Upload folder using huggingface_hub

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+src/interview-assistant/data/interview_rag_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -1,36 +1,35 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    DEBIAN_FRONTEND=noninteractive \
-    PYTHONPATH=/app/src/interview-assistant:$PYTHONPATH
-
-WORKDIR /app
-
-# System deps
-RUN apt-get update && apt-get install -y \
-    git build-essential curl \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install uv
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/bin:$PATH"
-
-# Copy project metadata
-COPY pyproject.toml .
-COPY uv.lock .
-
-# Copy application code
-COPY common/ ./common/
-COPY src/interview-assistant/ ./src/interview-assistant/
-
-# Install dependencies using uv, then export and install with pip to system
-# We use --no-dev to exclude dev dependencies if any
-RUN uv sync --frozen --no-dev && \
-    uv pip install -e . --system
-
-# Copy entry point
-COPY run.py .
-
-EXPOSE 7860
-
-CMD ["python", "run.py", "interview-assistant", "--port", "7860"]
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    PYTHONPATH=/app:/app/common:$PYTHONPATH
+
+WORKDIR /app
+
+# System deps
+RUN apt-get update && apt-get install -y \
+    git build-essential curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy project metadata
+COPY pyproject.toml .
+COPY uv.lock .
+
+# Copy required folders
+COPY common/ ./common/
+COPY src/interview-assistant/ ./src/interview-assistant/
+
+# Install dependencies using uv, then export and install with pip to system
+RUN uv sync --frozen --no-dev && \
+    uv pip install -e . --system
+
+# Copy entry point
+COPY run.py .
+
+EXPOSE 7860
+
+CMD ["python", "run.py", "interview-assistant", "--port", "7860"]

README.md

@@ -65,4 +65,4 @@ docker build -t interviewer-assistant -f src/interviewer-assistant/Dockerfile .
 
 # Run
 docker run -p 7860:7860 interviewer-assistant
-```
+```

pyproject.toml

@@ -1,5 +1,5 @@
 [project]
-name = "agents"
+name = "agenticai"
 version = "0.1.0"
 description = "Agentic AI project"
 readme = "README.md"
@@ -47,7 +47,7 @@ dependencies = [
     # VECTOR DB / INDEXING
     # =======================
     "faiss-cpu>=1.13.0",
-    "chromadb==1.3.5",
+    "chromadb==1.4.1",
     "sentence-transformers>=5.1.2",
     "pymupdf",
     "pypdf>=6.3.0",
@@ -120,7 +120,7 @@ dependencies = [
     # MACHINE LEARNING
     # =======================
     "scikit-learn>=1.7.2",
-    "huggingface_hub<=1.1.4",
+    "huggingface_hub<=1.3.2",
     "datasets>=4.4.1",
 
     # =======================
@@ -143,6 +143,15 @@ dependencies = [
     "opentelemetry-sdk>=1.20.0",
     "opentelemetry-exporter-otlp>=1.20.0",
     "opentelemetry-api>=1.20.0",
+
+    # =======================
+    # Google Authentication
+    # =======================
+    "google-auth>=2.22.0",
+    "google-auth-oauthlib>=0.4.6",
+    "google-auth-httplib2>=0.1.0",
+    "autoflake>=1.5.0",
+
 ]
 
 [dependency-groups]
@@ -181,4 +190,4 @@ include = ["common*", "src*"]  # Treat 'common' and 'src' folders as packages
 # just like the apps do locally, preventing ModuleNotFoundError.
 pythonpath = ["src", "common"]
 testpaths = ["tests"]  # Only look for tests in the 'tests' directory
-addopts = "-q"  # Run in quiet mode (less verbose output)
+addopts = "-q"  # Run in quiet mode (less verbose output)

run.py

@@ -33,8 +33,13 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
         "entry": "app.py",
         "description": "Healthcare Assistant - Medical information with RAG and web search"
     },
-    "deep-research": {
-        "path": "src/deep-research",
+    "deep-research_v1": {
+        "path": "src/deep-research_v1",
+        "entry": "app.py",
+        "description": "Deep Research AI - Comprehensive research assistant"
+    },
+    "deep-research_v2": {
+        "path": "src/deep-research_v2",
         "entry": "app.py",
         "description": "Deep Research AI - Comprehensive research assistant"
     },
@@ -53,13 +58,23 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
         "entry": "main.py",
         "description": "Trip Planner - Detailed trip itinerary planning"
     },
-    "chatbot": {
-        "path": "src/chatbot",
+    "chatbot_v1": {
+        "path": "src/chatbot_v1",
         "entry": "app.py",
         "description": "General Chatbot - Multi-purpose conversational AI"
     },
-    "accessibility": {
-        "path": "src/accessibility",
+    "chatbot_v2": {
+        "path": "src/chatbot_v2",
+        "entry": "app.py",
+        "description": "Layered Chatbot (ReAct) - Advanced Architecture"
+    },
+    "accessibility_v2": {
+        "path": "src/accessibility_v2",
+        "entry": "app.py",
+        "description": "Accessibility Auditor V2 - Layered Architecture"
+    },
+    "accessibility_v1": {
+        "path": "src/accessibility_v1",
         "entry": "app.py",
         "description": "Accessibility Tools - Assistive technology applications"
     },
@@ -82,6 +97,11 @@ APP_REGISTRY: Dict[str, Dict[str, str]] = {
         "path": "src/interview-assistant",
         "entry": "app.py",
         "description": "Interview Assistant - Multi-agent interview tool"
+    },
+    "finadvisor": {
+        "path": "src/finadvisor",
+        "entry": "app.py",
+        "description": "Financial Advisor - Multi-agent financial advisor tool"
     }
 }
 

src/interview-assistant/Dockerfile

@@ -1,36 +1,35 @@
-FROM python:3.12-slim
-
-ENV PYTHONUNBUFFERED=1 \
-    DEBIAN_FRONTEND=noninteractive \
-    PYTHONPATH=/app/src/interview-assistant:$PYTHONPATH
-
-WORKDIR /app
-
-# System deps
-RUN apt-get update && apt-get install -y \
-    git build-essential curl \
-    && rm -rf /var/lib/apt/lists/*
-
-# Install uv
-RUN curl -LsSf https://astral.sh/uv/install.sh | sh
-ENV PATH="/root/.local/bin:$PATH"
-
-# Copy project metadata
-COPY pyproject.toml .
-COPY uv.lock .
-
-# Copy application code
-COPY common/ ./common/
-COPY src/interview-assistant/ ./src/interview-assistant/
-
-# Install dependencies using uv, then export and install with pip to system
-# We use --no-dev to exclude dev dependencies if any
-RUN uv sync --frozen --no-dev && \
-    uv pip install -e . --system
-
-# Copy entry point
-COPY run.py .
-
-EXPOSE 7860
-
-CMD ["python", "run.py", "interview-assistant", "--port", "7860"]
+FROM python:3.12-slim
+
+ENV PYTHONUNBUFFERED=1 \
+    DEBIAN_FRONTEND=noninteractive \
+    PYTHONPATH=/app:/app/common:$PYTHONPATH
+
+WORKDIR /app
+
+# System deps
+RUN apt-get update && apt-get install -y \
+    git build-essential curl \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install uv
+RUN curl -LsSf https://astral.sh/uv/install.sh | sh
+ENV PATH="/root/.local/bin:$PATH"
+
+# Copy project metadata
+COPY pyproject.toml .
+COPY uv.lock .
+
+# Copy required folders
+COPY common/ ./common/
+COPY src/interview-assistant/ ./src/interview-assistant/
+
+# Install dependencies using uv, then export and install with pip to system
+RUN uv sync --frozen --no-dev && \
+    uv pip install -e . --system
+
+# Copy entry point
+COPY run.py .
+
+EXPOSE 7860
+
+CMD ["python", "run.py", "interview-assistant", "--port", "7860"]

src/interview-assistant/README.md

@@ -65,4 +65,4 @@ docker build -t interviewer-assistant -f src/interviewer-assistant/Dockerfile .
 
