demo code

.gitignore

@@ -9,3 +9,4 @@ venv/*
 *venv/*
 =0.19.0
 .streamlit/secrets.toml
+week1_image_classifier/myLearnings.md

pages/1_Image_Classifier.py

@@ -1,7 +1,6 @@
 import streamlit as st
 from PIL import Image
 import requests
-import io
 
 st.set_page_config(
     page_title="Food Classifier",
@@ -10,122 +9,46 @@ st.set_page_config(
 )
 
 st.title("Food Classification")
-st.markdown("### AI-powered food recognition")
+st.markdown("AI-powered food recognition")
 
-# Use proven working model
-API_URL = "https://api-inference.huggingface.co/models/Kaludi/food-category-classification-v2.0"
+API_URL = "https://api-inference.huggingface.co/models/nateraw/vit-base-beans"
 
-def query_model(image_bytes):
-    """Query the food classification model"""
+def classify_image(image_bytes):
     try:
         response = requests.post(API_URL, data=image_bytes, timeout=30)
-        if response.status_code == 200:
-            return response.json()
-        else:
-            return {"error": f"Status {response.status_code}"}
-    except Exception as e:
-        return {"error": str(e)}
+        return response.json()
+    except:
+        return None
 
-# Model info
-with st.expander("ℹ️ About This Service"):
-    st.markdown("""
-    **Technology**: Deep Learning Image Classification  
-    **Model**: Fine-tuned Vision Transformer  
-    **Capabilities**: Recognizes various food categories  
-    **Use Case**: Automated food recognition for nutrition tracking, restaurant apps
-    
-    ---
-    
-    **Week 1 Project**: Built complete ML training pipeline
-    - Trained custom ResNet-50 on Food-101 dataset (75K images, 101 classes)
-    - Implemented end-to-end training pipeline in Google Colab
-    - Deployed model to HuggingFace Hub
-    - Learned about model training, evaluation, and deployment
-    
-    *Note: Demo uses production model while custom model completes extended training*
-    """)
-
-# Main interface
-col1, col2 = st.columns([1, 1])
+col1, col2 = st.columns(2)
 
 with col1:
-    st.markdown("### Upload Image")
-    st.markdown("Upload a photo of any food item")
-    
-    uploaded_file = st.file_uploader(
-        "Choose an image",
-        type=['jpg', 'jpeg', 'png'],
-        help="Supported formats: JPG, JPEG, PNG"
-    )
+    st.subheader("Upload Image")
+    uploaded_file = st.file_uploader("Choose a food image", type=['jpg', 'jpeg', 'png'])
     
     if uploaded_file:
         image = Image.open(uploaded_file)
-        st.image(image, caption="Uploaded Image", use_column_width=True)
+        st.image(image, use_column_width=True)
 
 with col2:
-    st.markdown("### Classification Results")
+    st.subheader("Results")
     
     if uploaded_file:
-        with st.spinner("Analyzing food..."):
-            # Get image bytes
-            img_bytes = uploaded_file.getvalue()
-            
-            # Query model
-            results = query_model(img_bytes)
+        with st.spinner("Analyzing..."):
+            results = classify_image(uploaded_file.getvalue())
             
-            # Handle errors
-            if isinstance(results, dict) and "error" in results:
-                st.error("Model is initializing. Please wait 20 seconds and upload again.")
-                if st.button("Retry"):
-                    st.rerun()
-            
-            # Display results
-            elif isinstance(results, list) and len(results) > 0:
-                # Top prediction
-                top = results[0]
-                top_label = top['label']
-                top_score = top['score']
-                
-                st.success("✅ Classification Complete")
-                st.markdown(f"## {top_label}")
-                st.progress(top_score)
-                st.metric("Confidence", f"{top_score*100:.1f}%")
-                
-                # Show top 5
-                if len(results) > 1:
-                    st.markdown("---")
-                    st.markdown("#### Other Predictions")
+            if results and isinstance(results, list):
+                for i, result in enumerate(results[:5], 1):
+                    label = result.get('label', 'Unknown')
+                    score = result.get('score', 0)
                     
-                    for i, result in enumerate(results[1:5], 2):
-                        label = result['label']
-                        score = result['score']
-                        st.markdown(f"**{i}. {label}**")
-                        st.progress(score)
-                        st.caption(f"{score*100:.1f}%")
+                    st.write(f"**{i}. {label}**")
+                    st.progress(score)
+                    st.caption(f"{score*100:.1f}%")
             else:
-                st.warning("Unable to classify. Please try another image.")
+                st.info("Model loading. Wait 20 seconds and retry.")
     else:
-        st.info("👈 Upload a food image to get started")
+        st.info("Upload an image to classify")
 
 st.markdown("---")
-st.markdown("### 💡 Tips for Best Results")
-
-tip_col1, tip_col2 = st.columns(2)
-
-with tip_col1:
-    st.markdown("""
-    **Image Quality**
-    - Use clear, well-lit photos
-    - Ensure food is the main subject
-    - Avoid heavily filtered images
-    """)
-
-with tip_col2:
-    st.markdown("""
-    **Performance**
-    - First prediction: ~20 seconds (model loading)
-    - Subsequent predictions: 1-2 seconds
-    - Model auto-sleeps after 15 min idle
-    """)
-
-st.caption("Week 1 Complete | Built by Suchith Natraj Javali | View code on GitHub")
+st.caption("Week 1 Project - Image Classification Pipeline")