Deploy from GitHub: 7c7b0b67e1dae2679d91ef948e246730d7d10fbf

backend/app/ml/model.py

@@ -1,14 +1,118 @@
 import os
+import httpx
+
+OPENROUTER_API_KEY = os.environ.get("OPENROUTER_API_KEY", "")
+BASE_URL = "https://openrouter.ai/api/v1"
+
+# Use a fast model for simplification (called per-finding, so speed matters)
+SIMPLIFY_MODEL = "deepseek/deepseek-chat-v3-0324:free"
+
 
-# Mock model loader for testing without HuggingFace dependencies
 def load_model():
     """
-    Mock model loader. In production, replace with actual model loading.
+    Check if OpenRouter API key is available.
+    In production, this would also load local models.
     """
-    print("Using mock model for simplification")
+    if OPENROUTER_API_KEY and not OPENROUTER_API_KEY.startswith("placeholder"):
+        print("✅ OpenRouter API key found — using AI for simplification")
+    else:
+        print("⚠️ No OpenRouter API key — using template-based simplification")
     return None, None
 
 
+# Hardcoded fallback templates for when AI is unavailable
+FALLBACK_EXPLANATIONS = {
+    ("HIGH", "GLUCOSE"): {
+        "english": "Your blood glucose is elevated at {value} {unit}. This suggests your body is having trouble managing blood sugar. Reduce sugary foods and consult your doctor for diabetes screening.",
+        "hindi": "आपका ब्लड ग्लूकोज़ {value} {unit} पर बढ़ा हुआ है। यह दर्शाता है कि आपका शरीर ब्लड शुगर को नियंत्रित करने में परेशानी आ रही है। मीठे खाना कम करें और डॉक्टर से मिलें।"
+    },
+    ("HIGH", "SGPT"): {
+        "english": "Your liver enzyme SGPT is high at {value} {unit}. This indicates liver inflammation. Avoid fatty foods and alcohol.",
+        "hindi": "आपका यकृत एंजाइम SGPT {value} {unit} पर बढ़ा हुआ है। यह यकृत में सूजन दर्शाता है। तैलीय खाना और शराब न लें।"
+    },
+    ("LOW", "HEMOGLOBIN"): {
+        "english": "Your hemoglobin is low at {value} {unit}. You may be anemic. Increase iron-rich foods like spinach and beans.",
+        "hindi": "आपका हीमोग्लोबिन {value} {unit} पर कम है। आपको एनीमिया हो सकता है। पालक और दाल जैसे आयरन युक्त खाना बढ़ाएं।"
+    },
+    ("HIGH", "CHOLESTEROL"): {
+        "english": "Your cholesterol is elevated at {value} {unit}. Reduce saturated fats, increase fiber, and exercise regularly.",
+        "hindi": "आपका कोलेस्ट्रॉल {value} {unit} पर बढ़ा हुआ है। संतृप्त वसा कम करें और नियमित व्यायाम करें।"
+    },
+    ("HIGH", "CREATININE"): {
+        "english": "Your creatinine is elevated at {value} {unit}. This may indicate kidney issues. Reduce protein intake and stay hydrated.",
+        "hindi": "आपका क्रिएटिनिन {value} {unit} पर बढ़ा है। यह गुर्दे की समस्या दर्शा सकता है। प्रोटीन इनटेक कम करें।"
+    },
+}
+
+
+def _ai_simplify(parameter: str, value: str, unit: str, status: str, rag_context: str) -> dict | None:
+    """Call OpenRouter to generate a layman explanation of a lab finding."""
+    if not OPENROUTER_API_KEY or OPENROUTER_API_KEY.startswith("placeholder"):
+        return None
+
+    prompt = f"""You are a friendly Indian doctor explaining lab results to a patient who has no medical knowledge.
+
+Lab Finding:
+- Parameter: {parameter}
+- Value: {value} {unit}
+- Status: {status}
+{f'- Context: {rag_context}' if rag_context else ''}
+
+Provide two explanations (2-3 sentences each):
+1. In simple English
+2. In simple Hindi (Devanagari script, everyday words)
+
+Format your response EXACTLY as:
+ENGLISH: <explanation>
+HINDI: <explanation>"""
+
+    try:
+        headers = {
+            "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+            "Content-Type": "application/json",
+            "HTTP-Referer": "https://reportraahat.app",
+            "X-Title": "ReportRaahat",
+        }
+
+        with httpx.Client(timeout=15.0) as client:
+            resp = client.post(
+                f"{BASE_URL}/chat/completions",
+                headers=headers,
+                json={
+                    "model": SIMPLIFY_MODEL,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "max_tokens": 300,
+                    "temperature": 0.5,
+                },
+            )
+
+        if resp.status_code == 200:
+            text = resp.json()["choices"][0]["message"]["content"].strip()
+
+            # Parse ENGLISH: and HINDI: from response
+            english = ""
+            hindi = ""
+            for line in text.split("\n"):
+                line = line.strip()
+                if line.upper().startswith("ENGLISH:"):
+                    english = line[8:].strip()
+                elif line.upper().startswith("HINDI:"):
+                    hindi = line[6:].strip()
+
+            if english and hindi:
+                return {"english": english, "hindi": hindi}
+
+            # If parsing failed, use the full response as English
