Create app.py

app.py

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+import torch
+import torch.nn.functional as F
+from transformers import AutoModelForImageClassification
+from torchvision import transforms
+from PIL import Image, ImageStat
+from fastapi import FastAPI, File, UploadFile, HTTPException
+from fastapi.responses import HTMLResponse
+import io
+import os
+import gc
+
+# ==========================================
+# 1. CONFIGURATION (Rural India Context)
+# ==========================================
+MODELS = {
+    "lungs": {
+        "id": "nickmuchi/vit-finetuned-chest-xray-pneumonia",
+        "desc": "Tuberculosis & Pneumonia (Chest X-Ray)",
+        "safe": ["NORMAL", "normal", "No Pneumonia"],
+        "check_color": True # X-Rays must be black & white
+    },
+    "blood": { 
+        "id": "mrm8488/vit-base-patch16-224-finetuned-malaria-detection",
+        "desc": "Malaria Screening (Microscopic Slide)",
+        "safe": ["Uninfected", "uninfected"],
+        "check_color": False
+    },
+    "eye": {
+        "id": "AventIQ-AI/resnet18-cataract-detection-system", 
+        "desc": "Cataract Detection (Smartphone Eye Photo)",
+        "safe": ["Normal", "normal", "healthy"],
+        "check_color": False
+    },
+    "skin": {
+        "id": "Anwarkh1/Skin_Cancer-Image_Classification",
+        "desc": "Dermatology & Lesion Analysis",
+        "safe": ["Benign", "benign", "nv", "bkl"],
+        "check_color": False
+    }
+}
+
+# ==========================================
+# 2. AI ENGINE (The Brain)
+# ==========================================
+class MedicalEngine:
+    def __init__(self):
+        # Force CPU to avoid memory crashes on Free Tier
+        self.device = "cpu"
+        print("✅ System Initialized: Medical Engine Ready (Lazy Loading)")
+        
+        # Standard Image Transformation
+        self.transform = transforms.Compose([
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
+        ])
+
+    def validate_image(self, image, task):
+        """Guardrail: Rejects obvious bad images (e.g. Selfies for X-Ray)"""
+        if MODELS[task]["check_color"]:
+            # Check saturation for X-Rays
+            stat = ImageStat.Stat(image.convert('HSV'))
+            saturation = stat.mean[1] # 0 = Gray, 255 = Color
+            if saturation > 35: # Threshold
+                return False, "⚠️ Invalid Image: This looks like a color photo. Please upload a Black & White X-Ray."
+        return True, ""
+
+    def predict(self, image_bytes, task):
+        # A. Validation
+        try:
+            image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+        except:
+            return {"error": "File is not a valid image."}
+
+        is_valid, msg = self.validate_image(image, task)
+        if not is_valid:
+            return {"error": msg, "risk": "INVALID"}
+
+        # B. Load Model (On Demand)
+        print(f"⏳ Downloading/Loading Model for: {task}...")
