""
-                },
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "image_url",
-                            "image_url": {"url": f"data:image/jpeg;base64,{uploaded_image}"}
-                        },
-                        {
-                            "type": "text",
-                            "text": f"""Analyze this food image and generate a detailed recipe in {language}.
-                            {'Available cooking equipment: ' + ', '.join(available_equipment) if available_equipment else 'No equipment restrictions'}
-                            If specific equipment is available, prioritize cooking methods that use those tools.
-                            Provide:
-                            - Detailed food description
-                            - Ingredient list
-                            - Cooking method adapted to available equipment
-                            - Precise, step-by-step instructions
-                            - Difficulty level
-                            - Estimated cooking time
-                            Use markdown formatting for clear presentation."""
-                        }
-                    ]
-                }
-            ]
-            # Log API call details
-            pprint({
-                "function": "analyze_and_generate_recipe",
-                "model": model,
-                "language": language,
-                "available_equipment": available_equipment
-            })
-            # Make the LLM call
-            response = client.chat.completions.create(
-                model=model,
-                messages=messages
-            )
-            # Log response details
-            pprint({
-                "function": "analyze_and_generate_recipe_response",
-                "tokens_used": response.usage.total_tokens if response.usage else None,
-                "response_length": len(response.choices[0].message.content)
-            })
-            return response.choices[0].message.content
-        except Exception as e:
-            st.error(f"Error analyzing image and generating recipe: {e}")
-            return None
 def refine_recipe(original_recipe, user_refinement, language='English'):
@@ -241,7 +329,16 @@ img_source = st.radio("Choose image source:", ["Upload from device", "Take a pho
 if img_source == "Upload from device":
     uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])
 else:
-    uploaded_file = st.camera_input("Take a photo of your dish")
 # Food Analysis and Recipe Generation
 if uploaded_file is not None:
@@ -305,12 +402,12 @@ if uploaded_file is not None:
             else:
                 st.warning("Please enter refinement preferences.")
-    # Visual References
-    st.markdown("### 🖼️ Visual References", unsafe_allow_html=True)
-    if st.session_state.original_recipe:
-        food_name = st.session_state.original_recipe.split('\n')[0]
-        image_urls = [google_image_search(food_name) for _ in range(3)]
-        cols = st.columns(3)
-        for i, url in enumerate(image_urls):
-            cols[i].image(url, use_container_width=True)

 import base64
+import io
+import time
 import streamlit as st
 from openai import OpenAI
 import os
+from PIL import Image
+from utils import pprint, getFontsUrl
 # Load environment variables
 from dotenv import load_dotenv
     initial_sidebar_state="expanded"
 )
 # Custom CSS for styling
+st.markdown(
+    f"""
+    <head>
+        <link href="{getFontsUrl()}" rel="stylesheet">
+    </head>
+    """ """
+    <style>
+    h1 {
+        font-family: 'Whisper';
+    }
+    .big-font {
+        font-size:20px !important;
+        color: #2C3E50;
+    }
+    .highlight-box {
+        background-color: #F5F5F5;  /* Soft light gray */
+        border-radius: 10px;
+        padding: 20px;
+        margin-bottom: 20px;
+        color: #333333;  /* Very dark gray for text */
+        border: 1px solid #E0E0E0;  /* Subtle border */
+    }
+    .highlight-box h1 {
+        color: #1A5F7A;  /* Deep teal for main heading */
+        font-size: 24px;
+        margin-bottom: 15px;
+    }
+    .highlight-box h2 {
+        color: #2C7DA0;  /* Slightly lighter teal for subheadings */
+        font-size: 20px;
+        margin-top: 15px;
+        margin-bottom: 10px;
+    }
+    .highlight-box h3 {
+        color: #468FAF;  /* Even lighter teal for smaller headings */
+        font-size: 18px;
+        margin-top: 10px;
+        margin-bottom: 8px;
+    }
+    .highlight-box p {
+        color: #333333;  /* Dark gray for paragraphs */
+        line-height: 1.6;
+    }
+    </style>
+    """,