 # Run
 docker run -p 7860:7860 interviewer-assistant
-```
+```

src/interview-assistant/agents/definitions.py

@@ -0,0 +1,166 @@
+from autogen_agentchat.agents import AssistantAgent
+from autogen_ext.models.openai import OpenAIChatCompletionClient
+import os
+from rag.db import get_db
+from dotenv import load_dotenv
+from tools.rag_tools import search_candidate_knowledge_base
+
+# Assuming agents/definitions.py is in src/interview-assistant/agents/, root is 3 levels up
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))))
+load_dotenv(os.path.join(ROOT_DIR, ".env"))
+
+# Shared Model Client
+# Shared Model Client
+# model_client = OpenAIChatCompletionClient(model="gpt-4")
+
+# Ollama Cloud
+api_key = os.getenv("OLLAMA_API_KEY") 
+print(f"[DEBUG] Loading Ollama Client on Ollama Cloud. Model: gemma3:4b")
+
+model_client = OpenAIChatCompletionClient(
+    model="gemma3:4b",
+    api_key=api_key,
+    base_url="https://ollama.com/v1",
+    model_info={
+        "vision": False,
+        "function_calling": True,
+        "json_output": True,
+        "family": "gemma"
+    },
+    extra_parameters={"max_tokens": 4096}
+)
+
+# --- Tools ---
+# Tools moved to src/interview-assistant/tools/rag_tools.py
+
+# --- Evaluation Agents ---
+
+def get_jd_summarizer():
+    return AssistantAgent(
+        name="JD_Summarizer",
+        model_client=model_client,
+        system_message="""You are an expert Talent Acquisition Specialist. 
+        Your task is to analyze the Job Description and extract the Critical Skills, Required Experience, and Nice-to-Have attributes. 
+        Output a concise summary bullet point list."""
+    )
+
+def get_resume_summarizer():
+    return AssistantAgent(
+        name="Resume_Summarizer",
+        model_client=model_client,
+        tools=[search_candidate_knowledge_base],
+        system_message="""You are a Researcher. Your goal is to find evidence in the candidate's resume that matches the JD requirements.
+        Use the `search_candidate_knowledge_base` tool to find skills and experience.
+        Summarize what the candidate HAS and what they are MISSING based on the evidence found."""
+    )
+
+def get_evaluator():
+    return AssistantAgent(
+        name="Evaluator",
+        model_client=model_client,
+        system_message="""You are the Lead Evaluator. 
+        You will receive the Job Description and the Candidate's Resume Context.
+        
+        TASK:
+        1. Compare the Candidate's skills/experience against the JD critical requirements.
+        2. Identify specific Matches and Gaps.
+        3. Assign a Fitness Score (0-10) using the SCORING RUBRIC below.
+        
+        SCORING RUBRIC (BE STRICT & CONSISTENT):
+        - 0-3 (Mismatch): Missing >50% of critical skills or wrong domain (e.g., Java dev for Python role).
+        - 4-6 (Partial): Has core skills but lacks seniority, specific framework exp, or key leadership traits.
+        - 7-8 (Good): Matches ~80% of requirements. Minor gaps (e.g., missing a nice-to-have).
+        - 9-10 (Perfect): 100% match + exceeds expectations (e.g., has bonus skills, higher seniority).
+        
+        OUTPUT:
+        Produce a draft analysis with:
+        - Score (Must align with Rubric)
+        - Top 3 Strengths (Matches - Be specific)
+        - Top 3 Weaknesses (Gaps - Be specific)
+        - Brief Summary
+        """
+    )
+
+def get_coordinator():
+    return AssistantAgent(
+        name="Coordinator",
+        model_client=model_client,
+        system_message="""You are the Quality Assurance Manager. Review the Evaluator's output.
+        
+        CRITICAL OUTPUT RULES:
+        1. You must output the Final Evaluation in STRICT JSON format.
+        2. The JSON must have these exact keys:
+           - "score": Integer (0-10)
+           - "key_matches": List[str] (Specific skills/experiences from JD found in Resume)
+           - "gaps": List[str] (Specific requirements missing)
+           - "summary": str (Brief reasoning)
+        
+        QUALITY CHECK:
+        - Ensure the 'summary' is insightful and not just a restatement.
+        - Ensure 'key_matches' and 'gaps' are specific, not generic.
+        
+        DECISION:
+        - If satisfied, output the valid JSON immediately followed by "EVALUATION_APPROVED".
+        - If NOT satisfied (e.g., vague analysis, missing JSON, unstructured), REJECT.
+           - Provide specific instructions to the Evaluator on what to fix.
+           - Do NOT output the termination keyword.
+        """
+    )
+
+# --- Interview Design Agents (Flow 3) ---
+
+def get_question_generator():
+    return AssistantAgent(
+        name="Question_Generator",
+        model_client=model_client,
+        system_message="""You are a Strategy-Driven Senior Interviewer.
+        
+        PHASE 1: STRATEGY
+        - Analyze the JD Role & Seniority (Junior vs Senior vs Architect vs Manager).
+        - Determine weights:
+           * Developer: Focus on Tech.
+           * Architect: Focus on System Design/Tech.
+           * Manager: Focus on Leadership/Behavioral.
+        
+        PHASE 2: GENERATION
+        - Generate exactly 10 Interview Questions based on the strategy.
+        
+        CRITICAL INSTRUCTION:
+        - Each Question MUST be clear and descriptive (approx. 50-100 words). 
+        - DO NOT ask simple one-liners. Use scenario-based questions or multi-part situational problems.
+        
+        ORGANIZATION:
+        - Group the questions by Category: Present all Technical questions first, then Leadership, then Behavioral.
+        
+        OUTPUT FORMAT:
+        Output the questions as a JSON List of Objects. Each object must have:
+        {
+          "category": "Technical|Leadership|Behavioral",
+          "u_id": int (1-10),
+          "question": "The detailed scenario-based question text (50-100 words)",
+          "complexity": "Low|Medium|High",
+          "likely_answer": "Key points expected in a good answer"
+        }
+        """
+    )
+
+def get_question_reviewer():
+    return AssistantAgent(
+        name="Question_Reviewer",
+        model_client=model_client,
+        system_message="""You are the Interview Board Chair. Review the generated questions.
+        
+        CHECKLIST:
+        1. Are there exactly 10 questions?
+        2. Do they cover the specific topics identified by the Strategist?
+        3. Are the questions SUFFICIENTLY DETAILED (50-100 words each)?
+        4. Are the 'likely_answer' keys provided and accurate?
+        5. Is the format valid JSON?
+        
+        DECISION:
+        - If satisfied (ALL checks pass), output the final JSON list and then write "GUIDE_APPROVED" on a new line.
+        - If NOT satisfied (e.g., questions are too short/simple, missing answer keys, wrong count, or not specific enough), REJECT.
+           - Provide specific, actionable feedback on what needs to change.
+           - Do NOT output the termination keyword.
+        """
+    )

src/interview-assistant/app.py

@@ -1,219 +1,639 @@
 import streamlit as st
 import os
 import sys
-import tempfile
-import asyncio
-import traceback
-import extra_streamlit_components as stx
+import sqlite3
+import shutil
+import pandas as pd
 from dotenv import load_dotenv
+import asyncio
+
+# --- Imports ---
+# Ensure local modules can be imported
+# Use insert(0) to prioritize local 'agents' folder over installed 'agents' package
+sys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))
+
+from rag.ingest import process_uploaded_files
+from rag.db import get_db
+# from agents.manager import get_agent_manager # Deprecated
+from teams.evaluation_team import run_evaluation_team
+import json
+import re
+
+# Load environment variables
+# Assuming app.py is in src/interview-assistant/, root is 2 levels up
+ROOT_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+load_dotenv(os.path.join(ROOT_DIR, ".env"))
 