+            return {"english": text[:200], "hindi": f"{parameter} {status.lower()} है। डॉक्टर से मिलें।"}
+
+        print(f"⚠️ AI simplify failed ({resp.status_code})")
+        return None
+
+    except Exception as e:
+        print(f"⚠️ AI simplify error: {e}")
+        return None
+
+
 def simplify_finding(
     parameter: str,
     value: str,
@@ -17,54 +121,36 @@ def simplify_finding(
     rag_context: str = ""
 ) -> dict:
     """
-    Mock simplification of medical findings.
-    In production, this would use the T5 model.
+    Generate a layman-friendly explanation for a lab finding.
+    Tries AI first, then falls back to templates.
     """
-    
-    # Simplified explanations based on common parameters and status
-    explanations = {
-        ("HIGH", "GLUCOSE"): {
-            "english": f"Your blood glucose is elevated at {value} {unit}. This suggests your body is having trouble managing blood sugar. Reduce sugary foods and consult your doctor for diabetes screening.",
-            "hindi": f"आपका ब्लड ग्लूकोज़ {value} {unit} पर बढ़ा हुआ है। यह दर्शाता है कि आपका शरीर ब्लड शुगर को नियंत्रित करने में परेशानी आ रही है। मीठे खाना कम करें और डॉक्टर से मिलें।"
-        },
-        ("HIGH", "SGPT"): {
-            "english": f"Your liver enzyme SGPT is high at {value} {unit}. This indicates liver inflammation. Avoid fatty foods and alcohol, and get liver function tests repeated.",
-            "hindi": f"आपका यकृत एंजाइम SGPT {value} {unit} पर बढ़ा हुआ है। यह यकृत में सूजन दर्शाता है। तैलीय खाना और शराब न लें।"
-        },
-        ("LOW", "HEMOGLOBIN"): {
-            "english": f"Your hemoglobin is low at {value} {unit}. You may be anemic. Increase iron-rich foods like spinach, liver, and beans. Get iron supplements if recommended by doctor.",
-            "hindi": f"आपका हीमोग्लोबिन {value} {unit} पर कम है। आपको एनीमिया हो सकता है। पालक, यकृत, और दाल जैसे आयरन युक्त खाना बढ़ाएं।"
-        },
-        ("HIGH", "CHOLESTEROL"): {
-            "english": f"Your cholesterol is elevated at {value} {unit}. Reduce saturated fats, increase fiber intake, and exercise regularly. Follow up with your doctor.",
-            "hindi": f"आपका कोलेस्ट्रॉल {value} {unit} पर बढ़ा हुआ है। संतृप्त वसा कम करें और नियमित व्यायाम करें।"
-        },
-        ("HIGH", "CREATININE"): {
-            "english": f"Your creatinine is elevated at {value} {unit}. This may indicate kidney issues. Reduce protein intake and stay hydrated. Consult a nephrologist.",
-            "hindi": f"आपका क्रिएटिनिन {value} {unit} पर बढ़ा है। यह गुर्दे की समस्या दर्शा सकता है। प्रोटीन इनटेक कम करें।"
-        }
-    }
-    
-    # Try to match the parameter with explanations
+
+    # Try AI-powered simplification
+    ai_result = _ai_simplify(parameter, value, unit, status, rag_context)
+    if ai_result:
+        return ai_result
+
+    # Fallback to template matching
     param_upper = parameter.upper()
     status_upper = status.upper()
-    
-    for (status_key, param_key) in explanations.keys():
+
+    for (status_key, param_key) in FALLBACK_EXPLANATIONS.keys():
         if param_key in param_upper and status_key == status_upper:
-            return explanations[(status_key, param_key)]
-    
-    # Default explanation
+            template = FALLBACK_EXPLANATIONS[(status_key, param_key)]
+            return {
+                "english": template["english"].format(value=value, unit=unit),
+                "hindi": template["hindi"].format(value=value, unit=unit),
+            }
+
+    # Generic default
     default_exp = {
-        "HIGH": f"Your {parameter} is high at {value} {unit}. This suggests abnormality. Please consult your doctor for proper evaluation and treatment.",
-        "LOW": f"Your {parameter} is low at {value} {unit}. This may indicate deficiency. Consult your doctor for recommendations.",
-        "CRITICAL": f"Your {parameter} is critically high at {value} {unit}. This requires immediate medical attention. Please see a doctor urgently.",
+        "HIGH": f"Your {parameter} is high at {value} {unit}. This needs attention. Consult your doctor.",
+        "LOW": f"Your {parameter} is low at {value} {unit}. This may indicate a deficiency. Consult your doctor.",
+        "CRITICAL": f"Your {parameter} is critically abnormal at {value} {unit}. Please see a doctor urgently.",
         "NORMAL": f"Your {parameter} is normal at {value} {unit}. Keep maintaining healthy habits."
     }
-    
-    english_text = default_exp.get(status_upper, f"Your {parameter} is {status.lower()}.")
-    hindi_text = f"{parameter} {status.lower()} है। डॉक्टर से मिलें।"
-    
+
     return {
-        "english": english_text,
-        "hindi": hindi_text
+        "english": default_exp.get(status_upper, f"Your {parameter} is {status.lower()}."),
+        "hindi": f"{parameter} {status.lower()} है। डॉक्टर से मिलें।"
     }

