Fix deployment: add startup script, env config, routing fixes, and reorganize classifier service

DEPLOYMENT.md

@@ -0,0 +1,110 @@
+# 🦠 BacSense v2 - Hugging Face Deployment Guide
+
+## Current Status
+- **Frontend**: Deployed on Vercel ✅
+- **Backend**: Deployed on Hugging Face Spaces (Docker) ⚠️
+
+## Issue Diagnosis
+
+The "Not Found" error on Hugging Face typically means:
+1. The server isn't starting properly
+2. The port configuration is incorrect
+3. Model files aren't being found during initialization
+
+## Solution Implemented
+
+### 1. Updated Dockerfile
+- Added better debugging with startup script
+- Configured proper environment variables for Hugging Face
+- Added curl for health checks
+
+### 2. Added Startup Script (`start.sh`)
+- Shows directory contents on startup
+- Verifies model files are present
+- Starts uvicorn server with proper configuration
+
+### 3. API Configuration
+- Root endpoint (`/`) now shows a welcome page
+- Health check available at `/health`
+- Debug endpoint at `/debug_model` to diagnose issues
+- API documentation at `/docs`
+
+## How to Deploy
+
+### Step 1: Push to Hugging Face
+```bash
+cd "e:\1-Antigravity Python\Bacsense 2.0\BacSense-API"
+git add .
+git commit -m "Fix deployment: add startup script and debugging"
+git push origin main
+```
+
+### Step 2: Check Deployment Logs
+1. Go to your Hugging Face Space: https://huggingface.co/spaces/joytheslothh/BacSense-API
+2. Click on "Logs" tab
+3. Watch the startup logs to see if:
+   - All dependencies install successfully
+   - Model files are found
+   - Server starts on port 7860
+
+### Step 3: Test Endpoints
+Once deployed, test these endpoints:
+
+**Health Check:**
+```
+https://joytheslothh-bacsense-api.hf.space/health
+```
+
+**API Documentation:**
+```
+https://joytheslothh-bacsense-api.hf.space/docs
+```
+
+**Debug Model:**
+```
+https://joytheslothh-bacsense-api.hf.space/debug_model
+```
+
+## Frontend Configuration (Vercel)
+
+Update your frontend API URL in Vercel to point to:
+```
+https://joytheslothh-bacsense-api.hf.space
+```
+
+Or in your frontend code, update the API base URL:
+```typescript
+// In your React code
+const API_URL = "https://joytheslothh-bacsense-api.hf.space";
+```
+
+## Troubleshooting
+
+### If you still see "Not Found":
+
+1. **Check the logs** - Look for import errors or missing files
+2. **Verify model loading** - Visit `/debug_model` endpoint
+3. **Test locally** - Run `docker build -t bacsense .` and `docker run -p 7860:7860 bacsense`
+
+### Common Issues:
+
+**TensorFlow Import Error:**
+- Ensure all requirements are installed
+- Check if TensorFlow CPU version is compatible
+
+**Model Files Not Found:**
+- Verify `bacsense_v2_package` folder exists in the repo
+- Check that `.keras` and `.pkl` files are tracked by Git LFS
+
+**Port Binding Issue:**
+- Hugging Face expects port 7860
+- Server must bind to `0.0.0.0` not `localhost`
+
+## Alternative: Streamlit-Only Deployment
+
+If FastAPI continues to have issues, you can deploy as a pure Streamlit app:
+
+1. Rename `app.py` to `streamlit_app.py`
+2. Update Dockerfile CMD to: `CMD ["streamlit", "run", "streamlit_app.py"]`
+
+This gives you the dashboard without the separate API backend.

Dockerfile

@@ -7,6 +7,7 @@ RUN apt-get update && apt-get install -y \
     libsm6 \
     libxrender1 \
     libxext6 \
+    curl \
     && rm -rf /var/lib/apt/lists/*
 
 # Set up user
@@ -23,8 +24,15 @@ RUN pip install --no-cache-dir -r requirements.txt
 # Copy source code
 COPY --chown=user . .
 
