Im strugglign

app.py

@@ -19,9 +19,8 @@ def local_css():
         """
         <style>
         /* Main layout */
-        .main {
-            background-color: #f0f2f6;
-        }
+        .main { background-color: #f0f2f6; }
+
         /* Title styling */
         .title h1 {
             font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
@@ -30,22 +29,21 @@ def local_css():
             font-size: 3rem;
             margin-bottom: 20px;
         }
+
         /* Image styling */
         .st-image img {
             border-radius: 15px;
             margin-bottom: 20px;
             max-width: 100%;
         }
+
         /* Sidebar styling */
         [data-testid="stSidebar"] {
             background-color: #ff4b4b;
         }
-        [data-testid="stSidebar"] .css-ng1t4o {
-            color: white;
-        }
-        [data-testid="stSidebar"] .css-1d391kg {
-            color: white;
-        }
+        [data-testid="stSidebar"] .css-ng1t4o { color: white; }
+        [data-testid="stSidebar"] .css-1d391kg { color: white; }
+
         /* File uploader styling */
         .stFileUploader {
             border: 2px dashed #ff4b4b;
@@ -56,10 +54,12 @@ def local_css():
             background-color: #ffffff;
             font-weight: bold;
         }
+
         /* File uploader hover effect */
         .stFileUploader:hover {
             background-color: #ffe5e5;
         }
+
         /* Button styling */
         .stButton>button {
             background-color: #ff4b4b;
@@ -75,40 +75,25 @@ def local_css():
             background-color: #e04343;
             color: white;
         }
+
         /* Headers styling */
-        h2 {
-            color: #ff4b4b;
-            margin-top: 30px;
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
-        h3 {
-            color: #ff4b4b;
-            font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
-        }
+        h2 { color: #ff4b4b; margin-top: 30px; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
+        h3 { color: #ff4b4b; font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; }
+
         /* Text styling */
-        .stMarkdown p {
-            font-size: 1.1rem;
-        }
+        .stMarkdown p { font-size: 1.1rem; }
+
         /* Footer styling */
-        footer {
-            visibility: hidden;
-        }
+        footer { visibility: hidden; }
+
         /* Hide sidebar on small screens */
         @media only screen and (max-width: 600px) {
-            [data-testid="stSidebar"] {
-                display: none;
-            }
-            .main .block-container {
-                padding-left: 1rem;
-                padding-right: 1rem;
-            }
-            .title h1 {
-                font-size: 2rem;
-            }
-            .stButton>button {
-                width: 100%;
-            }
+            [data-testid="stSidebar"] { display: none; }
+            .main .block-container { padding-left: 1rem; padding-right: 1rem; }
+            .title h1 { font-size: 2rem; }
+            .stButton>button { width: 100%; }
         }
+
         /* Sample images grid */
         .sample-images {
             display: flex;
@@ -128,8 +113,7 @@ def local_css():
             border: 2px solid #ff4b4b;
         }
         </style>
-        """,
-        unsafe_allow_html=True
+        """, unsafe_allow_html=True
     )
 
 local_css()
@@ -143,19 +127,14 @@ 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.
-    """
+    """ 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.
-    """
+    """ 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",
@@ -165,9 +144,7 @@ def get_ingredients_qwen(food_name):
     ]
     try:
         completion = client.chat.completions.create(
-            model="Qwen/Qwen2.5-Coder-32B-Instruct",
-            messages=messages,
-            max_tokens=50
+            model="Qwen/Qwen2.5-Coder-32B-Instruct", messages=messages, max_tokens=50
         )
         generated_text = completion.choices[0].message["content"].strip()
         return generated_text
@@ -198,7 +175,6 @@ sample_images = {
     "Sushi": "sample_images/sushi.jpg",
     "Salad": "sample_images/salad.jpg"
 }
-
 cols = st.columns(len(sample_images))
 for idx, (name, file_path) in enumerate(sample_images.items()):
     with cols[idx]:
@@ -208,14 +184,11 @@ for idx, (name, file_path) in enumerate(sample_images.items()):
 # File uploader
 st.subheader("Upload a food image:")
 uploaded_file = st.file_uploader("", type=["jpg", "png", "jpeg"])
-
 if 'uploaded_file' in locals() and uploaded_file is not None:
     # Display the uploaded image
-    if isinstance(uploaded_file, str):
-        # Sample image selected
+    if isinstance(uploaded_file, str):  # Sample image selected
         image = Image.open(uploaded_file)
-    else:
-        # User uploaded image
+    else:  # User uploaded image
         image = Image.open(uploaded_file)
     st.image(image, caption="Uploaded Image", use_container_width=True)
 
@@ -224,11 +197,10 @@ if 'uploaded_file' in locals() and uploaded_file is not None:
         with st.spinner("Classifying..."):
             # Make predictions
             predictions = pipe_classification(image)
-
             # Display only the top prediction
             top_food = predictions[0]['label']
             st.header(f"🍽️ Food: {top_food}")
-
+            
             # Generate and display ingredients for the top prediction
             st.subheader("📝 Ingredients")
             try:
@@ -241,12 +213,10 @@ if 'uploaded_file' in locals() and uploaded_file is not None:
             try:
                 client_gradio = Client("https://8a56cb969da1f9d721.gradio.live/")
                 result = client_gradio.predict(
-                    query=f"What's a healthy {top_food} recipe, and why is it healthy?",
-                    api_name="/get_response"
+                    query=f"What's a healthy {top_food} recipe, and why is it healthy?", api_name="/get_response"
                 )
                 st.write(result)
             except Exception as e:
                 st.error(f"Unable to contact RAG: {e}")
 else:
     st.info("Please select or upload an image to get started.")
-