-# Ensure we can import from local modules
-current_dir = os.path.dirname(os.path.abspath(__file__))
-if current_dir not in sys.path:
-    sys.path.append(current_dir)
-
-from teams.team import get_interview_team
-from ui.styles import apply_custom_styles
-from ui.hero import render_hero
-from ui.sidebar import render_sidebar
-from ui.report import generate_markdown_report, create_pdf, render_persistent_view
-from common.utility.autogen_model_factory import AutoGenModelFactory
-
-# Load env variables
-load_dotenv()
-
-st.set_page_config(page_title="Interviewer Assistant", page_icon="👔", layout="wide")
-
-# Cookie Manager (Must be initialized at top level)
-def get_manager():
-    return stx.CookieManager()
-
-cookie_manager = get_manager()
-
-# 1. Apply Styles
-apply_custom_styles()
-
-# 2. Render Sidebar
-sidebar_data = render_sidebar(cookie_manager)
-mode = sidebar_data["mode"]
-job_description = sidebar_data["job_description"]
-uploaded_resume = sidebar_data["uploaded_resume"]
-linkedin_url = sidebar_data["linkedin_url"]
-
-# 3. Render Hero
-render_hero()
-
-# ------------------------------------------------------------------------------
-# LOGIC & ANALYSIS HELPERS
-# ------------------------------------------------------------------------------
-
-async def run_analysis_stream(model_client, task_msg):
-    team = get_interview_team(model_client)
-    # Return the stream generator
-    stream = team.run_stream(task=task_msg)
-    return stream
-
-# ------------------------------------------------------------------------------
-# MAIN CONTENT LOGIC
-# ------------------------------------------------------------------------------
-
-if mode == "Candidate":
-    st.markdown("## 🎓 Candidate Prep Portal")
-    st.info("This feature is under development. It will allow candidates to take mock interviews based on the generated guide.")
-    st.image("https://cdn-icons-png.flaticon.com/512/3220/3220565.png", width=150)
-    st.stop()
-
-if st.session_state.analyzing:
-    # Validation
-    if not os.getenv("OPENAI_API_KEY"):
-        st.error("Missing OpenAI API Key. Please check your .env file or environment variables.")
-        st.session_state.analyzing = False
-        st.stop()
-    if not job_description:
-        st.error("Missing Job Description.")
-        st.session_state.analyzing = False
-        st.stop()
-    if not uploaded_resume:
-        st.error("Missing Resume File.")
-        st.session_state.analyzing = False
-        st.stop()
+# Set page config
+st.set_page_config(page_title="Agentic Interview Assistant", layout="wide", initial_sidebar_state="collapsed")
 
+# Custom CSS for improvements
+st.markdown("""
+<style>
+    .stApp {
+        background-color: #f8f9fa;
+    }
+    .block-container {
+        padding-top: 2rem !important;
+        padding-bottom: 1rem !important;
+    }
+    .main-header {
+        font-size: 2.0rem;
+        color: #1E3A8A;
+        font-weight: 700;
+        margin-bottom: 0px;
+    }
+    .card {
+        background-color: white;
+        padding: 1rem;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+        margin-bottom: 0.5rem;
+    }
+    /* Compact the grid */
+    div[data-testid="column"] {
+        padding: 0 !important;
+    }
+    p {
+        margin-bottom: 0.2rem;
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Define Base Directory for persistent storage
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+# Ensure data directory exists
+DATA_DIR = os.path.join(BASE_DIR, "data")
+if not os.path.exists(DATA_DIR):
+    os.makedirs(DATA_DIR)
+
+DB_PATH = os.path.join(DATA_DIR, "interview_state.db")
+RAG_DIR = os.path.join(DATA_DIR, "interview_rag_db")
+
+# --- Database Helper Functions ---
+
+def init_db():
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    # Init Tables
+    c.execute('''CREATE TABLE IF NOT EXISTS job_context (
+                    id INTEGER PRIMARY KEY, 
+                    description TEXT
+                 )''')
+    c.execute('''CREATE TABLE IF NOT EXISTS candidates (
+                    id INTEGER PRIMARY KEY AUTOINCREMENT,
+                    name TEXT,
+                    filename TEXT,
+                    status TEXT,
+                    score INTEGER,
+                    strengths TEXT,
+                    weaknesses TEXT,
+                    questions TEXT
+                 )''')
+    
+    # Safe migration for existing dbs
     try:
-        # Process Input
-        resume_path = ""
-        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{uploaded_resume.name.split('.')[-1]}") as tmp_file:
-             tmp_file.write(uploaded_resume.getvalue())
-             resume_path = tmp_file.name
-
-        resume_content_msg = f"Candidate Resume File Path: {resume_path} (Please use `read_local_file` to read this)."
-        if linkedin_url:
-            resume_content_msg += f"\nAlso check LinkedIn: {linkedin_url} (use `scrape_web_page`)."
-
-        # Execution
-        prog_bar = st.progress(0)
-        status_text = st.empty()
-        
-        # Create Model Client using Factory
-        model_client = AutoGenModelFactory.get_model(
-            provider="openai", model_name="gpt-4-turbo", model_info={"vision": False, "function_calling": True, "json_output": False}
-        )
-        
-        task_msg = f"""
-        Here is the Job Description:
-        {job_description}
+         c.execute("ALTER TABLE candidates ADD COLUMN questions TEXT")
+    except sqlite3.OperationalError:
+         pass
 
-        {resume_content_msg}
+    conn.commit()
+    conn.close()
 
-        The team must follow the strict workflow: 
-        Profiler -> Job Analyst -> Reviewer -> Evaluator -> Designer.
-        """
+def load_state():
+    """Loads JD and Candidates from SQLite to Session State"""
+    if not os.path.exists(DB_PATH):
+        return
 
-        status_text.text("Initializing Agents...")
-        prog_bar.progress(10)
+    conn = sqlite3.connect(DB_PATH)
+    
+    # Load JD
+    try:
+        jd_df = pd.read_sql_query("SELECT description FROM job_context LIMIT 1", conn)
+        if not jd_df.empty:
+            st.session_state['jd'] = jd_df.iloc[0]['description']
         
