feat: AI patient onboarding backend and documentation

2026-08-27 12:15:09 +05:30
parent b9440772d1
commit 59c1372fe3
49 changed files with 4012 additions and 0 deletions
@@ -0,0 +1,73 @@
+import json
+import base64
+from app.config import GEMINI_API_KEY, GEMINI_MODEL
+
+
+class GeminiClient:
+
+    def __init__(self):
+        self.api_key = GEMINI_API_KEY
+        self.model = GEMINI_MODEL
+
+    async def extract_from_document(
+        self,
+        file_bytes: bytes,
+        mime_type: str,
+        patient_context: dict,
+    ) -> dict:
+        if not self.api_key or self.api_key == "your_gemini_key":
+            return {
+                "document_type": "medical_report",
+                "extracted_text": "Sample HbA1c 7.2%, Blood Pressure 130/85 mmHg",
+                "findings": [
+                    {"test": "HbA1c", "value": "7.2", "unit": "%", "interpretation": "Elevated"},
+                    {"test": "Blood Pressure", "value": "130/85", "unit": "mmHg", "interpretation": "Stage 1"},
+                ],
+                "medications_found": ["Metformin 500mg"],
+                "allergies_found": [],
+                "summary": "Report indicates elevated HbA1c and prehypertension.",
+            }
+
+        try:
+            import google.generativeai as genai
+            genai.configure(api_key=self.api_key)
+            model = genai.GenerativeModel(self.model)
+
+            prompt = f"""
+You are a medical document extraction AI.
+Analyze this medical report / prescription / lab result for patient {patient_context.get('name', 'Patient')}.
+Extract all lab tests, numeric values, units, diagnosed conditions, medications, and allergies into structured JSON.
+
+Return ONLY JSON:
+{{
+    "document_type": "lab_report | prescription | discharge_summary | other",
+    "extracted_text": "brief summary text",
+    "findings": [
+        {{
+            "test": "test name",
+            "value": "numeric or text value",
+            "unit": "unit or null",
+            "interpretation": "Normal | High | Low | etc."
+        }}
+    ],
+    "medications_found": ["medication 1", "medication 2"],
+    "allergies_found": ["allergy 1"],
+    "summary": "short clinical summary of the document"
+}}
+"""
+            part = {
+                "mime_type": mime_type,
+                "data": file_bytes,
+            }
+            response = await model.generate_content_async([part, prompt])
+            cleaned_text = response.text.replace("```json", "").replace("```", "").strip()
+            return json.loads(cleaned_text)
+        except Exception as e:
+            print(f"[Gemini Extraction Error]: {e}")
+            return {
+                "document_type": "unknown",
+                "findings": [],
+                "medications_found": [],
+                "allergies_found": [],
+                "summary": f"Could not extract document details: {e}",
+            }