fix: add root app.py and HF Space config (Streamlit SDK)

README.md

@@ -1,3 +1,14 @@
+---
+title: BacSense API
+emoji: 🦠
+colorFrom: blue
+colorTo: green
+sdk: streamlit
+sdk_version: "1.32.0"
+app_file: app.py
+pinned: false
+---
+
 # 🦠 Bacsense 2.0
 
 ![Bacsense Banner](https://img.shields.io/badge/Bacsense-2.0-13a4ec?style=for-the-badge)

app.py

@@ -0,0 +1,204 @@
+import streamlit as st
+from PIL import Image
+import os
+import sys
+import tempfile
+import pandas as pd
+
+# Ensure bacsense_v2_package is importable from the repo root
+sys.path.insert(0, os.path.abspath(os.path.dirname(__file__)))
+from bacsense_v2_package.inference import BacSense
+
+# Precaution dictionary
+PRECAUTIONS = {
+    "Escherichia coli": "Indicator of fecal contamination. \n\n**Precautions/Actions:** Boil water immediately before consumption. Source trace to find sewage leaks. Do not use for washing open wounds.",
+    "Pseudomonas aeruginosa": "Opportunistic pathogen resistant to many sanitizers. \n\n**Precautions/Actions:** Ensure water chlorination levels are adequate. Can cause severe infections in immunocompromised individuals. Avoid contact with eyes or ears.",
+    "Enterococcus faecalis": "Indicates prolonged fecal contamination. Very resilient. \n\n**Precautions/Actions:** Shock chlorinate the water system. Discontinue use for drinking until negative tests are returned.",
+    "Clostridium perfringens": "Spore-forming bacteria, highly resistant to standard disinfection. \n\n**Precautions/Actions:** Indicates remote or past fecal contamination. UV filtration or extreme heat treatment may be required.",
+    "Listeria monocytogenes": "Dangerous to pregnant women and immunocompromised individuals. \n\n**Precautions/Actions:** Do not use water for food preparation or drinking. Pasteurization/boiling is required."
+}
+
+# Set page config
+st.set_page_config(
+    page_title="BacSense v2 Dashboard",
+    page_icon="🦠",
+    layout="wide"
+)
+
+# Initialize classifier
+@st.cache_resource
+def get_classifier():
+    model_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), 'bacsense_v2_package'))
+    model = BacSense(model_dir=model_dir)
+    model.warmup()
+    return model
+
+try:
+    classifier = get_classifier()
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.stop()
+
+# Dialog function for detailed view
+def render_details(item):
+    st.image(item["Image"], use_column_width=True)
+    st.markdown(f"### Predicted: **{item['Predicted Class']}**")
+    
+    colA, colB = st.columns(2)
+    colA.metric("Confidence", f"{item['Confidence (%)']}%")
+    colB.metric("Risk Level", item['Risk'])
+    
+    st.markdown("""---""")
+    st.markdown("**Bacterial Summary:**")
+    st.write(f"- **Gram Stain:** {item['Gram Stain']}")
+    st.write(f"- **Shape:** {item['Shape']}")
+    
+    if item['Routed to Specialist']:
+        st.info(f"Ambiguous morphology triggered the Specialist SVM. Accepted: {'✅' if item['Specialist Accepted'] else '❌'}")
+
+    st.markdown("""---""")
+    st.markdown("**Precautions:**")
+    precaution_text = PRECAUTIONS.get(item['Predicted Class'], "No specific precautions available. Standard water safety protocols suggest boiling before consumption.")
+    st.warning(precaution_text)
+    
+    if st.button("Close Summary", key="close_summary_btn"):
+        st.session_state.selected_item = None
+        st.rerun()
+
+# Main UI
+st.title("🦠 BacSense v2 Analytics Dashboard")
+st.markdown("""
+Welcome to the BacSense v2 Dashboard. This cascaded hybrid classifier uses **VGG16 Transfer Learning** 
+combined with **Hand-Crafted Feature Engineering** and an **RBF-SVM Specialist** to disambiguate waterborne pathogens.
+You can safely upload **up to 60 images** at once.
+""")
+
+# Sidebar for info
+with st.sidebar:
+    st.header("Supported Species")
+    st.markdown("""
+    - *Clostridium perfringens*
+    - *Enterococcus faecalis*
+    - *Escherichia coli*
+    - *Listeria monocytogenes*
+    - *Pseudomonas aeruginosa*
+    """)
+    st.markdown("---")
+    st.caption("BacSense v2 Cascaded Model")
+    st.caption("Overall Accuracy: 95.65%")
+    st.caption("Specialist AUC: 0.9863")
+
+st.subheader("Batch Upload (Multiple Images)")
+uploaded_files = st.file_uploader("Upload microscopic bacterial images...", type=["jpg", "jpeg", "png"], accept_multiple_files=True)
+
+if "selected_item" not in st.session_state:
+    st.session_state.selected_item = None
+
+if uploaded_files:
+    if st.session_state.selected_item is not None:
+        render_details(st.session_state.selected_item)
+    else:
+        uploaded_files = list(uploaded_files)[:100]
+        results = []
+        
+        progress_container = st.container()
+        with progress_container:
+            st.write(f"Processing {len(uploaded_files)} images...")
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+        
+        for i, uploaded_file in enumerate(uploaded_files):
+            status_text.text(f"Analyzing [{i+1}/{len(uploaded_files)}]: {uploaded_file.name}...")