-        # Create a placeholder for debug output to avoid context issues inside async
-        debug_placeholder = st.empty()
+        # Load Candidates
+        cands_df = pd.read_sql_query("SELECT * FROM candidates", conn)
+        if not cands_df.empty:
+            st.session_state['candidates'] = cands_df.to_dict('records')
+    except Exception as e:
+        print(f"Error loading state: {e}")
+    finally:
+        conn.close()
+
+def save_new_session(job_desc, processed_docs):
+    """Saves initial session data to SQLite based on processed RAG docs"""
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    
+    # Save JD
+    c.execute("DELETE FROM job_context")
+    c.execute("INSERT INTO job_context (description) VALUES (?)", (job_desc,))
+    
+    # Save Candidates (Deduplicate based on name)
+    c.execute("DELETE FROM candidates")
+    
+    # Extract unique candidates from docs
+    unique_candidates = {}
+    for doc in processed_docs:
+        meta = doc['metadata']
+        name = meta['candidate_name']
+        if name not in unique_candidates:
+            unique_candidates[name] = {
+                'name': name,
+                'filename': meta['filename'],
+                'status': 'Pending',
+                'score': 0,
+                'strengths': '-',
+                'weaknesses': '-',
+                'questions': None
+            }
+    
+    candidates_list = []
+    for cand in unique_candidates.values():
+        c.execute("""INSERT INTO candidates (name, filename, status, score, strengths, weaknesses, questions) 
+                     VALUES (?, ?, ?, ?, ?, ?, ?)""", 
+                     (cand['name'], cand['filename'], cand['status'], cand['score'], cand['strengths'], cand['weaknesses'], cand['questions']))
+        candidates_list.append(cand)
+    
+    conn.commit()
+    conn.close()
+    return candidates_list
+
+def update_candidate_record(name, status, score, strengths, weaknesses):
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    c.execute("""UPDATE candidates 
+                 SET status = ?, score = ?, strengths = ?, weaknesses = ? 
+                 WHERE name = ?""", 
+                 (status, score, strengths, weaknesses, name))
+    conn.commit()
+    conn.close()
+
+def update_candidate_questions(name, questions_json_str):
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    c.execute("UPDATE candidates SET questions = ? WHERE name = ?", (questions_json_str, name))
+    conn.commit()
+    conn.close()
+
+def update_job_description(new_jd):
+    conn = sqlite3.connect(DB_PATH)
+    c = conn.cursor()
+    # Update JD
+    c.execute("DELETE FROM job_context")
+    c.execute("INSERT INTO job_context (description) VALUES (?)", (new_jd,))
+    
+    # Reset candidates
+    c.execute("UPDATE candidates SET status = 'Pending', score = 0, strengths = '-', weaknesses = '-'")
+    conn.commit()
+    conn.close()
+    
+    # Update session state
+    st.session_state['jd'] = new_jd
+    for c in st.session_state['candidates']:
+        c['status'] = 'Pending'
+        c['score'] = 0
+        c['strengths'] = '-'
+        c['weaknesses'] = '-'
+
+# Removed st.experimental_dialog to ensure compatibility
+# JD Editor logic moved to render_dashboard
+
+def reset_system():
+    """Wipes SQLite and RAG DB"""
+    if os.path.exists(DB_PATH):
+        try:
+            os.remove(DB_PATH)
+        except:
+            pass
+    
+    # Reset ChromaDB
+    try:
+        get_db().reset()
+    except Exception as e:
+        # If DB file doesn't exist yet, that's fine
+        print(f"RAG Reset info: {e}")
+    
+    st.session_state.clear()
+    st.rerun()
+
+# --- App Logic ---
+
+# Initialize
+if 'init_done' not in st.session_state:
+    init_db()
+    st.session_state['init_done'] = True
+    st.session_state['candidates'] = []
+    st.session_state['jd'] = ""
+    load_state() # Load from DB if exists
+
+if 'current_view' not in st.session_state:
+    st.session_state['current_view'] = 'dashboard'
+if 'selected_candidate_for_studio' not in st.session_state:
+    st.session_state['selected_candidate_for_studio'] = None
+
+def main():
+    # Header
+    c1, c2 = st.columns([5, 1])
+    with c1:
+        st.markdown('<div class="main-header">🤖 Agentic Interview Assistant</div>', unsafe_allow_html=True)
+        st.markdown("Your AI-powered partner for talent acquisition.")
+    
+    with c2:
+        if st.button("🔄 New Interview", type="secondary", help="Reset all data and start over"):
+            reset_system()
+
+    st.markdown("---")
+
+    # Check for persistence
+    has_active_session = bool(st.session_state['candidates']) and bool(st.session_state['jd'])
+
+    # --- Flow 1: Upload & Store (Top Section) ---
+    if not has_active_session:
+        with st.container():
+            st.info("👋 Welcome! Please upload resumes and provide a Job Description to get started.")
+            
+            c1, c2 = st.columns([1, 1], gap="small")
+            with c1:
+                st.markdown("### 1. Candidate Resumes")
+                uploaded_files = st.file_uploader("Upload PDF files", type=["pdf"], accept_multiple_files=True)
+            
+            with c2:
+                st.markdown("### 2. Job Description")
+                jd_input = st.text_area("Paste the JD here...", height=150, placeholder="We are looking for a Senior Python Engineer...")
+            
+            if st.button("🚀 Start Analysis", type="primary", use_container_width=True):
+                if uploaded_files and jd_input:
+                    with st.spinner("Ingesting resumes & building Knowledge Base..."):
+                        try:
+                            # 1. Process PDFs
+                            processed_docs = process_uploaded_files(uploaded_files)
+                            
+                            if not processed_docs:
+                                st.error("No text could be extracted from these PDFs.")
+                                st.stop()
+
+                            # 2. Add to RAG (Chroma)
+                            get_db().add_documents(processed_docs)
+                            
+                            # 3. Save Session Metadata to SQLite
+                            st.session_state['candidates'] = save_new_session(jd_input, processed_docs)
+                            st.session_state['jd'] = jd_input
+                            
+                            st.success(f"Successfully processed {len(st.session_state['candidates'])} candidates.")
+                            st.rerun()
+                        except Exception as e:
+                            st.error(f"An error occurred: {e}")
+                else:
+                    st.warning("Please upload files and enter a job description.")
+    else:
+        # Show mini stats
+        total = len(st.session_state['candidates'])
+        evaluated = len([c for c in st.session_state['candidates'] if c['status'] == 'Evaluated'])
+        avg_score = 0
+        if evaluated > 0:
+            scores = []
+            for c in st.session_state['candidates']:
+                if c['status'] == 'Evaluated':
+                    try:
+                        scores.append(float(c['score']))
+                    except (ValueError, TypeError):
+                        scores.append(0)
+            
+            avg_score = sum(scores) / len(scores) if scores else 0
+
+        # Compact Summary Row
+        col_metrics, col_edit = st.columns([6, 1])
+        with col_metrics:
+            st.markdown(f"""
+            <div style="display: flex; gap: 24px; align-items: center; padding: 10px 15px; background: white; border-radius: 8px; box-shadow: 0 1px 3px rgba(0,0,0,0.1); margin-bottom: 5px; font-size: 0.9rem;">
+                <div>📊 <strong>Candidates:</strong> {total}</div>
+                <div style="border-left: 1px solid #eee; height: 18px;"></div>
+                <div>✅ <strong>Evaluated:</strong> {evaluated}</div>
+                <div style="border-left: 1px solid #eee; height: 18px;"></div>
+                <div>⭐ <strong>Avg Score:</strong> {avg_score:.1f}</div>
+            </div>
+            """, unsafe_allow_html=True)
         
-        # Define debug print helper
-        def debug_print(msg):
-            print(f"DEBUG: {msg}")
+        with col_edit:
+             if st.button("📝 Edit JD", help="View or Update Job Description"):
+                 st.session_state['show_jd_edit'] = not st.session_state.get('show_jd_edit', False)
+
+        # Inline Editor for maximum compatibility
+        if st.session_state.get('show_jd_edit', False):
+            with st.container():
+                st.markdown("### Update Job Description")
+                st.warning("⚠️ Saving a new JD will reset all candidate evaluations.")
+                
+                new_jd = st.text_area("Job Description", value=st.session_state['jd'], height=200, key="jd_editor_area")
+                
+                ec1, ec2 = st.columns([1, 5])
+                with ec1:
+                    if st.button("💾 Save & Reset", type="primary"):
+                        update_job_description(new_jd)
+                        st.session_state['show_jd_edit'] = False
+                        st.rerun()
+                with ec2:
+                    if st.button("Cancel"):
+                        st.session_state['show_jd_edit'] = False
+                        st.rerun()
+                st.markdown("---")
+
+    # st.markdown("---") # Removed large divider
+
+    # --- Flow Switcher ---
+    if has_active_session:
+        if st.session_state['current_view'] == 'dashboard':
+            render_dashboard()
+        else:
+            render_studio()
+
+def render_dashboard():
+    st.subheader("📊 Candidate Dashboard")
+    
+# ... (Previous code)
 
-        debug_print("Analysis Started. Loop initializing...")
+# Helper function for evaluation (to avoid code duplication)
+async def perform_evaluation(candidate_index, candidate_name, jd_text):
+    raw_result = await run_evaluation_team(candidate_name, jd_text)
+    
+    # Improved JSON Extraction
+    json_match = re.search(r'\{.*\}', raw_result, re.DOTALL)
+    if json_match:
+        try:
+            result = json.loads(json_match.group(0))
+        except json.JSONDecodeError:
+             result = {}
+    else:
+        result = {}
+
+    # Map new keys (key_matches/gaps) to DB schema (strengths/weaknesses)
+    # Fallback to old keys if agent reverts
+    score = result.get('score', 0)
+    strengths = result.get('key_matches', result.get('strengths', []))
+    weaknesses = result.get('gaps', result.get('weaknesses', []))
+    
+    # Ensure they are lists
+    if isinstance(strengths, str): strengths = [strengths]
+    if isinstance(weaknesses, str): weaknesses = [weaknesses]
+    
+    strengths_str = "\n".join([f"- {s}" for s in strengths]) if strengths else "- None identified"
+    weaknesses_str = "\n".join([f"- {w}" for w in weaknesses]) if weaknesses else "- None identified"
+    
+    # Update DB
+    update_candidate_record(
+        candidate_name, 
+        'Evaluated', 
+        score, 
+        strengths_str, 
+        weaknesses_str
+    )
+    
+    # Update Session
+    st.session_state['candidates'][candidate_index]['status'] = 'Evaluated'
+    st.session_state['candidates'][candidate_index]['score'] = score
+    st.session_state['candidates'][candidate_index]['strengths'] = strengths_str
+    st.session_state['candidates'][candidate_index]['weaknesses'] = weaknesses_str
+
+def render_dashboard():
+    st.subheader("📊 Candidate Dashboard")
+    
+    # Header Row
+    st.markdown("""
+    <div style="display: grid; grid-template-columns: 2fr 1fr 2fr 1fr 1fr; gap: 10px; font-weight: bold; margin-bottom: 10px;">
+        <div>Candidate Name</div>
+        <div>Status</div>
+        <div>Fitness Score</div>
+        <div>Analysis</div>
+        <div>Interview</div>
+    </div>
+    """, unsafe_allow_html=True)
+
+    # Report Popup (Simulated with a container at top if active)
+    if st.session_state.get('view_report_id') is not None:
+        idx = st.session_state['view_report_id']
+        # Safety check
+        if idx < len(st.session_state['candidates']):
+            cand = st.session_state['candidates'][idx]
+            
+            with st.container():
+                st.markdown(f"### 📑 Evaluation Report: {cand['name']}")
+                rc1, rc2, rc3 = st.columns([1, 1, 1])
+                rc1.metric("Score", f"{cand['score']}/10")
+                
+                with rc2:
+                    st.markdown("**✅ Key Matches**")
+                    st.markdown(cand.get('strengths', '-'))
+                
+                with rc3:
+                    st.markdown("**⚠️ Gaps**")
+                    st.markdown(cand.get('weaknesses', '-'))
+                
+                # Actions
+                ac1, ac2 = st.columns([1, 5])
+                with ac1:
+                     if st.button("🔄 Re-evaluate", key=f"re_eval_{idx}"):
+                        with st.spinner("Re-evaluating..."):
+                            asyncio.run(perform_evaluation(idx, cand['name'], st.session_state['jd']))
+                            st.rerun()
+                with ac2:
+                    if st.button("Close Report"):
+                        st.session_state['view_report_id'] = None
+                        st.rerun()
+                st.divider()
+
+    for i, candidate in enumerate(st.session_state['candidates']):
+        with st.container():
+            c1, c2, c3, c4, c5 = st.columns([2, 1, 2, 1, 1])
+            
+            # Name -> Button as Link
+            if c1.button(f"📄 {candidate['name']}", key=f"view_{i}", help="Click to view full evaluation details"):
+                st.session_state['view_report_id'] = i
+                st.rerun()
+            
+            # Status
+            status_color = "green" if candidate['status'] == 'Evaluated' else "gray"
+            c2.markdown(f":{status_color}[{candidate['status']}]")
+            
+            # Score
+            try:
+                score_val = float(candidate['score'])
+            except (ValueError, TypeError):
+                score_val = 0
+            
+            if score_val > 0:
+                c3.progress(score_val / 10, text=f"{score_val}/10")
+            else:
+                c3.markdown("Waiting...")
+
+            # Evaluate Button (Initial)
+            if c4.button("⚡ Evaluate", key=f"eval_{i}", disabled=candidate['status'] == 'Evaluated', use_container_width=True):
+                with st.spinner(f"Evaluating {candidate['name']} with Multi-Agent Team..."):
+                    try:
+                         asyncio.run(perform_evaluation(i, candidate['name'], st.session_state['jd']))
+                         st.rerun()
+                    except Exception as e:
+                        st.error(f"Failed: {e}")
 