backend/app/ml/openrouter.py

@@ -11,9 +11,9 @@ BASE_URL = "https://openrouter.ai/api/v1"
 
 # Free models available on OpenRouter — fallback chain
 MODELS = [
-    "stepfun/step-3.5-flash:free",
-    "nvidia/nemotron-3-super-120b-a12b:free",
-    "arcee-ai/trinity-large-preview:free",
+    "deepseek/deepseek-chat-v3-0324:free",
+    "google/gemma-3-27b-it:free",
+    "meta-llama/llama-4-maverick:free",
 ]
 
 
@@ -59,7 +59,6 @@ def build_system_prompt(guc: dict) -> str:
         else "Always respond in simple English."
     )
 
-    # Add empathy instruction if stress is high
     empathy_note = (
         "\nNOTE: This patient has high stress levels. "
         "Be extra gentle, reassuring and empathetic in your responses. "
@@ -103,8 +102,52 @@ IMPORTANT RULES:
     return prompt
 
 
-# Enhanced mock responses moved to app/ml/enhanced_chat.py
-# See get_enhanced_mock_response() for detailed contextual responses
+def _call_openrouter(messages: list[dict]) -> str | None:
+    """
+    Call OpenRouter API with the given messages.
+    Tries each model in MODELS until one succeeds.
+    Returns the reply string, or None on failure.
+    """
+    if not OPENROUTER_API_KEY or OPENROUTER_API_KEY.startswith("placeholder"):
+        return None
+
+    headers = {
+        "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+        "Content-Type": "application/json",
+        "HTTP-Referer": "https://reportraahat.app",
+        "X-Title": "ReportRaahat",
+    }
+
+    for model in MODELS:
+        try:
+            payload = {
+                "model": model,
+                "messages": messages,
+                "max_tokens": 500,
+                "temperature": 0.7,
+            }
+
+            with httpx.Client(timeout=30.0) as client:
+                resp = client.post(
+                    f"{BASE_URL}/chat/completions",
+                    headers=headers,
+                    json=payload,
+                )
+
+            if resp.status_code == 200:
+                data = resp.json()
+                reply = data["choices"][0]["message"]["content"]
+                print(f"✅ OpenRouter reply via {model}: {len(reply)} chars")
+                return reply.strip()
+            else:
+                print(f"⚠️ OpenRouter {model} returned {resp.status_code}: {resp.text[:200]}")
+                continue
+
+        except Exception as e:
+            print(f"⚠️ OpenRouter {model} error: {e}")
+            continue
+
+    return None
 
