Update services/geminiService.ts

services/geminiService.ts

@@ -3,18 +3,18 @@ import { PatientProfile, ClinicalVitals, AppMode, RiskAnalysisResult, ChatMessag
 
 // --- API KEY & CONFIG ---
 const getApiKey = () => {
-  try {
-    // @ts-ignore
-    if (typeof import.meta !== 'undefined' && import.meta.env && import.meta.env.VITE_API_KEY) {
-      // @ts-ignore
-      return import.meta.env.VITE_API_KEY;
-    }
-  } catch (e) {}
-  
-  if (typeof process !== 'undefined' && process.env && process.env.API_KEY) {
-    return process.env.API_KEY;
-  }
-  return '';
+try {
+// @ts-ignore
+if (typeof import.meta !== 'undefined' && import.meta.env && import.meta.env.VITE_API_KEY) {
+// @ts-ignore
+return import.meta.env.VITE_API_KEY;
+}
+} catch (e) {}
+
+if (typeof process !== 'undefined' && process.env && process.env.API_KEY) {
+return process.env.API_KEY;
+}
+return '';
 };
 
 // --- BACKEND CONFIGURATION ---
@@ -22,403 +22,397 @@ const getApiKey = () => {
 const PRIMARY_API_BASE = 'https://arshenoy-somai-backend.hf.space';
 
 // Secondary Backend (Media - Moondream/Whisper)
-// Defaults to primary unless VITE_MEDIA_API_URL is set (for split architecture)
+// If you create a new space, put its URL here (e.g. via VITE_MEDIA_API_URL env var),
+// otherwise it defaults to the primary one.
 const getMediaApiBase = () => {
-  try {
-    // @ts-ignore
-    if (typeof import.meta !== 'undefined' && import.meta.env && import.meta.env.VITE_MEDIA_API_URL) {
-      // @ts-ignore
-      return import.meta.env.VITE_MEDIA_API_URL;
-    }
-  } catch (e) {}
-  return PRIMARY_API_BASE;
+try {
+// @ts-ignore
+if (typeof import.meta !== 'undefined' && import.meta.env && import.meta.env.VITE_MEDIA_API_URL) {
+// @ts-ignore
+return import.meta.env.VITE_MEDIA_API_URL;
+}
+} catch (e) {}
+return PRIMARY_API_BASE;
 };
-
 const MEDIA_API_BASE = getMediaApiBase();
+
 const API_KEY = getApiKey();
 const ai = new GoogleGenAI({ apiKey: API_KEY });
 
 // --- TIERED MODEL STRATEGY ---
-const MODEL_TIER_1 = 'gemini-2.5-flash-lite'; 
-const MODEL_TIER_2 = 'gemini-2.5-flash'; 
+const MODEL_TIER_1 = 'gemini-2.5-flash-lite';
+const MODEL_TIER_2 = 'gemini-2.5-flash';
 const MODEL_TTS = 'gemini-2.5-flash-preview-tts';
 
 const cleanText = (text: string) => {
-  if (!text) return "";
-  return text.replace(/\*\*/g, '').replace(/###/g, '').replace(/\*/g, '-').trim();
+if (!text) return "";
+return text.replace(/\*\*/g, '').replace(/###/g, '').replace(/\*/g, '-').trim();
 };
 
 const compressImage = async (base64Str: string, maxWidth = 800): Promise<string> => {
-  return new Promise((resolve) => {
-    const img = new Image();
-    img.src = base64Str;
-    img.onload = () => {
-      const canvas = document.createElement('canvas');
-      let width = img.width;
-      let height = img.height;
-      if (width > maxWidth) {
-        height = (height * maxWidth) / width;
-        width = maxWidth;
-      }
-      canvas.width = width;
-      canvas.height = height;
-      const ctx = canvas.getContext('2d');
-      ctx?.drawImage(img, 0, 0, width, height);
-      resolve(canvas.toDataURL('image/jpeg', 0.7)); 
-    };
-    img.onerror = () => resolve(base64Str); 
-  });
+return new Promise((resolve) => {
+const img = new Image();
+img.src = base64Str;
+img.onload = () => {
+const canvas = document.createElement('canvas');
+let width = img.width;
+let height = img.height;
+
+if (width > maxWidth) {
+height = (height * maxWidth) / width;
+width = maxWidth;
+}
+
+canvas.width = width;
+canvas.height = height;
+const ctx = canvas.getContext('2d');
+ctx?.drawImage(img, 0, 0, width, height);
+resolve(canvas.toDataURL('image/jpeg', 0.7));
+};
+img.onerror = () => resolve(base64Str);
+});
 };
 
