minor fix

Backend/app/api/v1/endpoints/interview.py

@@ -2,21 +2,18 @@ import os
 import uvicorn
 from fastapi import APIRouter, HTTPException, Request
 from fastapi.middleware.cors import CORSMiddleware
-from pydantic import BaseModel
+from pydantic import BaseModel, Field
 from app.config import settings
 from vapi import Vapi
 from dotenv import load_dotenv
 
-# Load environment variables from .env file
 load_dotenv()
 
 router = APIRouter()
 
-
 # --- CONFIGURATION ---
 VAPI_PRIVATE_KEY = os.getenv("VAPI_PRIVATE_KEY")
 VAPI_ASSISTANT_ID = os.getenv("VAPI_ASSISTANT_ID")
-# The SERVER_URL MUST be set to your public ngrok HTTPS URL for external webhooks to work.
 SERVER_URL = os.getenv("SERVER_URL", "http://localhost:8000") 
 
 # Initialize Vapi Server SDK
@@ -26,13 +23,16 @@ except Exception as e:
     print(f"Vapi SDK Initialization Error: {e}")
     print("Ensure VAPI_PRIVATE_KEY is set in .env")
 
-
 # --- SCHEMAS ---
 class ConfigRequest(BaseModel):
     name: str
     job_role: str
     experience: str
     level: str = "Medium"
+    # Optional customizable fields
+    model_name: str = "gpt-4o" 
+    voice_provider: str = "11labs"
+    voice_id: str = "burt" 
 
 # --- ENDPOINTS ---
 
@@ -41,10 +41,10 @@ async def get_vapi_config(data: ConfigRequest):
     """
     Endpoint called by the Frontend to get the dynamically generated Assistant configuration.
     """
-    if VAPI_ASSISTANT_ID == "asst_11111111111111111111":
+    if not VAPI_ASSISTANT_ID:
         raise HTTPException(
             status_code=503, 
-            detail="VAPI_ASSISTANT_ID not configured in .env. Please set your ID."
+            detail="VAPI_ASSISTANT_ID not configured in .env."
         )
 
     try:
@@ -52,37 +52,92 @@ async def get_vapi_config(data: ConfigRequest):
         print(f"👤 User: {data.name}, Role: {data.job_role}, Exp: {data.experience}, Level: {data.level}")
 
         system_prompt = (
-            f"You are a strict technical interviewer. You are interviewing {data.name} for a {data.job_role} role. "
-            f"They have {data.experience} years of experience. "
-            f"The interview difficulty level is {data.level}. "
-            f"If the level is 'Hard', ask complex, multi-layered questions. "
-            f"If 'Medium', focus on standard industry concepts. "
-            f"Ask short, concise questions. Wait for their answer. Do not lecture. "
-            f"Start by asking them to introduce themselves."
+            f"You are the hiring manager at a tech company. You are conducting a strict 5-minute screening interview "
+            f"with {data.name} for a {data.job_role} role. The candidate has {data.experience} years of experience. "
+            f"The difficulty level is {data.level}. Your style is clear, concise, and professional. Do not lecture. "
+            "All questions must be relevant to the provided job role.\n\n"
+
+            "You MUST strictly follow this time-boxed interview flow:\n\n"
+
+            "1. **Introduction (0:00–0:30)**: You have already welcomed them. Wait for their confirmation to begin.\n\n"
+
+            "2. **Background Snapshot (0:30–1:30)**: Ask: "
+            "'Give me a 30–40 second overview of your background and the type of work you've done related to this role.'\n\n"
+
+            "3. **Technical Depth (1:30–2:30)**: Ask the candidate to choose one project relevant to the job role. "
+            "Ask: 'Pick one project you're proud of that aligns with this role. In 45 seconds, explain the problem, your approach, "
+            "tools/techniques used, and the business impact.'\n\n"
+
+            "4. **Core Skills Check (2:30–3:30)**: Inform them you will ask 3 rapid questions tailored to the job. "
+            "Generate **three crisp, role-specific skill checks** following this logic:\n"
+            "   - Question 1: A foundational concept essential to the role.\n"
+            "   - Question 2: A practical troubleshooting or decision-making question.\n"
+            "   - Question 3: A tool/framework/technology familiarity question.\n"
+            "Questions must be specific to the given job role.\n\n"
+
+            "5. **Practical Scenario (3:30–4:15)**: Generate **one short applied scenario** relevant to the role. "
+            "Ask the candidate to describe their high-level approach to solve it.\n\n"
+
+            "6. **Role & Communication Fit (4:15–4:45)**: Ask a communication-focused question, such as: "
+            "'This role requires cross-team collaboration. Can you give an example where you explained something complex "
+            "to a non-technical or differently-skilled stakeholder?'\n\n"
+
+            "7. **Wrap-Up (4:45–5:00)**: Say: 'Thank you. Any questions for me?' Then conclude: 'We’ll get back to you with next steps.'\n\n"
+
+            "**CRITICAL RULES:**\n"
+            "- Do NOT exceed the time-box for each segment.\n"
+            "- If their answers run long, politely interrupt and move forward.\n"
+            "- Keep your phrasing tight and professional.\n"
+            "- All generated questions MUST be directly relevant to the specified job role."
         )
+
+
         webhook_url = f"{SERVER_URL}/api/webhook"
-        
-        # 3. Construct the Overrides Payload
+
         assistant_overrides = {
+
             "model": {
                 "provider": "openai", 
-                "model": "gpt-4o-mini", # or "gpt-4", "gpt-3.5-turbo"
+                "model": data.model_name,
+                "temperature": 0.3, 
+                "maxTokens": 150,   
+                "emotionRecognitionEnabled": True, 
                 "messages": [
                     {"role": "system", "content": system_prompt}
                 ]
             },
+
+            "voice": {
+                "provider": data.voice_provider,
+                "voiceId": data.voice_id,
+                "speed": 1.1, 
+                "stability": 0.5
+            },
+
+            "firstMessage": f"Hi {data.name}, thanks for joining. I’m the Data Science Lead. This is a quick 5-minute screening to understand your background and fit. Shall we begin?",
+            "maxDurationSeconds": 360, 
+            "silenceTimeoutSeconds": 40, 
+            "backgroundSound": "office", 
+            "backgroundDenoisingEnabled": True,
+
+            "endCallPhrases": [
+                "Goodbye",
+                "Have a great day",
+                "We’ll get back to you with next steps",
+                "Thank you for your time"
+            ],
+
+            # --- SERVER ---
             "server": {
                 "url": webhook_url
             },
-            # ... keep metadata as is ...
             "metadata": {
                 "user_name": data.name,
                 "job_role": data.job_role,
-                "environment": "standalone-test"
+                "environment": "production_screening"
             }
         }
 