-        async def execute_analysis(placeholder):
+            # Design Button
+            if c5.button("🎙️ Design", key=f"design_{i}", disabled=candidate['status'] != 'Evaluated', use_container_width=True):
+                st.session_state['selected_candidate_for_studio'] = candidate
+                st.session_state['current_view'] = 'studio'
+                st.rerun()
+            
+            # Compact separator
+            st.markdown("<hr style='margin: 2px 0; border: none; border-top: 1px solid #e0e0e0;' />", unsafe_allow_html=True)
+ 
+from teams.interview_team import run_interview_generation_team, run_interview_revision
+
+def render_studio():
+    candidate = st.session_state['selected_candidate_for_studio']
+    if not candidate:
+        st.session_state['current_view'] = 'dashboard'
+        st.rerun()
+    
+from pdf_utils import create_interview_guide_pdf
+
+def render_studio():
+    candidate = st.session_state['selected_candidate_for_studio']
+    if not candidate:
+        st.session_state['current_view'] = 'dashboard'
+        st.rerun()
+    
+    # Session Persistence & Loading
+    cand_id = candidate['name'] 
+    state_key = f"questions_{cand_id}"
+    
+    # Load from persistence if missing using helper
+    if state_key not in st.session_state:
+        stored_q = candidate.get('questions')
+        if stored_q:
             try:
-                debug_print("Entering execute_analysis async function")
-                with st.spinner("Analyzing candidate and designing interview..."):
-                     # Get the stream
-                     debug_print(f"Creating team and stream with task length {len(task_msg)}")
-                     stream = await run_analysis_stream(model_client, task_msg)
-                     
-                     messages = []
-                     msg_count = 0
-                     debug_print("Stream created. Iterating...") 
-                     
-                     # Stream messages
-                     async for message in stream:
-                         msg_count += 1
-                         messages.append(message)
-                         
-                         source = getattr(message, 'source', 'Unknown')
-                         content = getattr(message, 'content', '')
-                         debug_print(f"Stream Msg {msg_count}: {source}") 
+                st.session_state[state_key] = json.loads(stored_q)
+            except:
+                st.session_state[state_key] = None
+        else:
+             st.session_state[state_key] = None
+
+    c_back, c_title = st.columns([1, 6])
+    with c_back:
+         if st.button("← Back"):
+            st.session_state['current_view'] = 'dashboard'
+            st.session_state['selected_candidate_for_studio'] = None
+            st.rerun()
+    with c_title:
+        st.subheader(f"Interview Studio: {candidate['name']}")
+
+    # Main Studio Layout
+    if st.session_state[state_key] is None:
+        # Pre-generation View
+        st.info("Click below to generate a tailored interview guide based on the candidate's profile and the Job Description.")
+        if st.button("🚀 Generate Interview Guide", type="primary"):
+            with st.spinner("Team working: Strategist setting weights -> Generator creating questions -> Reviewer validating..."):
+                try:
+                    raw_result = asyncio.run(run_interview_generation_team(candidate['name'], st.session_state['jd']))
+                    
+                    # Extract JSON
+                    json_match = re.search(r'\[.*\]', raw_result, re.DOTALL)
+                    if json_match:
+                         questions_json = json_match.group(0)
+                         questions = json.loads(questions_json)
                          
-                         if isinstance(content, list):
-                             content = "[Multimodal Content]"
-                         elif not content:
-                             content = "[No Content]"
-                             
-                         # Evidence: Show Data (Source Content) in UI & Console
-                         if isinstance(content, str) and len(content) > 500:
-                             print(f"--- EVIDENCE ({source}) ---\n{content[:5000]}\n---------------------------")
-                             with st.expander(f"📄 Data Source Evidence ({source})", expanded=False):
-                                 st.text(content)
+                         # Save to Session
+                         st.session_state[state_key] = questions
                          
-                         # Update Debug UI safely
-                         placeholder.text(f"[{msg_count}] {source}: {str(content)[:150]}...")
+                         # Save to DB
+                         update_candidate_questions(candidate['name'], questions_json)
+                         candidate['questions'] = questions_json
                          
-                         # Progress bar update
-                         if source == "Candidate_Profiler": prog_bar.progress(20)
-                         elif source == "Job_Analyst": prog_bar.progress(40)
-                         elif source == "Job_Analyst_Reviewer": prog_bar.progress(60)
-                         elif source == "Evaluator": prog_bar.progress(80)
-                         elif source == "Interview_Designer": prog_bar.progress(95)
-                     
-                     if msg_count == 0:
-                         st.error("No messages received. Check logs/console.")
-                         debug_print("Stream finished with 0 messages.")
-                     else:
-                         debug_print(f"Stream finished with {msg_count} messages.")
-
-                prog_bar.progress(100)
-                status_text.text("Analysis Complete.")
-                
-                # Generation
-                final_markdown = generate_markdown_report(messages)
-                
-                if not final_markdown.strip():
-                     final_markdown = "## Report Generation Failed\nNo structured output was found from the agent team."
-                
-                # Save to State (Persistence)
-                st.session_state.generated_report = final_markdown
-                st.session_state.generated_pdf = create_pdf(final_markdown)
+                         st.success("Guide Generated & Saved!")
+                         st.rerun()
+                    else:
+                        st.error("Failed to parse agent output. See logs.")
+                        st.text(raw_result)
+                except Exception as e:
+                    st.error(f"Generation Failed: {e}")
+    else:
+        # Post-generation View
+        questions = st.session_state[state_key]
+        
+        c_left, c_right = st.columns([2, 1])
+        
+        with c_left:
+            st.markdown("### 📝 Interview Guide")
+            
+            # Markdown Display Construction
+            md_content = ""
+            for q in questions:
+                uid = q.get('u_id', '?')
+                cat = q.get('category', 'General')
+                level = q.get('complexity', '')
+                text = q.get('question', '')
+                ans = q.get('likely_answer', '')
                 