 export const wakeUpBackend = async () => {
-  try {
-    // Ping both potential backends to warm them up
-    fetch(`${PRIMARY_API_BASE}/`, { method: 'GET', mode: 'cors' }).catch(()=>{});
-    if (PRIMARY_API_BASE !== MEDIA_API_BASE) {
-      fetch(`${MEDIA_API_BASE}/`, { method: 'GET', mode: 'cors' }).catch(()=>{});
-    }
-  } catch (e) {}
+try {
+// Ping both potential backends
+fetch(`${PRIMARY_API_BASE}/`, { method: 'GET', mode: 'cors' }).catch(()=>{});
+if (PRIMARY_API_BASE !== MEDIA_API_BASE) {
+fetch(`${MEDIA_API_BASE}/`, { method: 'GET', mode: 'cors' }).catch(()=>{});
+}
+} catch (e) {}
 };
 
-// Generic Fallback Caller with Status Updates
+// Generic Fallback Caller
 const callBackend = async (baseUrl: string, endpoint: string, payload: any, onStatus?: (msg: string) => void): Promise<string> => {
-  const url = `${baseUrl}${endpoint}`;
-  console.info(`[SomAI] Calling Backend: ${url}`);
-  if (onStatus) onStatus("🐢 Switching to local backup...");
+const url = `${baseUrl}${endpoint}`;
+console.info(`[SomAI] Calling Backend: ${url}`);
+if (onStatus) onStatus("🐢 Switching to local backup...");
+
+const makeRequest = async (retries = 2) => {
+const controller = new AbortController();
+const timeoutId = setTimeout(() => controller.abort(), 90000); // 90s timeout for CPU
+
+try {
+  const response = await fetch(url, {
+    method: 'POST',
+    mode: 'cors', 
+    credentials: 'omit',
+    headers: { 'Content-Type': 'application/json', 'Accept': 'application/json' },
+    body: JSON.stringify(payload),
+    signal: controller.signal
+  });
+  clearTimeout(timeoutId);
+
+  if (!response.ok && (response.status === 503 || response.status === 504) && retries > 0) {
+    if (onStatus) onStatus(`💤 Backend waking up... (${retries} retries left)`);
+    await new Promise(r => setTimeout(r, 5000));
+    return makeRequest(retries - 1);
+  }
+
+  if (!response.ok) {
+     const err = await response.text().catch(() => "Unknown");
+     throw new Error(`API Error ${response.status}: ${err.substring(0, 50)}`);
+  }
+
+  const data = await response.json();
   
-  const makeRequest = async (retries = 2) => {
-    const controller = new AbortController();
-    const timeoutId = setTimeout(() => controller.abort(), 90000); // 90s timeout for CPU
-    
-    try {
-      const response = await fetch(url, {
-        method: 'POST',
-        mode: 'cors', 
-        credentials: 'omit',
-        headers: { 'Content-Type': 'application/json', 'Accept': 'application/json' },
-        body: JSON.stringify(payload),
-        signal: controller.signal
-      });
-      clearTimeout(timeoutId);
-
-      if (!response.ok && (response.status === 503 || response.status === 504) && retries > 0) {
-        if (onStatus) onStatus(`💤 Backend waking up... (${retries} retries left)`);
-        await new Promise(r => setTimeout(r, 5000));
-        return makeRequest(retries - 1);
-      }
-
-      if (!response.ok) {
-         const err = await response.text().catch(() => "Unknown");
-         throw new Error(`API Error ${response.status}: ${err.substring(0, 50)}`);
-      }
-
-      const data = await response.json();
-      
-      if (typeof data === 'string') return data;
-      if (data.text) return data.text;
-      if (data.response) return data.response;
-      if (data.generated_text) return data.generated_text;
-      
-      return JSON.stringify(data);
-
-    } catch (error: any) {
-      clearTimeout(timeoutId);
-      if (retries > 0 && (error.name === 'AbortError' || error.message.includes('Failed'))) {
-         if (onStatus) onStatus("📡 Connection unstable, retrying...");
-         await new Promise(r => setTimeout(r, 5000));
-         return makeRequest(retries - 1);
-      }
-      throw error;
-    }
-  };
+  if (typeof data === 'string') return data;
+  if (data.text) return data.text;
+  if (data.response) return data.response;
+  if (data.generated_text) return data.generated_text;
   