-        # 4. Return the necessary config to the frontend Web SDK
         return {
             "assistantId": VAPI_ASSISTANT_ID,
             "overrides": assistant_overrides
@@ -97,28 +152,39 @@ async def get_vapi_config(data: ConfigRequest):
 async def vapi_webhook_receiver(request: Request):
     """
     Endpoint that receives asynchronous events from Vapi's servers.
+    Saves transcripts to a local file.
     """
     payload = await request.json()
     message = payload.get("message", {})
+    call_id = payload.get("call", {}).get("id", "unknown_call")
     
-    # Log incoming transcripts in real-time
+    os.makedirs("transcripts", exist_ok=True)
+
     if message.get("type") == "transcript" and message.get("transcriptType") == "final":
-        print(f"🗣️ [Transcript] {message.get('role').upper()}: {message.get('transcript')}")
-        
-    # Log the final report (contains summary, full conversation, etc.)
+        transcript_text = message.get('transcript')
+        role = message.get('role', 'unknown')
+
+        try:
+            with open(f"transcripts/{call_id}.txt", "a", encoding="utf-8") as f:
+                f.write(f"{role.upper()}: {transcript_text}\n")
+            print(f"🗣️ [Saved] {role.upper()}: {transcript_text}")
+        except Exception as e:
+            print(f"❌ Error saving transcript: {e}")
+
     elif message.get("type") == "end-of-call-report":
         metadata = payload.get("assistant", {}).get("metadata", {})
+        summary = message.get('summary', 'N/A')
+        
+        # Save summary to the same file
+        try:
+            with open(f"transcripts/{call_id}.txt", "a", encoding="utf-8") as f:
+                f.write(f"\n--- SUMMARY ---\n{summary}\n")
+        except Exception as e:
+            print(f"❌ Error saving summary: {e}")
+
         print(f"\n--- 🏁 Call Ended Report ---")
-        print(f"  User: {metadata.get('user_name')}, Role: {metadata.get('job_role')}")
-        print(f"  Summary: {message.get('summary', 'N/A')}")
+        print(f"  User: {metadata.get('user_name')}")
+        print(f"  Summary: {summary}")
         print(f"---------------------------\n")
 
-    # Vapi expects a 200 OK response
-    return {"status": "ok"}
-
-@router.get("/")
-async def root():
-    return {"message": "Vapi Standalone Backend is running on port 8000."}
-
-if __name__ == "__main__":
-    uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
+    return {"status": "ok"}