 
 def chat(
@@ -115,19 +158,43 @@ def chat(
     """
     Send a message to Dr. Raahat via OpenRouter.
     Injects GUC context + RAG-retrieved knowledge.
-    Falls back to enhanced mock responses for testing.
+    Falls back to enhanced mock responses if API fails.
     """
-    retrieved_docs = []
-    
-    # Try to retrieve relevant medical documents from RAG
+    # Build system prompt with full GUC context
+    system_prompt = build_system_prompt(guc)
+
+    # Build conversation messages
+    messages = [{"role": "system", "content": system_prompt}]
+
+    # Add RAG-retrieved context if available
     try:
-        if rag_retriever and rag_retriever.loaded:
-            # Create embedding for the user's message (simplified for now)
-            # In production, use proper embeddings model
-            query_embedding = [0.1] * 768  # Placeholder - replace with real embeddings
-            retrieved_docs = rag_retriever.retrieve(query_embedding, k=3)
+        if retrieve_doctor_context:
+            docs = retrieve_doctor_context(message, top_k=3)
+            if docs:
+                context = "\n".join(f"- {d['text']}" for d in docs)
+                messages.append({
+                    "role": "system",
+                    "content": f"Relevant medical knowledge:\n{context}"
+                })
     except Exception as e:
-        print(f"⚠️  RAG retrieval failed: {e}")
-    
-    # Use enhanced mock responses with RAG grounding
+        print(f"⚠️ RAG retrieval failed: {e}")
+
+    # Add chat history
+    for msg in history[-10:]:  # Last 10 messages for context
+        role = msg.get("role", "user")
+        content = msg.get("content", msg.get("text", ""))
+        if content:
+            messages.append({"role": role, "content": content})
+
+    # Add current message
+    messages.append({"role": "user", "content": message})
+
+    # Try OpenRouter API first
+    reply = _call_openrouter(messages)
+    if reply:
+        return reply
+
+    # Fallback to enhanced mock responses
+    print("⚠️ OpenRouter unavailable, using mock responses")
+    retrieved_docs = []
     return get_enhanced_mock_response(message, guc, retrieved_docs)

frontend/app/api/exercise/route.ts

@@ -0,0 +1,36 @@
+// POST /api/exercise
+// Proxies to backend POST /exercise/
+
+import { NextRequest, NextResponse } from "next/server"
+
+const API_BASE = process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:8000"
+const TIMEOUT_MS = 10_000
+
+export async function POST(req: NextRequest) {
+  try {
+    const body = await req.json()
+
+    const controller = new AbortController()
+    const timer = setTimeout(() => controller.abort(), TIMEOUT_MS)
+
+    const res = await fetch(`${API_BASE}/exercise/`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+      signal: controller.signal,
+    })
+    clearTimeout(timer)
+
+    if (!res.ok) {
+      return NextResponse.json({ error: "Exercise API error" }, { status: res.status })
+    }
+
+    const data = await res.json()
+    return NextResponse.json(data)
+  } catch {
+    return NextResponse.json(
+      { error: "Exercise service unavailable" },
+      { status: 503 }
+    )
+  }
+}