-  try { return await makeRequest(); } catch (error) { throw error; }
+  return JSON.stringify(data);
+
+} catch (error: any) {
+  clearTimeout(timeoutId);
+  if (retries > 0 && (error.name === 'AbortError' || error.message.includes('Failed'))) {
+     if (onStatus) onStatus("📡 Connection unstable, retrying...");
+     await new Promise(r => setTimeout(r, 5000));
+     return makeRequest(retries - 1);
+  }
+  throw error;
+}
+};
+
+try { return await makeRequest(); } catch (error) { throw error; }
 };
 
 const parseRiskResponse = (text: string, calculatedScore: number): RiskAnalysisResult => {
-  try {
-    let jsonStr = text;
-    const codeBlockMatch = text.match(/```json\s*(\{[\s\S]*?\})\s*```/);
-    if (codeBlockMatch) jsonStr = codeBlockMatch[1];
-    else {
-      const braceMatch = text.match(/\{[\s\S]*\}/);
-      if (braceMatch) jsonStr = braceMatch[0];
-    }
-    const data = JSON.parse(jsonStr);
-    const pipeline = [
-      { code: data.primaryConditionCode?.code || "N/A", description: data.primaryConditionCode?.description || "Unknown", type: 'Primary' },
-      ...(data.historyCodes || []).map((h: any) => ({ code: h.code, description: h.description, type: 'History' }))
-    ];
-    return {
-      numericScore: calculatedScore,
-      summary: cleanText(data.summary || "Analysis completed."),
-      actionItems: (data.actionItems || []).map(cleanText),
-      icd10Codes: [], 
-      codingPipeline: pipeline as any,
-      insuranceNote: cleanText(data.insuranceNote || "Review required."),
-      timestamp: new Date().toISOString()
-    };
-  } catch (e) {
-    return {
-        numericScore: calculatedScore,
-        summary: cleanText(text).substring(0, 500) || "Analysis currently unavailable.",
-        actionItems: ["Review inputs", "Consult provider"],
-        icd10Codes: [],
-        codingPipeline: [],
-        insuranceNote: "Automated analysis fallback.",
-        timestamp: new Date().toISOString()
-    }
-  }
+try {
+let jsonStr = text;
+const codeBlockMatch = text.match(/json\s*(\{[\s\S]*?\})\s*/);
+if (codeBlockMatch) jsonStr = codeBlockMatch[1];
+else {
+const braceMatch = text.match(/{[\s\S]*}/);
+if (braceMatch) jsonStr = braceMatch[0];
+}
+
+const data = JSON.parse(jsonStr);
+
+const pipeline = [
+{ code: data.primaryConditionCode?.code || "N/A", description: data.primaryConditionCode?.description || "Unknown", type: 'Primary' },
+...(data.historyCodes || []).map((h: any) => ({ code: h.code, description: h.description, type: 'History' }))
+];
+
+return {
+numericScore: calculatedScore,
+summary: cleanText(data.summary || "Analysis completed."),
+actionItems: (data.actionItems || []).map(cleanText),
+icd10Codes: [],
+codingPipeline: pipeline as any,
+insuranceNote: cleanText(data.insuranceNote || "Review required."),
+timestamp: new Date().toISOString()
+};
+} catch (e) {
+return {
+numericScore: calculatedScore,
+summary: cleanText(text).substring(0, 500) || "Analysis currently unavailable.",
+actionItems: ["Review inputs", "Consult provider"],
+icd10Codes: [],
+codingPipeline: [],
+insuranceNote: "Automated analysis fallback.",
+timestamp: new Date().toISOString()
+}
+}
 };
 