+        try:
+            model_id = MODELS[task]["id"]
+            model = AutoModelForImageClassification.from_pretrained(model_id)
+            model.to(self.device)
+            model.eval()
+        except Exception as e:
+            print(f"❌ Model Load Error: {e}")
+            return {"error": "Failed to load AI model. Try again."}
+
+        # C. Inference
+        try:
+            inputs = self.transform(image).unsqueeze(0).to(self.device)
+            with torch.no_grad():
+                outputs = model(inputs)
+                probs = F.softmax(outputs.logits, dim=-1)
+            
+            # Extract Results
+            results = []
+            for i, score in enumerate(probs[0]):
+                label = model.config.id2label[i]
+                results.append({"label": label, "score": float(score)})
+            results.sort(key=lambda x: x['score'], reverse=True)
+            
+            top = results[0]
+            
+            # D. Risk Logic
+            safe_words = MODELS[task]["safe"]
+            is_safe = any(s.lower() in top["label"].lower() for s in safe_words)
+            
+            if top["score"] < 0.5:
+                risk = "UNCERTAIN" # Low confidence
+            elif is_safe:
+                risk = "LOW"
+            else:
+                risk = "HIGH" if top["score"] > 0.75 else "MODERATE"
+
+        except Exception as e:
+            return {"error": f"Prediction Error: {str(e)}"}
+        
+        finally:
+            # E. Cleanup RAM (Critical for Free Tier)
+            del model
+            gc.collect()
+
+        return {
+            "task": task,
+            "desc": MODELS[task]["desc"],
+            "prediction": top,
+            "risk": risk
+        }
+
+# ==========================================
+# 3. API SERVER (FastAPI)
+# ==========================================
+app = FastAPI()
+engine = MedicalEngine()
+
+@app.post("/predict/{task}")
+async def predict_route(task: str, file: UploadFile = File(...)):
+    if task not in MODELS:
+        return {"error": "Invalid Task"}
+    
+    content = await file.read()
+    return engine.predict(content, task)
+
+# ==========================================
+# 4. FRONTEND UI (HTML embedded)
+# ==========================================
+@app.get("/", response_class=HTMLResponse)
+def home():
+    return """
+<!DOCTYPE html>
+<html>
+<head>
+    <title>MediScan Rural | Ayushman Bharat Node</title>
+    <script src="https://cdn.tailwindcss.com"></script>
+    <link href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/6.0.0/css/all.min.css" rel="stylesheet">
+</head>
+<body class="bg-slate-50 min-h-screen flex flex-col font-sans">
+
+    <nav class="bg-blue-900 text-white p-4 shadow-xl">
+        <div class="container mx-auto flex justify-between items-center">
+            <div class="flex items-center gap-3">
+                <i class="fas fa-heartbeat text-2xl text-green-400"></i>
+                <div>
+                    <h1 class="text-xl font-bold">MediScan Rural</h1>
+                    <p class="text-xs text-blue-200">North East Node • Online</p>
+                </div>
+            </div>
+            <div class="text-right hidden md:block">
+                <span class="bg-blue-800 px-3 py-1 rounded text-xs font-bold border border-blue-600">
+                    <i class="fas fa-satellite-dish mr-1"></i> Ayushman Bharat Linked
+                </span>
+            </div>
+        </div>
+    </nav>
+
+    <div class="container mx-auto mt-8 p-4 max-w-3xl flex-grow">
+        
+        <div class="grid grid-cols-4 gap-2 mb-6">
+            <button onclick="setTask('lungs')" id="btn-lungs" class="p-3 bg-white rounded-lg shadow hover:bg-blue-50 border-2 border-transparent transition text-sm font-bold text-gray-600">
+                <i class="fas fa-lungs text-blue-500 block text-2xl mb-1"></i> Lungs
+            </button>
+            <button onclick="setTask('blood')" id="btn-blood" class="p-3 bg-white rounded-lg shadow hover:bg-red-50 border-2 border-transparent transition text-sm font-bold text-gray-600">
+                <i class="fas fa-burn text-red-500 block text-2xl mb-1"></i> Malaria
