Create endpoints.py

endpoints.py

@@ -0,0 +1,242 @@
+from fastapi import Depends, HTTPException, UploadFile, File, Query, Form
+from fastapi.responses import StreamingResponse, JSONResponse
+from fastapi.encoders import jsonable_encoder
+from config import app, agent, logger
+from models import ChatRequest, VoiceOutputRequest, RiskLevel
+from auth import get_current_user
+from utils import clean_text_response
+from analysis import analyze_patient_report, analyze_all_patients
+from voice import recognize_speech, text_to_speech, extract_text_from_pdf
+from docx import Document
+import re
+import mimetypes
+from bson import ObjectId
+from datetime import datetime
+import asyncio
+
+@app.get("/status")
+async def status(current_user: dict = Depends(get_current_user)):
+    logger.info(f"Status endpoint accessed by {current_user['email']}")
+    return {
+        "status": "running",
+        "timestamp": datetime.utcnow().isoformat(),
+        "version": "2.6.0",
+        "features": ["chat", "voice-input", "voice-output", "patient-analysis", "report-upload"]
+    }
+
+@app.get("/patients/analysis-results")
+async def get_patient_analysis_results(
+    name: Optional[str] = Query(None),
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Fetching analysis results by {current_user['email']}")
+    try:
+        query = {}
+        if name:
+            name_regex = re.compile(name, re.IGNORECASE)
+            matching_patients = await patients_collection.find({"full_name": name_regex}).to_list(length=None)
+            patient_ids = [p["fhir_id"] for p in matching_patients if "fhir_id" in p]
+            if not patient_ids:
+                return []
+            query = {"patient_id": {"$in": patient_ids}}
+
+        analyses = await analysis_collection.find(query).sort("timestamp", -1).to_list(length=100)
+        enriched_results = []
+        for analysis in analyses:
+            patient = await patients_collection.find_one({"fhir_id": analysis.get("patient_id")})
+            if patient:
+                analysis["full_name"] = patient.get("full_name", "Unknown")
+            analysis["_id"] = str(analysis["_id"])
+            enriched_results.append(analysis)
+
+        return enriched_results
+
+    except Exception as e:
+        logger.error(f"Error fetching analysis results: {e}")
+        raise HTTPException(status_code=500, detail="Failed to retrieve analysis results")
+
+@app.post("/chat-stream")
+async def chat_stream_endpoint(
+    request: ChatRequest,
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Chat stream initiated by {current_user['email']}")
+    async def token_stream():
+        try:
+            conversation = [{"role": "system", "content": agent.chat_prompt}]
+            if request.history:
+                conversation.extend(request.history)
+            conversation.append({"role": "user", "content": request.message})
+
+            input_ids = agent.tokenizer.apply_chat_template(
+                conversation, add_generation_prompt=True, return_tensors="pt"
+            ).to(agent.device)
+
+            output = agent.model.generate(
+                input_ids,
+                do_sample=True,
+                temperature=request.temperature,
+                max_new_tokens=request.max_new_tokens,
+                pad_token_id=agent.tokenizer.eos_token_id,
+                return_dict_in_generate=True
+            )
+
+            text = agent.tokenizer.decode(output["sequences"][0][input_ids.shape[1]:], skip_special_tokens=True)
+            for chunk in text.split():
+                yield chunk + " "
+                await asyncio.sleep(0.05)
+        except Exception as e:
+            logger.error(f"Streaming error: {e}")
+            yield f"⚠️ Error: {e}"
+
+    return StreamingResponse(token_stream(), media_type="text/plain")
+
+@app.post("/voice/transcribe")
+async def transcribe_voice(
+    audio: UploadFile = File(...),
+    language: str = Query("en-US", description="Language code for speech recognition"),
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Voice transcription initiated by {current_user['email']}")
+    try:
+        audio_data = await audio.read()
+        if not audio.filename.lower().endswith(('.wav', '.mp3', '.ogg', '.flac')):
+            raise HTTPException(status_code=400, detail="Unsupported audio format")
+        
+        text = recognize_speech(audio_data, language)
+        return {"text": text}
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in voice transcription: {e}")
+        raise HTTPException(status_code=500, detail="Error processing voice input")