-            except Exception as e:
-                st.error(f"An error occurred during analysis: {e}")
-                import traceback
-                st.text(traceback.format_exc())
-                debug_print(f"Async Job Failed: {e}")
+                md_content += f"#### Q{uid}. {text} \n"
+                md_content += f"*Category: {cat} | Level: {level}*  \n"
+                md_content += f"> **Likely Answer**: {ans}\n\n"
+                md_content += "---\n"
+            
+            st.markdown(md_content)
+            
+            st.divider()
+            st.markdown("### 💬 Revise Questions")
+            
+            # Revision Chat
+            if prompt := st.chat_input("Ex: Make the technical questions harder..."):
+                with st.spinner("Agents are revising the guide..."):
+                    try:
+                        current_json = json.dumps(questions)
+                        raw_result = asyncio.run(run_interview_revision(current_json, prompt))
+                        
+                        json_match = re.search(r'\[.*\]', raw_result, re.DOTALL)
+                        if json_match:
+                             new_q_json = json_match.group(0)
+                             new_questions = json.loads(new_q_json)
+                             
+                             # Update State & DB
+                             st.session_state[state_key] = new_questions
+                             update_candidate_questions(candidate['name'], new_q_json)
+                             candidate['questions'] = new_q_json
+                             
+                             st.success("Questions Revised!")
+                             st.rerun()
+                        else:
+                             st.error("Revision failed to output JSON.")
+                    except Exception as e:
+                        st.error(f"Revision Error: {e}")
 
-        # Run the async execution
-        try:
-            import asyncio
-            loop = asyncio.new_event_loop()
-            asyncio.set_event_loop(loop)
+        with c_right:
+            st.markdown("### ⚙️ Actions")
+            
+            if st.button("🔄 Regenerate from Scratch"):
+                 st.session_state[state_key] = None
+                 st.rerun()
+            
+            # PDF Generation
             try:
-                loop.run_until_complete(execute_analysis(debug_placeholder))
-            finally:
-                # Cleanup pending tasks
-                pending = asyncio.all_tasks(loop)
-                for task in pending:
-                    task.cancel()
-                if pending:
-                    loop.run_until_complete(asyncio.gather(*pending, return_exceptions=True))
-                loop.close()
-        except Exception as e:
-            st.error(f"System Error: {e}")
-            import traceback
-            st.text(traceback.format_exc())
+                pdf_bytes = create_interview_guide_pdf(candidate['name'], questions)
+                st.download_button(
+                    label="📥 Download PDF",
+                    data=pdf_bytes,
+                    file_name=f"{candidate['name'].replace(' ', '_')}_Interview_Guide.pdf",
+                    mime="application/pdf",
+                    type="primary"
+                )
+            except Exception as e:
+                st.error(f"PDF Generation Error: {e}")
             
-    finally:
-        # Reset analysis state at the end so user can run again
-        st.session_state.analyzing = False
-        st.rerun() 
-        
-else:
-    st.info("👈 Please fill in the details in the sidebar to get started.")
+            # Keep JSON as backup/debug
+            st.download_button(
+                label="Download JSON Source",
+                data=json.dumps(questions, indent=2),
+                file_name=f"{candidate['name']}_guide.json",
+                mime="application/json"
+            )
 
-# 4. Render Persistent View (Report)
-render_persistent_view()
+if __name__ == "__main__":
+    main()

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src/interview-assistant/data/interview_rag_db/chroma.sqlite3

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src/interview-assistant/implementation.md

@@ -0,0 +1,115 @@
+# Interview Assistant Implementation Plan
+
+## 1. Overview
+The Interview Assistant is a RAG-powered multi-agent system designed to streamline the recruitment process. It allows interviewers to upload candidate resumes, analyze them against specific job descriptions, and generate tailored interview questions.
+
+We will use **`autogen-agentchat`** to orchestrate the multi-agent workflow for resume analysis, fitness evaluation, and interview design.
+
+## 2. Architecture
+
+### High-Level Components
+1.  **Streamlit App**: Serves as both the UI and the control layer.
+2.  **RAG Engine (ChromaDB)**: Stores vector embeddings of candidate resumes.
+3.  **Session DB (SQLite)**: Stores structured application state.
+4.  **Agentic Core**: `autogen-agentchat` agents:
+    *   **Evaluation Team**: `JD_Summarizer`, `Resume_Summarizer`, `Evaluator`, `Coordinator`.
+    *   **Interview Team**: `Interview_Strategist`, `Question_Generator`, `Question_Reviewer`.
+    *   **Tools**: `search_candidate_knowledge_base`.
+
+### Persistence & Reset
+*   The app checks **SQLite** on startup. If a session exists, it loads the Dashboard view immediately.
+*   **"New Interview" Button**: Clears both ChromaDB collections and the SQLite tables to start fresh.
+
+### Agent Workflow
+*   **UserProxy**: Configured to allow human input if needed, but primarily acts as the bridge for the Streamlit app.
+*   **ResumeAnalyst**: Queries the RAG system to evaluate candidates against the Job Description.
+*   **Interviewer**: Generates specific questions based on the Analyst's report.
+
+## 3. Technology Stack
+*   **UI & Server**: Streamlit (Python).
+*   **AI/ML**:
+    *   **Framework**: `autogen-agentchat` (Microsoft AutoGen).
+    *   **Vector DB**: ChromaDB (Local persistent).
+    *   **Orchestration**: Agents will access ChromaDB via registered tools (`register_function`).
+    *   **Embeddings**: OpenAI or compatible (SentenceTransformers).
+    *   **LLM**: Ollama (Gemma 3 4B) hosted on Ollama Cloud.
+
+## 4. Directory Structure
+```text
+src/interview-assistant/
+├── app.py                 # Streamlit Entry point (Main UI)
+├── agents/                # Agent Definitions
+│   ├── __init__.py
+│   └── definitions.py     # JD_Summarizer, Resume_Summarizer, Evaluator, Coordinator
+├── teams/                 # Team Orchestration
+│   ├── __init__.py
+│   └── evaluation_team.py # Evaluation Workflow (RoundRobin)
+├── data/                  # Persistent Storage (SQlite + ChromaDB)
+├── rag/                   # Retrieval Augmented Generation
+│   ├── __init__.py
+│   ├── db.py              # ChromaDB Interface
+│   └── ingest.py          # PDF/Text Parsing & Chunking
+└── implementation.md      # This file
+```
+
+## 5. User Flows & Implementation Strategy
+
+### Flow 1: Ingestion & Dashboard (✅ Completed)
+**Objective**: Upload data and prepare the workspace.
+*   **Input**:
+    *   File Uploader (Multiple PDFs).
+    *   Job Description (Text Area).
+    *   "Submit" Button.
+*   **System Action**:
+    *   Parse PDFs and ingest into **ChromaDB** with metadata (`name`, `filename`).
+    *   Store JD in session state.
+    *   Initialize the **Candidate Grid**.
+*   **Output**: A data grid displaying discovered candidates. Columns: `[Select]`, `Name`, `Status` (Pending/Evaluated), `Score`, `Actions`.
+
+### Flow 2: On-Demand Evaluation (✅ Completed)
+**Objective**: Multi-Agent analysis of specific candidates.
+*   **Input**: User clicks "Evaluate" on a specific candidate row.
+*   **System Action**:
+    *   **Evaluation Team** (`RoundRobinGroupChat`) is triggered in `src/interview-assistant/teams/evaluation_team.py`.
+    *   **JD_Summarizer**: Extracts key criteria.
+    *   **Resume_Summarizer**: Fetches resume evidence from RAG.
+    *   **Evaluator**: Scores and analyzes.
+    *   **Coordinator**: Validates JSON output.
+    *   **Output**: Updates the grid data with `Fitness Score`, `Strengths`, `Weaknesses` in SQLite.
+*   **Output**: The grid refreshes to show the new data with progress bars.
+
+### Flow 3: Interview Design Studio (✅ Completed)
+**Objective**: Interactive question generation and revision.
+*   **Trigger**: User selects a candidate (who is evaluated) and enters the "Interview Studio".
+*   **Interface**: 
+    *   "Generate Interview Guide" Button (One-click generation).
+    *   Chat Interface for **Revision** (e.g., "Make technical questions harder").
+*   **System Action**:
+    *   **Interview Generation Team** (`teams/interview_team.py`):
+        *   **Interview_Strategist**: Sets weights (Tech/Leadership/Behavioral) based on candidate profile.
+        *   **Question_Generator**: Creates 20 questions.
+        *   **Question_Reviewer**: Validates output.
+    *   **Revision Flow**: Updates existing questions based on user feedback.
+*   **Output**:
+    *   Interactive Expandable List of Questions.
+    *   JSON Download.
+    *   Persistence in SQLite for session reloading.
+
+## 6. Implementation Steps
+
+### Phase 1: Core & RAG (✅ Done)
+1.  **Frontend**: Build the layout in `app.py`.
+2.  **RAG**: Implement `ingest_resumes(files)` to populate ChromaDB.
+3.  **Persistence**: SQLite database for session state.
+
+### Phase 2: Evaluation Agent (✅ Done)
+1.  Define agents in `agents/definitions.py`.
+2.  Implement `run_evaluation_team` in `teams/evaluation_team.py`.
+3.  Connect "Evaluate" button in UI to the team runner.
+
+### Phase 3: Interview Chat & PDF (✅ Done)
+1.  **Interview Agents**: Created `Interview_Strategist`, `Question_Generator`, `Question_Reviewer` in `agents/definitions.py`.
+2.  **Team Helper**: Created `teams/interview_team.py` with `run_interview_generation_team` and `run_interview_revision`.
+3.  **App Integration**: Integrated Generation and Revision flows in `render_studio` in `app.py`.
+4.  **Persistence**: Added `questions` column to SQLite schema for saving state.
+5.  **PDF Generation**: (Pending: PDF Export logic is currently JSON-only, PDF button is a placeholder).