-# Hugging Face usually expects 7860
+# Hugging Face Spaces typically expects port 7860
 EXPOSE 7860
 
-# Run FastAPI
-CMD ["uvicorn", "classifier_service.api:app", "--host", "0.0.0.0", "--port", "7860"]
+# Environment variable for Hugging Face
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+ENV GRADIO_SERVER_PORT="7860"
+
+# Make startup script executable
+RUN chmod +x start.sh
+
+# Run the startup script for better debugging
+CMD ["./start.sh"]

README.md

@@ -1,90 +1,11 @@
----
-title: BacSense API
+title: BacSense v2 API
 emoji: 🦠
 colorFrom: blue
-colorTo: green
-sdk: streamlit
-sdk_version: "1.32.0"
-app_file: app.py
+colorTo: purple
+sdk: docker
 pinned: false
----
-
-# 🦠 Bacsense 2.0
-
-![Bacsense Banner](https://img.shields.io/badge/Bacsense-2.0-13a4ec?style=for-the-badge)
-
-**Bacsense 2.0** is an open-access visual platform for clinical microbiology research. Our mission is to accelerate pathogen identification through advanced hybrid neural networks and machine learning. 
-
-This project integrates a robust **VGG16 + SVM** hybrid classification architecture with a modern, high-performance web interface to quickly and accurately identify microscopic bacterial species from uploaded culture images.
-
-## ✨ Key Features
-
-- **🔬 High-Accuracy Classification:** Leverages a pre-trained VGG16 backbone for deep feature extraction, paired with a Support Vector Machine (SVM) classifier for pinpoint taxa identification.
-- **⚡ Real-time API:** Fast and lightweight inference backend powered by FastAPI.
-- **🌌 Premium Scientific UI:** A stunning, fully responsive dark-theme design featuring highly interactive GSAP spring cursors, meteor shower effects, and beautifully animated petri-dish data components.
-- **📊 Detailed Analysis Metrics:** Get immediate clinical insights on morphological traits, probability distribution thresholds, and gram stains for tested pathogens natively in the browser.
-
-## 🛠️ Tech Stack
-
-### Frontend
-- **Framework:** React + Vite (TypeScript)
-- **Styling:** Tailwind CSS
-- **Animations:** GSAP (GreenSock) & Framer Motion
-- **UI Architecture:** MagicUI
-
-### Backend / ML Engine
-- **REST API Runtime:** FastAPI & Uvicorn
-- **Machine Learning Pipelines:** TensorFlow / Keras (VGG16), Scikit-Learn (SVM, PCA)
-- **Image Processing Computation:** Pillow (PIL), NumPy, SciPy
-
----
-
-## 🚀 Getting Started
-
-### Prerequisites
-- [Node.js](https://nodejs.org/) (v16+)
-- [Python](https://python.org/) (3.9+)
-
-### 1. Boot the ML Backend
-
-Open a terminal in the project root and navigate to the backend service to spin up the prediction API:
-
-```bash
-cd bacterial-classifier
-python -m venv venv
-
-# Windows Activation
-venv\Scripts\activate  
-# Mac/Linux Activation
-# source venv/bin/activate  
-
-pip install -r requirements.txt
-pip install fastapi uvicorn python-multipart
-
-# Start the FastAPI uvicorn server
-uvicorn api:app --host 0.0.0.0 --port 5000 --reload
-```
-The ML API will successfully bind to `http://localhost:5000`.
-
-### 2. Start the React Frontend
-
-Open a new terminal tab, navigate to the frontend folder, install dependencies, and launch the Vite dev server:
-
-```bash
-cd frontend
-npm install
-npm run dev
-```
-
-The user interface will be live at `http://localhost:5173`. 🥳 Drag and drop a microscopic image into the Upload Zone to test the prediction model!
-
-## 🔬 Supported Species
-The engine spans multiple common pathogenic datasets and correctly identifies critical bacteria including:
-- *Escherichia coli* (Gram-negative)
-- *Staphylococcus aureus* (Gram-positive)
-- *Clostridium perfringens* (Anaerobic)
-- *Bacillus cereus* (Spore-forming)
-- *Listeria monocytogenes*
-
----
-*© 2026 Bacsense Scientific Systems. Built for Next-Gen Bioinformatics.*
+license: mit
+tags:
+  - water-quality
+  - bacteria-classification
+  - machine-learning

