done

app.py

@@ -3,22 +3,96 @@ from transformers import pipeline
 from PIL import Image
 from huggingface_hub import InferenceClient
 import os
-import openai  # Added import
-from openai.error import OpenAIError  # For specific exception handling
+import openai
+from openai.error import OpenAIError
 
 # Set page configuration
 st.set_page_config(
     page_title="Plate Mate - Your Culinary Assistant",
     page_icon="🍽️",
-    layout="centered",
-    initial_sidebar_state="expanded",
+    layout="centered",  # center content for better mobile experience
+    initial_sidebar_state="collapsed",
 )
 
 def local_css():
     st.markdown(
         """
         <style>
-        /* Your existing CSS styles here */
+        /* General resets */
+        body, html {
+            margin: 0;
+            padding: 0;
+            font-family: "Helvetica Neue", Arial, sans-serif;
+            background-color: #f9f9f9;
+        }
+
+        /* Container and spacing */
+        .css-1aumxhk, .css-keje6w, .css-18e3th9, .css-12oz5g7 {
+            padding-left: 0 !important;
+            padding-right: 0 !important;
+        }
+
+        /* Title styling */
+        .title h1 {
+            text-align: center;
+            font-size: 2.5em;
+            margin-bottom: 0.5em;
+            color: #333;
+        }
+
+        /* Subheader styling */
+        h2, h3, h4, h5, h6 {
+            color: #555;
+            margin-bottom: 0.5em;
+        }
+        
+        /* Adjust image styling */
+        img {
+            max-width: 100%;
+            height: auto;
+            border-radius: 8px;
+        }
+
+        /* On mobile, reduce font sizes and margins */
+        @media (max-width: 600px) {
+            .title h1 {
+                font-size: 1.8em;
+            }
+
+            h2, h3, h4 {
+                font-size: 1em;
+            }
+
+            .stButton button {
+                width: 100%;
+            }
+        }
+
+        /* Sidebar adjustments */
+        [data-testid="stSidebar"] {
+            width: 250px;
+            background: #fff;
+        }
+
+        /* Preset images container */
+        .preset-container {
+            display: flex;
+            flex-wrap: wrap;
+            gap: 10px;
+            justify-content: center;
+            margin: 1em 0;
+        }
+        .preset-container img {
+            width: 80px;
+            height: 80px;
+            object-fit: cover;
+            cursor: pointer;
+            border: 2px solid transparent;
+        }
+        .preset-container img:hover {
+            border: 2px solid #007BFF;
+        }
+
         </style>
         """, unsafe_allow_html=True
     )
@@ -27,21 +101,15 @@ local_css()  # Apply the CSS
 
 # Hugging Face API key
 API_KEY = st.secrets["HF_API_KEY"]
-
-# Initialize the Hugging Face Inference Client
 client = InferenceClient(api_key=API_KEY)
 
-# Load the image classification pipeline
 @st.cache_resource
 def load_image_classification_pipeline():
-    """ Load the image classification pipeline using a pretrained model. """
     return pipeline("image-classification", model="Shresthadev403/food-image-classification")
 
 pipe_classification = load_image_classification_pipeline()
 
-# Function to generate ingredients using Hugging Face Inference Client
 def get_ingredients_qwen(food_name):
-    """ Generate a list of ingredients for the given food item using Qwen NLP model. Returns a clean, comma-separated list of ingredients. """
     messages = [
         {
             "role": "user",
@@ -58,61 +126,69 @@ def get_ingredients_qwen(food_name):
     except Exception as e:
         return f"Error generating ingredients: {e}"
 
-# **Set OpenAI API Key**
-openai.api_key = st.secrets["openai"]  # Ensure you have this in your secrets
+openai.api_key = st.secrets["openai"]
 
-# Main content
 st.markdown('<div class="title"><h1>PlateMate - Your Culinary Assistant</h1></div>', unsafe_allow_html=True)
 
-# Add banner image with existence check
+# Banner Image (Smaller or optional)
 banner_image_path = "IR_IMAGE.png"
 if os.path.exists(banner_image_path):
-    st.image(banner_image_path, use_container_width=True)
+    # Display a smaller version of the banner
+    col1, col2, col3 = st.columns([1,3,1])
+    with col2:
+        st.image(banner_image_path, use_container_width=True)
 else:
     st.warning(f"Banner image '{banner_image_path}' not found.")
 