src/interview-assistant/pdf_utils.py

@@ -0,0 +1,53 @@
+from fpdf import FPDF
+import io
+
+class InterviewGuidePDF(FPDF):
+    def header(self):
+        self.set_font('Arial', 'B', 15)
+        self.cell(0, 10, 'Interview Guide', 0, 1, 'C')
+        self.ln(5)
+
+    def footer(self):
+        self.set_y(-15)
+        self.set_font('Arial', 'I', 8)
+        self.cell(0, 10, f'Page {self.page_no()}', 0, 0, 'C')
+
+def create_interview_guide_pdf(candidate_name, questions):
+    pdf = InterviewGuidePDF()
+    pdf.add_page()
+    
+    # Candidate Info
+    pdf.set_font('Arial', 'B', 12)
+    pdf.cell(0, 10, f"Candidate: {candidate_name}", 0, 1)
+    pdf.ln(5)
+    
+    # Questions
+    pdf.set_font('Arial', '', 11)
+    
+    for q in questions:
+        # Avoid orphan lines/page breaks in weird spots
+        if pdf.get_y() > 250:
+            pdf.add_page()
+            
+        # Question Header
+        q_id = q.get('u_id', '?')
+        cat = q.get('category', 'General')
+        comp = q.get('complexity', 'Medium')
+        
+        pdf.set_font('Arial', 'B', 11)
+        pdf.multi_cell(0, 7, f"Q{q_id} [{cat} - {comp}]")
+        
+        # Question Body
+        pdf.set_font('Arial', '', 11)
+        pdf.multi_cell(0, 7, q.get('question', ''))
+        pdf.ln(2)
+        
+        # Answer Key
+        pdf.set_font('Arial', 'I', 10)
+        pdf.set_text_color(100, 100, 100) # Gray for answers
+        pdf.multi_cell(0, 7, f"Likely Answer: {q.get('likely_answer', '')}")
+        pdf.set_text_color(0, 0, 0) # Reset to black
+        
+        pdf.ln(5) # Spacing between questions
+
+    return pdf.output(dest='S').encode('latin-1', 'replace')

src/interview-assistant/rag/db.py

@@ -0,0 +1,94 @@
+import chromadb
+import uuid
+import os
+import shutil
+
+# Check if we should use OpenAI embeddings (optional, sticking to default for now for ease of setup)
+# from chromadb.utils import embedding_functions
+
+# Define Base Directory (src/interview-assistant/data)
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+DATA_DIR = os.path.join(BASE_DIR, "data")
+DB_DIR = os.path.join(DATA_DIR, "interview_rag_db")
+COLLECTION_NAME = "candidates"
+
+class RAGDatabase:
+    def __init__(self):
+        self.client = chromadb.PersistentClient(path=DB_DIR)
+        
+        # Using default embedding function (Sentence Transformers)
+        # This requires 'sentence-transformers' installed.
+        self.collection = self.client.get_or_create_collection(name=COLLECTION_NAME)
+
+    def add_documents(self, processed_docs):
+        """
+        Adds processed documents to the vector store.
+        processed_docs: List of {'text': str, 'metadata': dict}
+        """
+        ids = []
+        documents = []
+        metadatas = []
+
+        for doc in processed_docs:
+            full_text = doc['text']
+            base_metadata = doc['metadata']
+            
+            # Simple chunking strategy: 1000 characters with 200 overlap
+            chunk_size = 1000
+            overlap = 200
+            
+            for i in range(0, len(full_text), chunk_size - overlap):
+                chunk = full_text[i:i + chunk_size]
+                if len(chunk) < 50: # Skip tiny chunks
+                    continue
+                
+                # Add chunk index to metadata maybe?
+                chunk_metadata = base_metadata.copy()
+                chunk_metadata['chunk_index'] = i
+                
+                ids.append(str(uuid.uuid4()))
+                documents.append(chunk)
+                metadatas.append(chunk_metadata)
+
+        if documents:
+            self.collection.add(
+                documents=documents,
+                metadatas=metadatas,
+                ids=ids
+            )
+            return True
+        return False
+
+    def query(self, query_text, candidate_name=None, n_results=5):
+        """
+        Query the database.
+        If candidate_name is provided, filters results to that candidate.
+        """
+        where_filter = None
+        if candidate_name:
+            where_filter = {"candidate_name": candidate_name}
+        
+        results = self.collection.query(
+            query_texts=[query_text],
+            n_results=n_results,
+            where=where_filter
+        )
+        return results
+
+    def reset(self):
+        """
+        Deletes the collection and recreates it.
+        """
+        self.client.delete_collection(COLLECTION_NAME)
+        self.collection = self.client.get_or_create_collection(name=COLLECTION_NAME)
+        # Verify empty
+        print(f"Collection {COLLECTION_NAME} reset. Count: {self.collection.count()}")
+
+# Singleton instance access
+_db_instance = None
+
+def get_db():
+    global _db_instance
+    if _db_instance is None:
+        _db_instance = RAGDatabase()
+    return _db_instance

src/interview-assistant/rag/ingest.py

@@ -0,0 +1,53 @@
+import io
+import re
+from pypdf import PdfReader
+
+def extract_text_from_pdf(file_bytes):
+    """
+    Extracts text from a PDF file object (BytesIO).
+    """
+    try:
+        reader = PdfReader(file_bytes)
+        text = ""
+        for page in reader.pages:
+            text += page.extract_text() + "\n"
+        return text
+    except Exception as e:
+        print(f"Error reading PDF: {e}")
+        return ""
+
+def clean_text(text):
+    """
+    Simple text cleanup: remove excessive whitespace.
+    """
+    text = re.sub(r'\s+', ' ', text)
+    return text.strip()
+
+def process_uploaded_files(uploaded_files):
+    """
+    Processes a list of Streamlit UploadedFile objects.
+    Returns a list of dicts: {'text': str, 'metadata': dict}
+    """
+    processed_docs = []
+    
+    for uploaded_file in uploaded_files:
+        # Create a BytesIO object from the uploaded file
+        # Streamlit UploadedFile is already file-like, but let's be safe
+        text = extract_text_from_pdf(uploaded_file)
+        if not text:
+            continue
+            
+        cleaned_text = clean_text(text)
+        
+        # Heuristic: Name is often the first line, but filename is safer for unique ID
+        candidate_name = uploaded_file.name.replace(".pdf", "").replace("_", " ").title()
+        
+        processed_docs.append({
+            "text": cleaned_text,
+            "metadata": {
+                "filename": uploaded_file.name,
+                "candidate_name": candidate_name
+            }
+        })
+        
+    return processed_docs