+    unsafe_allow_html=True
+)
 # Initialize session state
 if "ipAddress" not in st.session_state:
     return "https://via.placeholder.com/300x200.png?text=" + query.replace(" ", "+")
+def resize_image(image_base64, max_size=1024):
+    """
+    Resize an image from base64 to max dimension of 1024 pixels while maintaining aspect ratio
+    Args:
+        image_base64 (str): Base64 encoded image
+        max_size (int): Maximum dimension for the image
+    Returns:
+        str: Resized image as base64 encoded string
+    """
+    # Decode base64 image
+    image_bytes = base64.b64decode(image_base64)
+    # Open image with Pillow
+    img = Image.open(io.BytesIO(image_bytes))
+    # Calculate resize ratio
+    width, height = img.size
+    resize_ratio = min(max_size / width, max_size / height)
+    # If image is already smaller than max_size, return original
+    if resize_ratio >= 1:
+        return image_base64
+    # Calculate new dimensions
+    new_width = int(width * resize_ratio)
+    new_height = int(height * resize_ratio)
+    # Resize image
+    resized_img = img.resize((new_width, new_height), Image.LANCZOS)
+    # Convert back to base64
+    buffered = io.BytesIO()
+    resized_img.save(buffered, format=img.format)
+    return base64.b64encode(buffered.getvalue()).decode('utf-8')
 def analyze_and_generate_recipe(uploaded_image, available_equipment=None, language='English'):
     """Analyze food image and generate recipe in a single LLM call"""
+    progress_stages = [
+        {"message": "🔍 Scanning the delicious image...", "progress": 10},
+        {"message": "🧐 Identifying culinary ingredients...", "progress": 30},
+        {"message": "🍳 Consulting virtual chef's expertise...", "progress": 50},
+        {"message": "📝 Crafting personalized recipe...", "progress": 70},
+        {"message": "🌟 Finalizing gourmet instructions...", "progress": 90}
+    ]
+    # Create a progress bar
+    progress_bar = st.progress(0)
+    status_text = st.empty()
+    try:
+        # Update progress stages
+        for stage in progress_stages:
+            status_text.text(stage["message"])
+            progress_bar.progress(stage["progress"])
+            time.sleep(1)  # Short delay between stages
+        # Resize the image before sending to LLM
+        resized_image_base64 = resize_image(uploaded_image)
+        # Prepare the system and user messages
+        messages = [
+            {
+                "role": "system",
+                "content": f"""You are a professional chef and food analyst.
+                When analyzing a food image, provide a comprehensive recipe in {language} that considers:
+                1. Detailed food description
+                2. Complete ingredient list
+                3. Cooking method
+                4. Step-by-step instructions"""
+            },
+            {
+                "role": "user",
+                "content": [
+                    {
+                        "type": "image_url",
+                        "image_url": {"url": f"data:image/jpeg;base64,{resized_image_base64}"}
+                    },
+                    {
+                        "type": "text",
+                        "text": f"""Analyze this food image and generate a detailed recipe in {language}.
+                        {'Available cooking equipment: ' + ', '.join(available_equipment) if available_equipment else 'No equipment restrictions'}
+                        If specific equipment is available, prioritize cooking methods that use those tools.
+                        Provide:
+                        - Detailed food description
+                        - Ingredient list
+                        - Cooking method adapted to available equipment
+                        - Precise, step-by-step instructions
+                        - Difficulty level
+                        - Estimated cooking time
+                        Use markdown formatting for clear presentation."""
+                    }
+                ]
+            }
+        ]
+        # Log API call details
+        pprint({
+            "function": "analyze_and_generate_recipe",
+            "model": model,
+            "language": language,
+            "available_equipment": available_equipment
+        })
+        # Make the LLM call
+        status_text.text("🚀 Generating recipe with AI...")
+        progress_bar.progress(95)
+        response = client.chat.completions.create(
+            model=model,
+            messages=messages
+        )
+        # Final progress update
+        status_text.text("✅ Recipe generated successfully!")