frontend/app/api/nutrition/route.ts

@@ -0,0 +1,52 @@
+// POST /api/nutrition
+// Proxies to backend POST /nutrition/
+// Accepts: { dietary_flags, allergy_flags, vegetarian }
+
+import { NextRequest, NextResponse } from "next/server"
+
+const API_BASE = process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:8000"
+const TIMEOUT_MS = 10_000
+
+export async function POST(req: NextRequest) {
+  try {
+    const body = await req.json()
+
+    const controller = new AbortController()
+    const timer = setTimeout(() => controller.abort(), TIMEOUT_MS)
+
+    const res = await fetch(`${API_BASE}/nutrition/`, {
+      method: "POST",
+      headers: { "Content-Type": "application/json" },
+      body: JSON.stringify(body),
+      signal: controller.signal,
+    })
+    clearTimeout(timer)
+
+    if (!res.ok) {
+      // Try fallback
+      const fallback = await fetch(`${API_BASE}/nutrition/fallback`)
+      if (fallback.ok) {
+        const data = await fallback.json()
+        return NextResponse.json(data)
+      }
+      return NextResponse.json({ error: "Nutrition API error" }, { status: res.status })
+    }
+
+    const data = await res.json()
+    return NextResponse.json(data)
+  } catch {
+    // Try fallback on timeout/error
+    try {
+      const fallback = await fetch(`${API_BASE}/nutrition/fallback`)
+      if (fallback.ok) {
+        const data = await fallback.json()
+        return NextResponse.json(data)
+      }
+    } catch { /* ignore */ }
+
+    return NextResponse.json(
+      { error: "Nutrition service unavailable" },
+      { status: 503 }
+    )
+  }
+}

frontend/app/exercise/page.tsx

@@ -67,7 +67,7 @@ export default function ExercisePage() {
   const [selectedDay, setSelectedDay]     = useState<string | null>(null);
   const [completedDays, setCompletedDays] = useState<Set<string>>(new Set());
 
-  const API_BASE = process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:8000";
+
   const severity = latestReport?.severity_level ?? "MILD_CONCERN";
 
   useEffect(() => {
@@ -80,8 +80,8 @@ export default function ExercisePage() {
     const fetchPlan = async () => {
       try {
         setLoading(true);
-        // Backend expects POST /exercise/ with empty body (stub)
-        const res = await fetch(`${API_BASE}/exercise/`, {
+        // Call Next.js API route which proxies to backend
+        const res = await fetch(`/api/exercise`, {
           method: "POST",
           headers: { "Content-Type": "application/json" },
           body: JSON.stringify({}),
@@ -107,7 +107,7 @@ export default function ExercisePage() {
       }
     };
     fetchPlan();
-  }, [exerciseLevel, severity, API_BASE, profile.language]);
+  }, [exerciseLevel, severity, profile.language]);
 
   const handleComplete = (day: string) => {
     if (completedDays.has(day)) return;

frontend/app/nutrition/page.tsx

@@ -82,7 +82,6 @@ export default function NutritionPage() {
   const [loggedToday, setLoggedToday] = useState<string[]>([]);
   const [activeCard, setActiveCard]   = useState<string | null>(null);
 
-  const API_BASE = process.env.NEXT_PUBLIC_API_URL ?? "http://localhost:8000";
   const flags    = nutritionProfile.deficiencies.join(",") || "INCREASE_IRON";
 
   useEffect(() => {
@@ -95,8 +94,8 @@ export default function NutritionPage() {
         setLoading(true);
         setError(false);
 
-        // Backend expects POST /nutrition/ with NutritionRequest JSON body
-        const res = await fetch(`${API_BASE}/nutrition/`, {
+        // Call Next.js API route which proxies to backend
+        const res = await fetch(`/api/nutrition`, {
           method: "POST",
           headers: { "Content-Type": "application/json" },
           body: JSON.stringify({
@@ -132,7 +131,11 @@ export default function NutritionPage() {
         setData(transformed);
       } catch {
         try {
-          const res = await fetch(`${API_BASE}/nutrition/fallback`);
+          const res = await fetch(`/api/nutrition`, {
+            method: "POST",
+            headers: { "Content-Type": "application/json" },
+            body: JSON.stringify({ dietary_flags: ["INCREASE_IRON"], vegetarian: true }),
+          });
           if (!res.ok) throw new Error();
           const json = await res.json();
 
@@ -163,7 +166,7 @@ export default function NutritionPage() {
       }
     };
     fetchNutrition();
-  }, [flags, API_BASE, profile.language, nutritionProfile.deficiencies]);
+  }, [flags, profile.language, nutritionProfile.deficiencies]);
 
   const handleAddToToday = (food: FoodItem) => {
     logFood(food.name_english);