bacterial-classifier/api.py

@@ -1,80 +0,0 @@
-import io
-import os
-import sys
-import tempfile
-from typing import List
-from fastapi import FastAPI, UploadFile, File, HTTPException
-from fastapi.middleware.cors import CORSMiddleware
-
-# Add the parent directory to sys.path to import bacsense_v2_package
-sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
-from bacsense_v2_package.inference import BacSense
-
-app = FastAPI(title="Bacsense 2.0 API")
-
-# Setup CORS to allow requests from the React frontend
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=["*"],  # Adjust this in production, e.g., ["http://localhost:5173"]
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
-
-# Load the model upon startup
-# The model files are located in the bacsense_v2_package directory
-model_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..', 'bacsense_v2_package'))
-classifier = BacSense(model_dir=model_dir)
-classifier.warmup()
-
-@app.post("/predict_batch")
-async def predict_batch(files: List[UploadFile] = File(...)):
-    if not files or len(files) == 0:
-        raise HTTPException(status_code=400, detail="No files uploaded")
-    
-    results = []
-    
-    for file in files:
-        temp_path = None
-        try:
-            # Read the uploaded file into an IO stream
-            contents = await file.read()
-            
-            # BacSense uses cv2.imread and PIL.Image.open with a file path, so we save it to disk temporarily
-            fd, temp_path = tempfile.mkstemp(suffix=".png")
-            with os.fdopen(fd, 'wb') as f:
-                f.write(contents)
-            
-            # Process the image
-            result = classifier.predict(temp_path)
-            
-            # Format probabilities for the frontend
-            # UI expects 0-100 for confidence
-            confidence_pct = result["confidence"] * 100 if result["confidence"] <= 1.0 else result["confidence"]
-            
-            results.append({
-                "filename": file.filename,
-                "success": True,
-                "prediction": result['prediction'],
-                "confidence": confidence_pct,
-                "probabilities": [
-                    {"name": result['prediction'], "probability": confidence_pct}
-                ],
-                "details": {
-                    "gram_stain": result.get("gram", "Unknown"),
-                    "shape": result.get("shape", "Unknown"),
-                    "pathogenicity": result.get("risk", "Unknown")
-                }
-            })
-        except Exception as e:
-            results.append({
-                "filename": file.filename,
-                "success": False,
-                "error": str(e)
-            })
-        finally:
-            # Clean up the temporary file
-            if temp_path and os.path.exists(temp_path):
-                os.remove(temp_path)
-                
-    return {"results": results}

classifier_service/api.py

@@ -49,12 +49,14 @@ async def root():
     </html>
     """
 
-# Catch-all route to resolve any routing issues on Hugging Face
+# Catch-all route for SPA support (but exclude API endpoints)
 @app.get("/{path_name:path}", response_class=HTMLResponse)
 async def catch_all(path_name: str):
-    # If the user is trying to reach docs or health, let those routes handle it
-    if path_name in ["docs", "redoc", "openapi.json", "health", "debug_model", "predict_batch"]:
-        raise HTTPException(status_code=404) 
+    # Don't catch API endpoints - they should return 404 if not found
+    api_endpoints = ["docs", "redoc", "openapi.json", "health", "debug_model", "predict_batch", "api"]
+    if any(path_name.startswith(endpoint) for endpoint in api_endpoints):
+        raise HTTPException(status_code=404)
+    # For everything else, return the root page (useful for SPA routing)
     return await root()
 
 # Global classifier instance

push2.txt

@@ -0,0 +1,12 @@
+Uploading LFS objects: 100% (7/7), 61 MB | 0 B/s, done.
+remote: -------------------------------------------------------------------------        
+remote: Your push was rejected because it contains files larger than 10 MiB.        
+remote: Please use https://git-lfs.github.com/ to store large files.        
+remote: See also: https://hf.co/docs/hub/repositories-getting-started#terminal        
+remote:         
+remote: Offending files:        
+remote:   - bacsense_v2_package/vgg16_feature_extractor.keras (ref: refs/heads/main)        
+remote: -------------------------------------------------------------------------        
+To https://huggingface.co/spaces/joytheslothh/BacSense-API
+ ! [remote rejected] main -> main (pre-receive hook declined)
+error: failed to push some refs to 'https://huggingface.co/spaces/joytheslothh/BacSense-API'

push_final.txt

@@ -0,0 +1,14 @@
+Pushing to https://huggingface.co/spaces/joytheslothh/BacSense-API
+Uploading LFS objects: 100% (7/7), 61 MB | 0 B/s, done.
+POST git-receive-pack (60176516 bytes)
+remote: -------------------------------------------------------------------------        
+remote: Your push was rejected because it contains files larger than 10 MiB.        
+remote: Please use https://git-lfs.github.com/ to store large files.        
+remote: See also: https://hf.co/docs/hub/repositories-getting-started#terminal        
+remote:         
+remote: Offending files:        
+remote:   - bacsense_v2_package/vgg16_feature_extractor.keras (ref: refs/heads/main)        
+remote: -------------------------------------------------------------------------        
+To https://huggingface.co/spaces/joytheslothh/BacSense-API
+ ! [remote rejected] main -> main (pre-receive hook declined)
+error: failed to push some refs to 'https://huggingface.co/spaces/joytheslothh/BacSense-API'