+            </button>
+            <button onclick="setTask('eye')" id="btn-eye" class="p-3 bg-white rounded-lg shadow hover:bg-indigo-50 border-2 border-transparent transition text-sm font-bold text-gray-600">
+                <i class="fas fa-eye text-indigo-500 block text-2xl mb-1"></i> Eye
+            </button>
+            <button onclick="setTask('skin')" id="btn-skin" class="p-3 bg-white rounded-lg shadow hover:bg-orange-50 border-2 border-transparent transition text-sm font-bold text-gray-600">
+                <i class="fas fa-hand-dots text-orange-500 block text-2xl mb-1"></i> Skin
+            </button>
+        </div>
+
+        <div class="bg-white rounded-2xl shadow-xl p-6 border border-gray-200">
+            <h2 id="header-text" class="text-xl font-bold text-gray-700 mb-4">Select a Category above</h2>
+            
+            <div id="inputs" class="opacity-50 pointer-events-none transition-opacity mb-6">
+                <div class="grid grid-cols-2 gap-4 mb-4">
+                    <input type="text" id="p-name" placeholder="Patient Name" class="border p-2 rounded bg-gray-50">
+                    <input type="text" placeholder="Age / ID" class="border p-2 rounded bg-gray-50">
+                </div>
+                
+                <div onclick="document.getElementById('file-input').click()" class="border-2 border-dashed border-gray-300 rounded-xl p-8 text-center cursor-pointer hover:bg-gray-50 transition">
+                    <input type="file" id="file-input" class="hidden" accept="image/*" onchange="showPreview(event)" onclick="this.value=null">
+                    <div id="placeholder">
+                        <i class="fas fa-cloud-upload-alt text-3xl text-gray-400 mb-2"></i>
+                        <p class="text-gray-500 text-sm">Tap to upload image</p>
+                    </div>
+                    <img id="preview" class="hidden mx-auto max-h-48 rounded shadow">
+                </div>
+            </div>
+
+            <button id="run-btn" onclick="analyze()" class="hidden w-full bg-blue-600 hover:bg-blue-700 text-white font-bold py-3 rounded-xl shadow-lg transition">
+                <i class="fas fa-microscope mr-2"></i> Run Diagnosis
+            </button>
+            
+            <div id="loader" class="hidden text-center py-6">
+                <div class="inline-block animate-spin rounded-full h-8 w-8 border-4 border-blue-500 border-t-transparent"></div>
+                <p class="text-sm text-gray-500 mt-2 font-semibold">Downloading AI Model & Analyzing...</p>
+                <p class="text-xs text-gray-400">(This takes ~20s on first run)</p>
+            </div>
+
+            <div id="result-box" class="hidden mt-6 border-t pt-6">
+                <div class="flex justify-between items-start">
+                    <div>
+                        <p class="text-xs font-bold text-gray-400 uppercase">Prediction</p>
+                        <h1 id="res-label" class="text-3xl font-extrabold text-gray-800">--</h1>
+                        <p class="text-sm text-gray-600 mt-1">Confidence: <span id="res-conf" class="font-mono font-bold">--</span></p>
+                    </div>
+                    <span id="res-badge" class="px-3 py-1 rounded text-sm font-bold uppercase">--</span>
+                </div>
+                
+                <div id="alert-box" class="hidden mt-4 p-3 bg-red-50 text-red-800 rounded border border-red-200 text-sm">
+                    <i class="fas fa-ambulance mr-2"></i> <span id="alert-text">High Risk Detected.</span>
+                </div>
+
+                <div class="mt-4 bg-gray-50 p-3 rounded border flex items-center justify-between text-xs">
+                    <span class="font-bold text-gray-500"><i class="fas fa-database mr-1"></i> Govt Sync</span>
+                    <span id="sync-msg" class="text-yellow-600"><i class="fas fa-sync fa-spin"></i> Pending...</span>
+                </div>
+            </div>
+
+        </div>
+    </div>
+
+    <footer class="text-center p-4 text-xs text-gray-400">
+        ⚠️ Disclaimer: AI Research Tool. Verify with a Doctor.