+            
+            image = Image.open(uploaded_file)
+            fd, temp_path = tempfile.mkstemp(suffix=".png")
+            if image.mode != 'RGB':
+                image = image.convert('RGB')
+            
+            with os.fdopen(fd, 'wb') as f:
+                image.save(f, format="PNG")
+            
+            try:
+                prediction = classifier.predict(temp_path)
+                confidence_pct = prediction['confidence'] * 100 if prediction['confidence'] <= 1.0 else prediction['confidence']
+                results.append({
+                    "Filename": uploaded_file.name,
+                    "Predicted Class": prediction['prediction'],
+                    "Confidence (%)": round(confidence_pct, 2),
+                    "Gram Stain": prediction.get('gram', 'Unknown'),
+                    "Shape": prediction.get('shape', 'Unknown'),
+                    "Risk": prediction.get('risk', 'Unknown'),
+                    "Routed to Specialist": prediction.get('routed_to_specialist', False),
+                    "Specialist Accepted": prediction.get('specialist_accepted', False),
+                    "Image": image
+                })
+            except Exception as e:
+                st.error(f"Error processing {uploaded_file.name}: {e}")
+            finally:
+                if os.path.exists(temp_path):
+                    os.remove(temp_path)
+            
+            progress_bar.progress((i + 1) / len(uploaded_files))
+        
+        status_text.text("Batch Processing Complete!")
+        
+        if results:
+            df = pd.DataFrame(results)
+            
+            st.markdown("---")
+            st.subheader("📊 Batch Analytics Summary")
+            col1, col2, col3, col4 = st.columns(4)
+            
+            total_images = len(results)
+            high_risk = len(df[df["Risk"] == "High"])
+            routed_spec = len(df[df["Routed to Specialist"] == True])
+            avg_confidence = df["Confidence (%)"].mean()
+            
+            col1.metric("Total Images", total_images)
+            col2.metric("High Target Risk", high_risk)
+            col3.metric("Routed to Specialist", routed_spec, help="Ambiguous cases handled by the 683-dim Specialist SVM")
+            col4.metric("Avg Confidence", f"{avg_confidence:.1f}%")
+
+            st.markdown("<br>", unsafe_allow_html=True)
+            col_chart1, col_chart2 = st.columns(2)
+            with col_chart1:
+                st.markdown("**Species Distribution**")
+                class_counts = df["Predicted Class"].value_counts().reset_index()
+                class_counts.columns = ["Species", "Count"]
+                st.bar_chart(class_counts.set_index("Species"))
+                
+            with col_chart2:
+                st.markdown("**Gram Stain Breakdown**")
+                gram_counts = df["Gram Stain"].value_counts()
+                st.bar_chart(gram_counts)
+
+            st.markdown("---")
+            st.subheader("📋 Detailed Results Table")
+            st.dataframe(df.drop(columns=["Image"]), use_container_width=True)
+            
+            st.markdown("---")
+            st.subheader("🖼️ Processed Image Gallery")
+            st.caption("Click on 'View Summary' underneath any image to view brief details and precautions for the detected pathogen.")
+            
+            filter_class = st.selectbox("Filter gallery by predicted species:", ["All"] + sorted(df["Predicted Class"].unique().tolist()))
+            filtered_results = results if filter_class == "All" else [r for r in results if r["Predicted Class"] == filter_class]
+            
+            cols_per_row = 4
+            for i in range(0, len(filtered_results), cols_per_row):
+                cols = st.columns(cols_per_row)
+                for j in range(cols_per_row):
+                    if i + j < len(filtered_results):
+                        item = filtered_results[i + j]
+                        with cols[j]:
+                            st.image(item["Image"], use_column_width=True)
+                            st.markdown(f"**{item['Predicted Class']}**")
+                            det_col1, det_col2 = st.columns(2)
+                            det_col1.markdown(f"<small>{item['Confidence (%)']}% Conf</small>", unsafe_allow_html=True)
+                            if item["Routed to Specialist"]:
+                                det_col2.markdown(f"<small>Specialist: {'✅' if item['Specialist Accepted'] else '❌'}</small>", unsafe_allow_html=True)
+                            if st.button("View Summary", key=f"details_btn_{i}_{j}"):
+                                st.session_state.selected_item = item
+                                st.rerun()
+
+else:
+    st.info("Please upload one or more images (up to 60) to start the batch analysis.")

requirements.txt

@@ -5,6 +5,8 @@ opencv-python-headless>=4.8.0
 numpy>=1.24.0
 Pillow>=9.5.0
 scipy>=1.11.0
+streamlit>=1.32.0
+pandas>=2.0.0
 fastapi
 uvicorn
 python-multipart