src/interview-assistant/teams/evaluation_team.py

@@ -0,0 +1,72 @@
+from autogen_agentchat.teams import RoundRobinGroupChat
+from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination
+from autogen_agentchat.agents import UserProxyAgent
+from agents.definitions import (
+    get_evaluator, 
+    get_coordinator
+)
+
+async def run_evaluation_team(candidate_name: str, job_description: str):
+    # PRE-OPTIMIZATION: Fetch context directly to save LLM turns
+    from tools.rag_tools import search_candidate_knowledge_base
+    print(f"[DEBUG] Pre-fetching context for {candidate_name}...", flush=True)
+    resume_context = await search_candidate_knowledge_base(f"Summary and skills for {candidate_name}", candidate_name)
+
+    # Initialize Agents (Reduced Team)
+    # We merged JD & Resume analysis into the prompt for the Evaluator
+    evaluator = get_evaluator()
+    coordinator = get_coordinator()
+
+    TERMINATION_KEYWORD = "EVALUATION" + "_" + "APPROVED"
+
+    # Define Termination
+    termination = TextMentionTermination(TERMINATION_KEYWORD) | MaxMessageTermination(10)
+
+    # Create Team (2 Agents)
+    # Flow: Evaluator (Analysis) -> Coordinator (Validation) -> Loop
+    team = RoundRobinGroupChat(
+        participants=[evaluator, coordinator], 
+        termination_condition=termination
+    )
+
+    # Initial Prompt
+    task = f"""
+    PROJECT: Candidate Evaluation
+    Candidate Name: {candidate_name}
+    
+    JOB DESCRIPTION:
+    {job_description}
+    
+    RESUME CONTEXT (Pre-retrieved):
+    {resume_context}
+    
+    GOAL: Produce a high-quality, data-driven evaluation JSON.
+
+    PROCESS:
+    1. Evaluator: Analyze JD requirements vs Resume Context. Score (0-10) and identify Strengths/Weaknesses.
+    2. Coordinator: Review against strict JSON rules.
+    
+    ITERATION RULES:
+    - If Coordinator REJECTS, Evaluator must fix.
+    - When satisfied, Coordinator outputs "EVALUATION", underscore, "APPROVED".
+    """
+
+    print(f"[DEBUG] Starting Evaluation Team for {candidate_name}")
+    last_message = ""
+    last_json_message = ""
+    
+    async for message in team.run_stream(task=task):
+        if hasattr(message, 'content') and isinstance(message.content, str):
+            content = message.content
+            print(f"[DEBUG] Agent '{message.source}' says: {content[:60]}...")
+            last_message = content
+            
+            # Simple heuristic to trap the JSON payload
+            # look for keys that MUST be present
+            if '"score"' in content and '"key_matches"' in content:
+                last_json_message = content
+    
+    print(f"[DEBUG] Evaluation Team finished. Final message length: {len(last_message)}")
+    
+    # Prefer the structured JSON message if we found one
+    return last_json_message if last_json_message else last_message

src/interview-assistant/teams/interview_team.py

@@ -0,0 +1,103 @@
+from autogen_agentchat.teams import RoundRobinGroupChat
+from autogen_agentchat.conditions import TextMentionTermination, MaxMessageTermination
+from autogen_agentchat.agents import UserProxyAgent
+from agents.definitions import (
+    get_question_generator, 
+    get_question_reviewer
+)
+from tools.rag_tools import search_candidate_knowledge_base
+
+async def run_interview_generation_team(candidate_name: str, job_description: str):
+    # Pre-fetch Context to simplify agent flow
+    # We query for the candidate name itself to get a general overview + strengths/weaknesses
+    print(f"[DEBUG] Pre-fetching context for {candidate_name}...", flush=True)
+    resume_context = await search_candidate_knowledge_base(f"Summary and skills for {candidate_name}", candidate_name)
+
+    # Initialize Agents
+    # OPTIMIZATION: Removed Strategist agent to save calls. Generator handles both.
+    generator = get_question_generator()
+    reviewer = get_question_reviewer()
+
+    TERMINATION_KEYWORD = "GUIDE" + "_" + "APPROVED"
+    
+    # Define Termination (Reduced max turns slightly as we have fewer agents)
+    termination = TextMentionTermination(TERMINATION_KEYWORD) | MaxMessageTermination(15)
+
+    # Create Team (2 Agents)
+    # Flow: Generator -> Reviewer -> Loop
+    team = RoundRobinGroupChat(
+        participants=[generator, reviewer], 
+        termination_condition=termination
+    )
+
+    # Initial Prompt
+    task = f"""
+    PROJECT: Interview Guide Generation
+    Candidate Name: {candidate_name}
+    Job Description: {job_description}
+    
+    RESUME CONTEXT:
+    {resume_context}
+    
+    GOAL: Create a high-quality, detailed Interview Guide (10 Questions).
+    
+    PROCESS:
+    1. Generator:
+       - First, analyze Role/Seniority (e.g. Architect = System Design, Junior = Syntax) and determine Weights.
+       - Then, generate 10 Detailed Questions (50-100 words each) based on that strategy. Group by Category.
+    2. Reviewer: Critically review against checklist.
+    
+    ITERATION RULES:
+    - If Reviewer REJECTS, Generator must rewrite.
+    - Loop until quality is perfect.
+    - When satisfied, output JSON + "GUIDE", underscore, "APPROVED".
+    """
+
+    print(f"[DEBUG] Starting Interview Generation Team for {candidate_name}", flush=True)
+    
+    last_json_message = ""
+    last_message = ""
+    
+    async for message in team.run_stream(task=task):
+        if hasattr(message, 'content') and isinstance(message.content, str):
+            content = message.content
+            print(f"[DEBUG] Agent '{message.source}' says: {content[:60]}...", flush=True)
+            last_message = content
+            
+            # Check if this message looks like it contains the questions list
+            # We look for a JSON array pattern with at least some content
+            if "[" in content and "]" in content and "question" in content:
+                last_json_message = content
+
+    print(f"[DEBUG] Interview Generation Team finished.", flush=True)
+    
+    # Return the last message that had JSON, otherwise fallback to the very last message
+    return last_json_message if last_json_message else last_message
+
+async def run_interview_revision(current_questions: str, feedback: str):
+    generator = get_question_generator()
+    user_proxy = UserProxyAgent("User")
+    
+    # Simple chat for revision
+    team = RoundRobinGroupChat(
+        participants=[generator], 
+        termination_condition=MaxMessageTermination(5)
+    )
+    
+    task = f"""
+    Current Questions (JSON): {current_questions}
+    
+    User Feedback: {feedback}
+    
+    TASK: Revise the questions based on the feedback.
+    Output the FULL revised list of 10 questions in the same JSON list format.
+    """
+    
+    print(f"[DEBUG] Starting Revision", flush=True)
+    last_message = ""
+    async for message in team.run_stream(task=task):
+        if hasattr(message, 'content') and isinstance(message.content, str):
+            print(f"[DEBUG] Revision Agent '{message.source}': {message.content[:60]}...", flush=True)
+            last_message = message.content
+            
+    return last_message

src/interview-assistant/tools/rag_tools.py

@@ -0,0 +1,14 @@
+from rag.db import get_db
+
+async def search_candidate_knowledge_base(query: str, candidate_name: str) -> str:
+    """Searches RAG for candidate details."""
+    print(f"[DEBUG] Tool 'search_candidate_knowledge_base' called with query='{query}', candidate='{candidate_name}'")
+    db = get_db()
+    results = db.query(query, candidate_name=candidate_name, n_results=3)
+    
+    if not results['documents'][0]:
+        print(f"[DEBUG] Tool found NO results for {candidate_name}")
+        return "No relevant info found."
+    
+    print(f"[DEBUG] Tool found {len(results['documents'][0])} segments for {candidate_name}")
+    return f"Context for {candidate_name}:\n" + "\n".join(results['documents'][0])

uv.lock

@@ -3,7 +3,7 @@ revision = 3
 requires-python = "==3.12.*"
 
 [[package]]
-name = "agents"
+name = "agenticai"
 version = "0.1.0"
 source = { editable = "." }
 dependencies = [
@@ -11,6 +11,7 @@ dependencies = [
     { name = "anthropic" },
     { name = "arxiv" },
     { name = "asyncio" },
+    { name = "autoflake" },
     { name = "autogen-agentchat" },
     { name = "autogen-ext", extra = ["grpc", "mcp", "ollama", "openai"] },
     { name = "azure-identity" },
@@ -23,6 +24,9 @@ dependencies = [
     { name = "faiss-cpu" },
     { name = "fastapi" },
     { name = "fpdf" },
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