 // --- VISION EXTRACTION ---
 export const extractClinicalData = async (imageBase64: string, onStatus?: (msg: string) => void): Promise<ExtractionResult> => {
-  const base64Data = imageBase64.includes('base64,') ? imageBase64.split('base64,')[1] : imageBase64;
-  const prompt = `Analyze this medical document. CRITICAL: Look for Patient Name. Extract JSON: { name, age, condition, history, allergies, systolicBp, glucose, heartRate, weight, temperature, spo2, clinicalNote }. Return JSON only.`;
-  
-  const callGeminiVision = async (modelName: string) => {
-    if (onStatus) onStatus(`⚡ Scanning with ${modelName}...`);
-    const response = await ai.models.generateContent({
-      model: modelName,
-      contents: [{ role: 'user', parts: [{ text: prompt }, { inlineData: { mimeType: 'image/jpeg', data: base64Data } }] }],
-      config: { responseMimeType: "application/json", maxOutputTokens: 2000 }
-    });
-    const text = response.text || "{}";
-    const data = JSON.parse(text);
-    return {
-      profile: { name: data.name, age: data.age, condition: data.condition, history: data.history, allergies: data.allergies },
-      vitals: { systolicBp: data.systolicBp, glucose: data.glucose, heartRate: data.heartRate, weight: data.weight, temperature: data.temperature, spo2: data.spo2, clinicalNote: data.clinicalNote },
-      confidence: 0.9
-    };
-  };
-
-  try {
-    if (!API_KEY) throw new Error("API Key missing");
-    return await callGeminiVision(MODEL_TIER_1);
-  } catch (e: any) { 
-    if (e.toString().includes('429') || e.toString().includes('Quota')) {
-        try {
-            return await callGeminiVision(MODEL_TIER_2);
-        } catch (e2) {}
-    }
+const base64Data = imageBase64.includes('base64,') ? imageBase64.split('base64,')[1] : imageBase64;
+const prompt = `Analyze this medical document. CRITICAL: Look for Patient Name. Extract JSON: { name, age, condition, history, allergies, systolicBp, glucose, heartRate, weight, temperature, spo2, clinicalNote }. Return JSON only.`;
+
+const callGeminiVision = async (modelName: string) => {
+if (onStatus) onStatus(`⚡ Scanning with ${modelName}...`);
+const response = await ai.models.generateContent({
+model: modelName,
+contents: [{ role: 'user', parts: [{ text: prompt }, { inlineData: { mimeType: 'image/jpeg', data: base64Data } }] }],
+config: { responseMimeType: "application/json", maxOutputTokens: 2000 }
+});
+const text = response.text || "{}";
+const data = JSON.parse(text);
+return {
+profile: { name: data.name, age: data.age, condition: data.condition, history: data.history, allergies: data.allergies },
+vitals: { systolicBp: data.systolicBp, glucose: data.glucose, heartRate: data.heartRate, weight: data.weight, temperature: data.temperature, spo2: data.spo2, clinicalNote: data.clinicalNote },
+confidence: 0.9
+};
+};
 
-    // Fallback: Moondream on Media Backend
-    try {
-        if (onStatus) onStatus("🐢 Compressing for Moondream...");
-        const compressedBase64 = await compressImage(imageBase64);
-        const cleanBase64 = compressedBase64.includes('base64,') ? compressedBase64.split('base64,')[1] : compressedBase64;
-        
-        if (onStatus) onStatus("🐢 Using Local Vision Node...");
-        const resText = await callBackend(MEDIA_API_BASE, '/vision', { image: cleanBase64, prompt: "Extract patient name and vitals from this document." }, onStatus);
-        
-        return {
-            profile: {}, 
-            vitals: { clinicalNote: `[Auto-Scanned]: ${resText}` },
-            confidence: 0.6
-        }
-    } catch (fallbackError) {
-        throw new Error("Scan failed. Please type details manually."); 
+try {
+if (!API_KEY) throw new Error("API Key missing");
+return await callGeminiVision(MODEL_TIER_1);
+} catch (e: any) {
+if (e.toString().includes('429') || e.toString().includes('Quota')) {
+try {
+return await callGeminiVision(MODEL_TIER_2);
+} catch (e2) {}
+}
+
+// Fallback: Moondream on Media Backend
+try {
+    if (onStatus) onStatus("🐢 Compressing for Moondream...");
+    const compressedBase64 = await compressImage(imageBase64);
+    const cleanBase64 = compressedBase64.includes('base64,') ? compressedBase64.split('base64,')[1] : compressedBase64;
+    
+    if (onStatus) onStatus("🐢 Using Local Vision Node...");
+    const resText = await callBackend(MEDIA_API_BASE, '/vision', { image: cleanBase64, prompt: "Extract patient name and vitals from this document." }, onStatus);
+    
+    return {
+        profile: {}, 
+        vitals: { clinicalNote: `[Auto-Scanned]: ${resText}` },
+        confidence: 0.6
     }
-  }
+} catch (fallbackError) {
+    throw new Error("Scan failed. Please type details manually."); 
+}
+}
 };
 