-# Sidebar for model information (hidden on small screens)
+# Sidebar Info
 with st.sidebar:
     st.title("Model Information")
-    st.write("**Image Classification Model**")
+    st.write("**Image Classification Model:**")
     st.write("Shresthadev403/food-image-classification")
-    st.write("**LLM for Ingredients**")
+    st.write("**LLM for Ingredients:**")
     st.write("Qwen/Qwen2.5-Coder-32B-Instruct")
     st.markdown("---")
     st.markdown("<p style='text-align: center;'>Developed by Muhammad Hassan Butt.</p>", unsafe_allow_html=True)
 
-
-# File uploader
 st.subheader("Upload a food image:")
-uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])
+
+# Preset Images
+preset_images = {
+    "Pizza": "sample_pizza.jpg",
+    "Salad": "sample_salad.jpg",
+    "Sushi": "sample_sushi.jpg"
+}
+
+selected_preset = st.selectbox("Or choose a preset sample image:", ["None"] + list(preset_images.keys()))
+if selected_preset != "None":
+    uploaded_file = preset_images[selected_preset]
+else:
+    uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])
 
 if uploaded_file is not None:
-    # Display the uploaded image
-    if isinstance(uploaded_file, str):  # Sample image selected
+    if isinstance(uploaded_file, str):
+        # Use the preset image
         if os.path.exists(uploaded_file):
             image = Image.open(uploaded_file)
         else:
             st.error(f"Sample image '{uploaded_file}' not found.")
             image = None
-    else:  # User uploaded image
+    else:
         image = Image.open(uploaded_file)
 
     if image:
-        st.image(image, caption="Uploaded Image", use_container_width=True)
+        st.image(image, caption="Selected Image", use_container_width=True)
 
-        # Classification button
         if st.button("Classify"):
             with st.spinner("Classifying..."):
                 try:
-                    # Make predictions
                     predictions = pipe_classification(image)
                     if predictions:
-                        # Display only the top prediction
                         top_food = predictions[0]['label']
                         confidence = predictions[0]['score']
                         st.header(f"🍽️ Food: {top_food} ({confidence*100:.2f}% confidence)")
 
-                        # Generate and display ingredients for the top prediction
+                        # Generate ingredients
                         st.subheader("📝 Ingredients")
                         try:
                             ingredients = get_ingredients_qwen(top_food)
@@ -120,11 +196,11 @@ if uploaded_file is not None:
                         except Exception as e:
                             st.error(f"Error generating ingredients: {e}")
 
-                        # **Healthier Alternatives using OpenAI API**
+                        # Healthier Alternatives
                         st.subheader("💡 Healthier Alternatives")
                         try:
                             response = openai.ChatCompletion.create(
-                                model="gpt-4",  # You can choose the model you prefer
+                                model="gpt-4",
                                 messages=[
                                     {
                                         "role": "system",
@@ -135,10 +211,9 @@ if uploaded_file is not None:
                                         "content": f"What's a healthy {top_food} recipe, and why is it healthy?"
                                     }
                                 ],
-                                max_tokens=200,  # Adjust as needed
-                                temperature=0.7,  # Adjust creativity level as needed
+                                max_tokens=200,
+                                temperature=0.7,
                             )
-                            # Corrected access to 'content'
                             result = response['choices'][0]['message']['content'].strip()
                             st.write(result)
                         except OpenAIError as e:
@@ -149,5 +224,6 @@ if uploaded_file is not None:
                         st.error("No predictions returned from the classification model.")
                 except Exception as e:
                     st.error(f"Error during classification: {e}")
+
 else:
     st.info("Please select or upload an image to get started.")