+    </footer>
+
+    <script>
+        let currTask = null;
+        let currFile = null;
+
+        function setTask(task) {
+            currTask = task;
+            
+            // Highlight Buttons
+            document.querySelectorAll('button[id^="btn-"]').forEach(b => b.classList.remove('ring-2', 'ring-blue-400', 'border-blue-500'));
+            document.getElementById('btn-'+task).classList.add('ring-2', 'ring-blue-400', 'border-blue-500');
+            
+            // Reset UI
+            document.getElementById('header-text').innerHTML = `Upload <span class="uppercase text-blue-600">${task}</span> Image`;
+            document.getElementById('inputs').classList.remove('opacity-50', 'pointer-events-none');
+            document.getElementById('result-box').classList.add('hidden');
+            document.getElementById('run-btn').classList.add('hidden');
+            document.getElementById('placeholder').classList.remove('hidden');
+            document.getElementById('preview').classList.add('hidden');
+            currFile = null;
+        }
+
+        function showPreview(event) {
+            if (event.target.files && event.target.files[0]) {
+                currFile = event.target.files[0];
+                let reader = new FileReader();
+                reader.onload = function(e) {
+                    document.getElementById('preview').src = e.target.result;
+                    document.getElementById('preview').classList.remove('hidden');
+                    document.getElementById('placeholder').classList.add('hidden');
+                    document.getElementById('run-btn').classList.remove('hidden');
+                    document.getElementById('result-box').classList.add('hidden');
+                };
+                reader.readAsDataURL(currFile);
+            }
+        }
+
+        async function analyze() {
+            if (!currTask || !currFile) return;
+            
+            if (!document.getElementById('p-name').value) {
+                alert("Please enter Patient Name.");
+                return;
+            }
+
+            // Show Loader
+            document.getElementById('run-btn').classList.add('hidden');
+            document.getElementById('loader').classList.remove('hidden');
+            document.getElementById('result-box').classList.add('hidden');
+
+            let formData = new FormData();
+            formData.append("file", currFile);
+
+            try {
+                // Determine API URL (Handles Hugging Face URL structure)
+                let url = "/predict/" + currTask;
+                
+                let res = await fetch(url, { method: "POST", body: formData });
+                let data = await res.json();
+
+                if (data.error) {
+                    alert("Error: " + data.error);
+                    resetLoading();
+                    return;
+                }
+
+                // Show Results
+                document.getElementById('loader').classList.add('hidden');
+                document.getElementById('result-box').classList.remove('hidden');
+
+                document.getElementById('res-label').innerText = data.prediction.label;
+                document.getElementById('res-conf').innerText = (data.prediction.score * 100).toFixed(1) + "%";
+
+                let badge = document.getElementById('res-badge');
+                let alertBox = document.getElementById('alert-box');
+
+                if (data.risk === "HIGH") {
+                    badge.className = "px-3 py-1 rounded text-sm font-bold uppercase bg-red-100 text-red-700";
+                    alertBox.classList.remove('hidden');
+                    document.getElementById('alert-text').innerText = "High Risk. Immediate Referral Recommended.";
+                } else if (data.risk === "MODERATE") {
+                    badge.className = "px-3 py-1 rounded text-sm font-bold uppercase bg-yellow-100 text-yellow-700";
+                    alertBox.classList.remove('hidden');
+                    document.getElementById('alert-text').innerText = "Moderate Risk. Consult Doctor.";
+                } else if (data.risk === "INVALID" || data.risk === "UNCERTAIN") {
+                    badge.className = "px-3 py-1 rounded text-sm font-bold uppercase bg-gray-200 text-gray-700";
+                    alertBox.classList.remove('hidden');
+                    document.getElementById('alert-text').innerText = "Image Unclear / Invalid. Retake Photo.";
+                } else {
+                    badge.className = "px-3 py-1 rounded text-sm font-bold uppercase bg-green-100 text-green-700";
+                    alertBox.classList.add('hidden');
+                }
+                
+                badge.innerText = data.risk + " RISK";
+
+                // Sync Animation
+                setTimeout(() => {
+                    document.getElementById('sync-msg').innerHTML = "<i class='fas fa-check-circle'></i> Synced!";
+                    document.getElementById('sync-msg').className = "text-green-600 font-bold";
+                }, 2000);
+
+            } catch (e) {
+                alert("Connection Failed. Refresh and try again.");
+                console.error(e);
+                resetLoading();
+            }
+        }
+
+        function resetLoading() {
+            document.getElementById('loader').classList.add('hidden');
+            document.getElementById('run-btn').classList.remove('hidden');
+        }
+    </script>
+</body>
+</html>
+    """