 export const generateSpeech = async (text: string): Promise<string | null> => {
-  if (!API_KEY) return null;
-  try {
-    const response = await ai.models.generateContent({
-      model: MODEL_TTS,
-      contents: [{ parts: [{ text }] }],
-      config: {
-        responseModalities: ['AUDIO'],
-        speechConfig: { voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Fenrir' } } },
-      },
-    });
-    return response.candidates?.[0]?.content?.parts?.[0]?.inlineData?.data || null;
-  } catch (e) {
-    return null;
-  }
+if (!API_KEY) return null;
+try {
+const response = await ai.models.generateContent({
+model: MODEL_TTS,
+contents: [{ parts: [{ text }] }],
+config: {
+responseModalities: ['AUDIO'],
+speechConfig: { voiceConfig: { prebuiltVoiceConfig: { voiceName: 'Fenrir' } } },
+},
+});
+return response.candidates?.[0]?.content?.parts?.[0]?.inlineData?.data || null;
+} catch (e) {
+return null;
+}
 };
 
 export const transcribeAudio = async (audioBlob: Blob): Promise<string> => {
-    const reader = new FileReader();
-    return new Promise((resolve, reject) => {
-        reader.onloadend = async () => {
-            const base64 = (reader.result as string).split(',')[1];
-            try {
-                // Whisper calls go to Media Backend
-                const text = await callBackend(MEDIA_API_BASE, '/transcribe', { audio: base64 });
-                resolve(text);
-            } catch (e) { reject("Voice transcription failed."); }
-        };
-        reader.readAsDataURL(audioBlob);
-    });
+const reader = new FileReader();
+return new Promise((resolve, reject) => {
+reader.onloadend = async () => {
+const base64 = (reader.result as string).split(',')[1];
+try {
+// Whisper calls go to Media Backend
+const text = await callBackend(MEDIA_API_BASE, '/transcribe', { audio: base64 });
+resolve(text);
+} catch (e) { reject("Voice transcription failed."); }
+};
+reader.readAsDataURL(audioBlob);
+});
 };
 
 // --- RISK ANALYSIS ---
 export const analyzeRisk = async (
-  profile: PatientProfile, 
-  vitals: ClinicalVitals, 
-  calculatedScore: number,
-  onStatus?: (msg: string) => void
+profile: PatientProfile,
+vitals: ClinicalVitals,
+calculatedScore: number,
+onStatus?: (msg: string) => void
 ): Promise<RiskAnalysisResult> => {
-  const prompt = `
-    Act as a Senior Clinical Risk Assessor.
-    Patient: ${profile.name} (${profile.age}, ${profile.gender}). Condition: ${profile.condition}.
-    History: ${profile.history}. 
-    Vitals: BP ${vitals.systolicBp}, Glucose ${vitals.glucose}, SpO2 ${vitals.spo2}%.
-    Note: ${vitals.clinicalNote}.
-    Task: 1. Summary. 2. 3 Action Items. 3. ICD-10 Pipeline (Condition, History). 4. Insurance Note.
-    Return JSON.
-  `;
-
-  const callGeminiRisk = async (modelName: string) => {
-    if (onStatus) onStatus(`⚡ Analyzing with ${modelName}...`);
-    const response = await ai.models.generateContent({
-        model: modelName,
-        contents: prompt,
-        config: {
-            responseMimeType: "application/json",
-            maxOutputTokens: 4000,
-            responseSchema: {
-            type: Type.OBJECT,
-            properties: {
-                summary: { type: Type.STRING },
-                actionItems: { type: Type.ARRAY, items: { type: Type.STRING } },
-                primaryConditionCode: { type: Type.OBJECT, properties: { code: {type: Type.STRING}, description: {type: Type.STRING} } },
-                historyCodes: { type: Type.ARRAY, items: { type: Type.OBJECT, properties: { code: {type: Type.STRING}, description: {type: Type.STRING} } } },
-                insuranceNote: { type: Type.STRING }
-            },
-            required: ["summary", "actionItems", "primaryConditionCode", "historyCodes", "insuranceNote"]
-            }
-        }
-    });
-    return { ...parseRiskResponse(response.text || "{}", calculatedScore), source: modelName === MODEL_TIER_1 ? 'Gemini 2.5 Flash-Lite' : 'Gemini 2.5 Flash' };
-  };
-
-  try {
-    if (!API_KEY) throw new Error("API Key missing");
-    return await callGeminiRisk(MODEL_TIER_1);
-  } catch (err: any) {
-    if (err.toString().includes('429') || err.toString().includes('Quota')) {
-        try { return await callGeminiRisk(MODEL_TIER_2); } catch (e2) {}
-    }
+const prompt = `Act as a Senior Clinical Risk Assessor. Patient: ${profile.name} (${profile.age}, ${profile.gender}). Condition: ${profile.condition}. History: ${profile.history}. Vitals: BP ${vitals.systolicBp}, Glucose ${vitals.glucose}, SpO2 ${vitals.spo2}%. Note: ${vitals.clinicalNote}. Task: 1. Summary. 2. 3 Action Items. 3. ICD-10 Pipeline (Condition, History). 4. Insurance Note. Return JSON.`;
+
+const callGeminiRisk = async (modelName: string) => {
+if (onStatus) onStatus(`⚡ Analyzing with ${modelName}...`);
+const response = await ai.models.generateContent({
+model: modelName,
+contents: prompt,
+config: {
+responseMimeType: "application/json",
+maxOutputTokens: 4000,
+responseSchema: {
+type: Type.OBJECT,
+properties: {
+summary: { type: Type.STRING },
+actionItems: { type: Type.ARRAY, items: { type: Type.STRING } },
+primaryConditionCode: { type: Type.OBJECT, properties: { code: {type: Type.STRING}, description: {type: Type.STRING} } },
+historyCodes: { type: Type.ARRAY, items: { type: Type.OBJECT, properties: { code: {type: Type.STRING}, description: {type: Type.STRING} } } },
+insuranceNote: { type: Type.STRING }
+},
+required: ["summary", "actionItems", "primaryConditionCode", "historyCodes", "insuranceNote"]
+}
+}
+});
+return { ...parseRiskResponse(response.text || "{}", calculatedScore), source: modelName === MODEL_TIER_1 ? 'Gemini 2.5 Flash-Lite' : 'Gemini 2.5 Flash' };
+};
 