+
+@app.post("/voice/synthesize")
+async def synthesize_voice(
+    request: VoiceOutputRequest,
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Voice synthesis initiated by {current_user['email']}")
+    try:
+        audio_data = text_to_speech(request.text, request.language, request.slow)
+        
+        if request.return_format == "base64":
+            return {"audio": base64.b64encode(audio_data).decode('utf-8')}
+        else:
+            return StreamingResponse(
+                io.BytesIO(audio_data),
+                media_type="audio/mpeg",
+                headers={"Content-Disposition": "attachment; filename=speech.mp3"}
+            )
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in voice synthesis: {e}")
+        raise HTTPException(status_code=500, detail="Error generating voice output")
+
+@app.post("/voice/chat")
+async def voice_chat_endpoint(
+    audio: UploadFile = File(...),
+    language: str = Query("en-US", description="Language code for speech recognition"),
+    temperature: float = Query(0.7, ge=0.1, le=1.0),
+    max_new_tokens: int = Query(512, ge=50, le=1024),
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Voice chat initiated by {current_user['email']}")
+    try:
+        audio_data = await audio.read()
+        user_message = recognize_speech(audio_data, language)
+        
+        chat_response = agent.chat(
+            message=user_message,
+            history=[],
+            temperature=temperature,
+            max_new_tokens=max_new_tokens
+        )
+        
+        audio_data = text_to_speech(chat_response, language.split('-')[0])
+        
+        return StreamingResponse(
+            io.BytesIO(audio_data),
+            media_type="audio/mpeg",
+            headers={"Content-Disposition": "attachment; filename=response.mp3"}
+        )
+    
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in voice chat: {e}")
+        raise HTTPException(status_code=500, detail="Error processing voice chat")
+
+@app.post("/analyze-report")
+async def analyze_clinical_report(
+    file: UploadFile = File(...),
+    patient_id: Optional[str] = Form(None),
+    temperature: float = Form(0.5),
+    max_new_tokens: int = Form(1024),
+    current_user: dict = Depends(get_current_user)
+):
+    logger.info(f"Report analysis initiated by {current_user['email']}")
+    try:
+        content_type = file.content_type
+        allowed_types = [
+            'application/pdf',
+            'text/plain',
+            'application/vnd.openxmlformats-officedocument.wordprocessingml.document'
+        ]
+
+        if content_type not in allowed_types:
+            raise HTTPException(
+                status_code=400,
+                detail=f"Unsupported file type: {content_type}. Supported types: PDF, TXT, DOCX"
+            )
+
+        file_content = await file.read()
+
+        if content_type == 'application/pdf':
+            text = extract_text_from_pdf(file_content)
+        elif content_type == 'text/plain':
+            text = file_content.decode('utf-8')
+        elif content_type == 'application/vnd.openxmlformats-officedocument.wordprocessingml.document':
+            doc = Document(io.BytesIO(file_content))
+            text = "\n".join([para.text for para in doc.paragraphs])
+        else:
+            raise HTTPException(status_code=400, detail="Unsupported file type")
+
+        text = clean_text_response(text)
+        if len(text.strip()) < 50:
+            raise HTTPException(
+                status_code=400,
+                detail="Extracted text is too short (minimum 50 characters required)"
+            )
+
+        analysis = await analyze_patient_report(
+            patient_id=patient_id,
+            report_content=text,
+            file_type=content_type,
+            file_content=file_content
+        )
+
+        if "_id" in analysis and isinstance(analysis["_id"], ObjectId):
+            analysis["_id"] = str(analysis["_id"])
+        if "timestamp" in analysis and isinstance(analysis["timestamp"], datetime):
+            analysis["timestamp"] = analysis["timestamp"].isoformat()
+
+        return JSONResponse(content=jsonable_encoder({
+            "status": "success",
+            "analysis": analysis,
+            "patient_id": patient_id,
+            "file_type": content_type,
+            "file_size": len(file_content)
+        }))
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"Error in report analysis: {str(e)}")
+        raise HTTPException(
+            status_code=500,
+            detail=f"Failed to analyze report: {str(e)}"
+        )