push_log.txt

@@ -0,0 +1,12 @@
+Uploading LFS objects: 100% (7/7), 61 MB | 0 B/s, done.
+remote: -------------------------------------------------------------------------        
+remote: Your push was rejected because it contains files larger than 10 MiB.        
+remote: Please use https://git-lfs.github.com/ to store large files.        
+remote: See also: https://hf.co/docs/hub/repositories-getting-started#terminal        
+remote:         
+remote: Offending files:        
+remote:   - bacsense_v2_package/vgg16_feature_extractor.keras (ref: refs/heads/main)        
+remote: -------------------------------------------------------------------------        
+To https://huggingface.co/spaces/joytheslothh/BacSense-API
+ ! [remote rejected] main -> main (pre-receive hook declined)
+error: failed to push some refs to 'https://huggingface.co/spaces/joytheslothh/BacSense-API'

requirements.txt

@@ -1,11 +1,11 @@
-tensorflow>=2.12.0
+tensorflow-cpu>=2.15.0,<2.16.0
 scikit-learn>=1.3.0
 scikit-image>=0.21.0
 opencv-python-headless>=4.8.0
-numpy>=1.24.0
-Pillow>=9.5.0
+numpy>=1.24.0,<2.0.0
+Pillow>=10.0.0
 scipy>=1.11.0
-streamlit>=1.32.0
+streamlit>=1.41.0
 pandas>=2.0.0
 fastapi
 uvicorn

start.sh

@@ -0,0 +1,15 @@
+#!/bin/bash
+# Startup script for Hugging Face Spaces
+
+echo "🦠 Starting BacSense v2 API..."
+echo "Current directory: $(pwd)"
+echo "Contents:"
+ls -la
+
+echo ""
+echo "Checking bacsense_v2_package:"
+ls -la bacsense_v2_package/ || echo "Directory not found!"
+
+echo ""
+echo "Starting uvicorn server..."
+exec uvicorn classifier_service.api:app --host 0.0.0.0 --port 7860

upload_to_hf.py

@@ -0,0 +1,43 @@
+from huggingface_hub import HfApi
+import os
+
+TOKEN = "REDACTED"
+REPO_ID = "joytheslothh/BacSense-API"
+REPO_TYPE = "space"
+
+# Files/dirs to skip (frontend, docs, git artifacts)
+SKIP_DIRS = {"frontend", ".git", "__pycache__", ".gitignore"}
+SKIP_FILES = {"push_log.txt", "push2.txt", "push_final.txt", "upload_to_hf.py",
+              "render.yaml", "BacSense_v2_Technical_Documentation.docx",
+              "BacSense_v2_Technical_Documentation.docx-1.pdf",
+              "BacSense_v2_Technical_Documentation.docx.txt"}
+SKIP_EXTS = {".ps1"}
+
+api = HfApi(token=TOKEN)
+base = os.path.abspath(os.path.dirname(__file__))
+
+files_to_upload = []
+for root, dirs, files in os.walk(base):
+    # Prune skip dirs in-place
+    dirs[:] = [d for d in dirs if d not in SKIP_DIRS and not d.startswith('.')]
+    for fname in files:
+        if fname in SKIP_FILES:
+            continue
+        if any(fname.endswith(ext) for ext in SKIP_EXTS):
+            continue
+        full_path = os.path.join(root, fname)
+        rel_path = os.path.relpath(full_path, base).replace("\\", "/")
+        files_to_upload.append((full_path, rel_path))
+
+print(f"Uploading {len(files_to_upload)} files to {REPO_ID}...\n")
+for i, (local, remote) in enumerate(files_to_upload, 1):
+    size_mb = os.path.getsize(local) / (1024*1024)
+    print(f"[{i}/{len(files_to_upload)}] {remote} ({size_mb:.2f} MB)")
+    api.upload_file(
+        path_or_fileobj=local,
+        path_in_repo=remote,
+        repo_id=REPO_ID,
+        repo_type=REPO_TYPE,
+    )
+
+print("\n✅ All files uploaded successfully!")