-    try {
-      const payload = { ...profile, ...vitals, riskScore: calculatedScore, prompt };
-      // Fallback goes to Primary Backend (Text Node)
-      const fallback = await callBackend(PRIMARY_API_BASE, '/analyze', payload, onStatus);
-      return { 
-        ...parseRiskResponse(fallback, calculatedScore),
-        source: 'Phi-3 Mini (Fallback)' 
-      };
-    } catch {
-      throw new Error("Analysis failed");
-    }
-  }
+try {
+if (!API_KEY) throw new Error("API Key missing");
+return await callGeminiRisk(MODEL_TIER_1);
+} catch (err: any) {
+if (err.toString().includes('429') || err.toString().includes('Quota')) {
+try { return await callGeminiRisk(MODEL_TIER_2); } catch (e2) {}
+}
+
+try {
+  const payload = { ...profile, ...vitals, riskScore: calculatedScore, prompt };
+  // Fallback goes to Primary Backend (Text Node)
+  const fallback = await callBackend(PRIMARY_API_BASE, '/analyze', payload, onStatus);
+  return { 
+    ...parseRiskResponse(fallback, calculatedScore),
+    source: 'Phi-3 Mini (Fallback)' 
+  };
+} catch {
+  throw new Error("Analysis failed");
+}
+}
 };
 
 export const generateHealthInsights = async (profile: PatientProfile, vitals: ClinicalVitals): Promise<HealthInsights> => {
-  const prompt = `Based on Patient: ${profile.name}, ${profile.age}y, ${profile.condition}. Vitals: BP ${vitals.systolicBp}. Generate JSON: { weeklySummary, progress, tips: [] }.`;
-  
-  const callGeminiInsights = async (model: string) => {
-    const response = await ai.models.generateContent({
-      model: model,
-      contents: prompt,
-      config: { responseMimeType: "application/json", maxOutputTokens: 2000 }
-    });
-    return JSON.parse(response.text || "{}");
-  }
-
-  try {
-    if (!API_KEY) throw new Error("No Key");
-    return await callGeminiInsights(MODEL_TIER_1);
-  } catch (err: any) {
-    if (err.toString().includes('429')) {
-        try { return await callGeminiInsights(MODEL_TIER_2); } catch (e) {}
-    }
-    return { weeklySummary: "Keep tracking your vitals.", progress: "Data accumulated.", tips: ["Maintain a balanced diet.", "Stay hydrated."] };
-  }
+const prompt = `Based on Patient: ${profile.name}, ${profile.age}y, ${profile.condition}. Vitals: BP ${vitals.systolicBp}. Generate JSON: { weeklySummary, progress, tips: [] }.`;
+
+const callGeminiInsights = async (model: string) => {
+const response = await ai.models.generateContent({
+model: model,
+contents: prompt,
+config: { responseMimeType: "application/json", maxOutputTokens: 2000 }
+});
+return JSON.parse(response.text || "{}");
+}
+
+try {
+if (!API_KEY) throw new Error("No Key");
+return await callGeminiInsights(MODEL_TIER_1);
+} catch (err: any) {
+if (err.toString().includes('429')) {
+try { return await callGeminiInsights(MODEL_TIER_2); } catch (e) {}
+}
+return { weeklySummary: "Keep tracking your vitals.", progress: "Data accumulated.", tips: ["Maintain a balanced diet.", "Stay hydrated."] };
+}
 };
 