+        progress_bar.progress(100)
+        # Clear the progress bar and status text after a short delay
+        time.sleep(1)
+        progress_bar.empty()
+        status_text.empty()
+        # Log response details
+        pprint({
+            "function": "analyze_and_generate_recipe_response",
+            "tokens_used": response.usage.total_tokens if response.usage else None,
+            "response_length": len(response.choices[0].message.content)
+        })
+        return response.choices[0].message.content
+    except Exception as e:
+        # Clear progress indicators in case of error
+        progress_bar.empty()
+        status_text.empty()
+        st.error(f"Error analyzing image and generating recipe: {e}")
+        return None
 def refine_recipe(original_recipe, user_refinement, language='English'):
 if img_source == "Upload from device":
     uploaded_file = st.file_uploader("Choose an image...", type=['jpg', 'jpeg', 'png'])
 else:
+    uploaded_file = st.camera_input(
+        "Take a photo of your dish",
+        help="Please hold your device vertically for best results",
+        # Set aspect ratio to portrait (3:4)
+        key="portrait_camera",
+        args={
+            "landscape": False,  # Force portrait mode
+            "aspectRatio": 3 / 4   # Portrait aspect ratio
+        }
+    )
 # Food Analysis and Recipe Generation
 if uploaded_file is not None:
             else:
                 st.warning("Please enter refinement preferences.")
+    # # Visual References
+    # st.markdown("### 🖼️ Visual References", unsafe_allow_html=True)
+    # if st.session_state.original_recipe:
+    #     food_name = st.session_state.original_recipe.split('\n')[0]
+    #     image_urls = [google_image_search(food_name) for _ in range(3)]
+    #     cols = st.columns(3)
+    #     for i, url in enumerate(image_urls):
+    #         cols[i].image(url, use_container_width=True)

utils.py CHANGED Viewed

@@ -3,6 +3,25 @@ import pytz
 import streamlit as st
 def __nowInIST() -> DT.datetime:
     return DT.datetime.now(pytz.timezone("Asia/Kolkata"))
@@ -10,4 +29,13 @@ def __nowInIST() -> DT.datetime:
 def pprint(log: str):
     now = __nowInIST()
     now = now.strftime("%Y-%m-%d %H:%M:%S")
-    print(f"[{now}] [{st.session_state.ipAddress}] {log}")

 import streamlit as st
+FONTS = [
+    # "Poppins:ital,wght@0,100;0,200;0,300;0,400;0,500;0,600;0,700;0,800;0,900;1,100;1,200;1,300;1,400;1,500;1,600;1,700;1,800;1,900",
+    # "Roboto:ital,wght@0,100;0,300;0,400;0,500;0,700;0,900;1,100;1,300;1,400;1,500;1,700;1,900",
+    # "Raleway:ital,wght@0,100..900;1,100..900",
+    # "Lato:ital,wght@0,100;0,300;0,400;0,700;0,900;1,100;1,300;1,400;1,700;1,900",
+    # "Nunito:ital,wght@0,200..1000;1,200..1000",
+    # "Quicksand:[email protected]",
+    "Montserrat:ital,wght@0,100..900;1,100..900",
+    # "Edu+AU+VIC+WA+NT+Dots:[email protected]",
+    "Whisper",
+    # "Merienda:[email protected]",
+    "Playwrite+DE+Grund:[email protected]",
+    # "Roboto+Slab:[email protected]",
+    # "Open+Sans:ital,wght@0,300..800;1,300..800",
+    # "Nunito+Sans:ital,opsz,wght@0,6..12,200..1000;1,6..12,200..1000",
+    # "Ubuntu:ital,wght@0,300;0,400;0,500;0,700;1,300;1,400;1,500;1,700",
+]
 def __nowInIST() -> DT.datetime:
     return DT.datetime.now(pytz.timezone("Asia/Kolkata"))
 def pprint(log: str):
     now = __nowInIST()
     now = now.strftime("%Y-%m-%d %H:%M:%S")
+    print(f"[{now}] [{st.session_state.ipAddress}] {log}")
+def getFontsUrl():
+    baseLink = "https://fonts.googleapis.com/css2"
+    params = "&".join([f"family={font}" for font in FONTS])
+    params = f"{params}&display=swap"
+    fontsUrl = f"{baseLink}?{params}"
+    # pprint(f"{fontsUrl=}")
+    return fontsUrl