 export const generateSessionName = async (userText: string, aiText: string): Promise<string> => {
-  const prompt = `Generate a very short, specific title (max 4 words) for a medical chat session based on this context. User: ${userText}. AI: ${aiText}. Title:`;
-  try {
-    if (!API_KEY) return "New Consultation";
-    const response = await ai.models.generateContent({ model: MODEL_TIER_1, contents: prompt, config: { maxOutputTokens: 20 } });
-    return cleanText(response.text || "New Consultation").replace(/^["']|["']$/g, '');
-  } catch (e) {
-    return "New Consultation";
-  }
+const prompt = `Generate a very short, specific title (max 4 words) for a medical chat session based on this context. User: ${userText}. AI: ${aiText}. Title:`;
+try {
+if (!API_KEY) return "New Consultation";
+const response = await ai.models.generateContent({ model: MODEL_TIER_1, contents: prompt, config: { maxOutputTokens: 20 } });
+return cleanText(response.text || "New Consultation").replace(/^["']|["']$/g, '');
+} catch (e) {
+return "New Consultation";
+}
 };
 
 // --- CHAT ---
 export const generateChatResponse = async (
-  history: ChatMessage[], 
-  currentMessage: string, 
-  image: string | undefined, 
-  profile: PatientProfile, 
-  mode: AppMode,
-  onSource: (source: string) => void,
-  onStatus?: (msg: string) => void
+history: ChatMessage[],
+currentMessage: string,
+image: string | undefined,
+profile: PatientProfile,
+mode: AppMode,
+onSource: (source: string) => void,
+onStatus?: (msg: string) => void
 ): Promise<string> => {
-  const context = `
-    Patient: ${profile.name} (${profile.age}y).
-    Condition: ${profile.condition}. History: ${profile.history}.
-    Tone: ${mode === AppMode.THERAPY ? 'Empathetic CBT' : 'Medical Guide'}.
-    Format: Plain text. No markdown.
-  `;
-  
-  const contents = history.map(msg => ({ role: msg.role === 'user' ? 'user' : 'model', parts: [{ text: msg.text }, ...(msg.image ? [{ inlineData: { mimeType: 'image/jpeg', data: msg.image.split('base64,')[1] } }] : [])] }));
-  contents.push({ role: 'user', parts: [{ text: context + "\nUser: " + currentMessage }, ...(image ? [{ inlineData: { mimeType: 'image/jpeg', data: image.split('base64,')[1] } }] : [])] });
-
-  const callGeminiChat = async (modelName: string) => {
-    if (onStatus) onStatus(`Generating with ${modelName}...`);
-    onSource(modelName === MODEL_TIER_1 ? 'Gemini 2.5 Flash-Lite' : 'Gemini 2.5 Flash'); 
-    const response = await ai.models.generateContent({
-        model: modelName,
-        contents: contents,
-        config: { maxOutputTokens: 4000, temperature: 0.7 }
-    });
-    return cleanText(response.text || "I didn't catch that.");
-  };
-
-  try {
-    if (!API_KEY) throw new Error("No Key");
-    return await callGeminiChat(MODEL_TIER_1);
-  } catch (e: any) {
-    if (e.toString().includes('429') || e.toString().includes('Quota')) {
-        try {
-            return await callGeminiChat(MODEL_TIER_2);
-        } catch (e2) {}
-    }
+const context = `Patient: ${profile.name} (${profile.age}y). Condition: ${profile.condition}. History: ${profile.history}. Tone: ${mode === AppMode.THERAPY ? 'Empathetic CBT' : 'Medical Guide'}. Format: Plain text. No markdown.`;
+
+const contents = history.map(msg => ({ role: msg.role === 'user' ? 'user' : 'model', parts: [{ text: msg.text }, ...(msg.image ? [{ inlineData: { mimeType: 'image/jpeg', data: msg.image.split('base64,')[1] } }] : [])] }));
+contents.push({ role: 'user', parts: [{ text: context + "\nUser: " + currentMessage }, ...(image ? [{ inlineData: { mimeType: 'image/jpeg', data: image.split('base64,')[1] } }] : [])] });
+
+const callGeminiChat = async (modelName: string) => {
+if (onStatus) onStatus(`Generating with ${modelName}...`);
+onSource(modelName === MODEL_TIER_1 ? 'Gemini 2.5 Flash-Lite' : 'Gemini 2.5 Flash');
+const response = await ai.models.generateContent({
+model: modelName,
+contents: contents,
+config: { maxOutputTokens: 4000, temperature: 0.7 }
+});
+return cleanText(response.text || "I didn't catch that.");
+};
 
-    try {
-      if (onStatus) onStatus("Falling back to Local Phi-3...");
-      onSource('Phi-3 Mini (Fallback)'); 
-      const fallbackPrompt = `${context}\n\nChat History:\n${history.slice(-3).map(m => m.text).join('\n')}\nUser: ${currentMessage}`;
-      // Fallback goes to Primary Backend (Text Node)
-      const responseText = await callBackend(PRIMARY_API_BASE, '/generate', { prompt: fallbackPrompt }, onStatus);
-      return cleanText(responseText);
-    } catch { 
-        return "I'm having trouble connecting. Please check your internet.";
-    }
-  }
+try {
+if (!API_KEY) throw new Error("No Key");
+return await callGeminiChat(MODEL_TIER_1);
+} catch (e: any) {
+if (e.toString().includes('429') || e.toString().includes('Quota')) {
+try {
+return await callGeminiChat(MODEL_TIER_2);
+} catch (e2) {}
+}
+
+try {
+  if (onStatus) onStatus("Falling back to Local Phi-3...");
+  onSource('Phi-3 Mini (Fallback)'); 
+  const fallbackPrompt = `${context}\n\nChat History:\n${history.slice(-3).map(m => m.text).join('\n')}\nUser: ${currentMessage}`;
+  // Fallback goes to Primary Backend (Text Node)
+  const responseText = await callBackend(PRIMARY_API_BASE, '/generate', { prompt: fallbackPrompt }, onStatus);
+  return cleanText(responseText);
+} catch { 
+    return "I'm having trouble connecting. Please check your internet.";
+}
+}
 };
 
 export const generateQuickReplies = async (history: ChatMessage[]) => {
-  if (!API_KEY || history.length === 0) return [];
-  const recentContext = history.slice(-3).map(m => `${m.role}: ${m.text}`).join('\n');
-  const prompt = `Based on: ${recentContext}. Suggest 3 short follow-up questions. JSON array.`;
-  try {
-    const res = await ai.models.generateContent({ model: MODEL_TIER_1, contents: prompt, config: { responseMimeType: "application/json" } });
-    return JSON.parse(res.text || "[]");
-  } catch { return []; }
+if (!API_KEY || history.length === 0) return [];
+const recentContext = history.slice(-3).map(m => `${m.role}: ${m.text}`).join('\n');
+const prompt = `Based on: ${recentContext}. Suggest 3 short follow-up questions. JSON array.`;
+
+try {
+const res = await ai.models.generateContent({ model: MODEL_TIER_1, contents: prompt, config: { responseMimeType: "application/json" } });
+return JSON.parse(res.text || "[]");
+} catch { return []; }
 };
 
 export const summarizeConversation = async (history: ChatMessage[]) => {
-  if (!API_KEY) return "Summary unavailable.";
-  try {
-    const res = await ai.models.generateContent({ model: MODEL_TIER_1, contents: `Summarize:\n${history.map(m=>m.text).join('\n')}` });
-    return cleanText(res.text || "");
-  } catch { return "Could not summarize."; }
+if (!API_KEY) return "Summary unavailable.";
+try {
+const res = await ai.models.generateContent({ model: MODEL_TIER_1, contents: `Summarize:\n${history.map(m=>m.text).join('\n')}` });
+return cleanText(res.text || "");
+} catch { return "Could not summarize."; }
 };