Update app.py

app.py

@@ -1,643 +1,680 @@
-import os
-import json
-import re
-import time
-from typing import Dict, Any, List, Optional, Annotated
-from chatbot_model import (
-    UserMemory,
-    ChatbotState,
-    ProfileAnalysisModel,
-    JobFitModel,
-    ContentGenerationModel,
-    
-)
-from llm_utils import call_llm_and_parse
-from profile_preprocessing import (
-    preprocess_profile,
-    initialize_state,
-    normalize_url
-)
-from openai import OpenAI
-import streamlit as st
-import hashlib
-from dotenv import load_dotenv
-from pydantic import BaseModel, Field,ValidationError
-# import pdb; pdb.set_trace()
-from scraping_profile import scrape_linkedin_profile
-from langchain_openai import ChatOpenAI
-from langchain_core.messages import SystemMessage, HumanMessage, AIMessage,BaseMessage,ToolMessage
-from langchain_core.tools import tool
-from langgraph.graph import StateGraph, END,START
-from langgraph.checkpoint.memory import MemorySaver
-from langgraph.graph import add_messages  # if your framework exposes this
-from langgraph.prebuilt import ToolNode,tools_condition,InjectedState
-import dirtyjson
-import sqlite3
-try:
-    from langgraph.checkpoint.sqlite import SqliteSaver
-    SQLITE_AVAILABLE = True
-except ImportError:
-    SQLITE_AVAILABLE = False
-
-
-
-# ========== 1. ENVIRONMENT & LLM SETUP ==========
-load_dotenv()
-groq_key = os.getenv("GROQ_API_KEY")
-assert groq_key, "GROQ_API_KEY not found in environment!"
-groq_client=OpenAI(
-            api_key=os.getenv("GROQ_API_KEY"),
-            base_url="https://api.groq.com/openai/v1"
-        )
-
-def normalize_url(url):
-    return url.strip().rstrip('/')
-
-def validate_state(state: dict) -> None:
-    """
-    Validate given state dict against ChatbotState schema.
-    Displays result in Streamlit instead of printing.
-    """
-    # st.write("=== Validating chatbot state ===")
-    try:
-        ChatbotState.model_validate(state)
-        # st.success("✅ State is valid!")
-    except ValidationError as e:
-        st.error("❌ Validation failed!")
-        errors_list = []
-        for error in e.errors():
-            loc = " → ".join(str(item) for item in error['loc'])
-            msg = error['msg']
-            errors_list.append(f"- At: {loc}\n  Error: {msg}")
-        st.write("\n".join(errors_list))
-        # Optionally show raw validation error too:
-        st.expander("See raw validation error").write(str(e))
-        st.stop()
-
-
-user_memory = UserMemory()
-
-# ========== 7. AGENT FUNCTIONS ==========
-
-def profile_analysis_prompt(profile: Dict[str, str]) -> str:
-    return f"""
-You are a top-tier LinkedIn career coach and AI analyst.
-
-Analyze the following candidate profile carefully.
-
-Candidate profile data:
-FullName: {profile.get("FullName", "")}
-Headline: {profile.get("Headline", "")}
-JobTitle: {profile.get("JobTitle", "")}
-CompanyName: {profile.get("CompanyName", "")}
-CompanyIndustry: {profile.get("CompanyIndustry", "")}
-CurrentJobDuration: {profile.get("CurrentJobDuration", "")}
-About: {profile.get("About", "")}
-Experiences: {profile.get("Experiences", "")}
-Skills: {profile.get("Skills", "")}
-Educations: {profile.get("Educations", "")}
-Certifications: {profile.get("Certifications", "")}
-HonorsAndAwards: {profile.get("HonorsAndAwards", "")}
-Verifications: {profile.get("Verifications", "")}
-Highlights: {profile.get("Highlights", "")}
-Projects: {profile.get("Projects", "")}
-Publications: {profile.get("Publications", "")}
-Patents: {profile.get("Patents", "")}
-Courses: {profile.get("Courses", "")}
-TestScores: {profile.get("TestScores", "")}
-
-
-Identify and summarize:
-1. strengths:
-    - technical strengths (skills, tools, frameworks)
-    - project strengths (impactful projects, innovation)
-    - educational strengths (degrees, certifications, awards)
-    - soft skills and personality traits (teamwork, leadership)
-2. weaknesses:
-    - missing or weak technical skills
-    - gaps in projects, experience, or education
-    - unclear profile sections or missing context
-3. actionable suggestions:
-    - concrete ways to improve profile headline, about section, or add projects
-    - suggestions to learn or highlight new skills
-    - ideas to make the profile more attractive for recruiters
-
-Important instructions:
-- Respond ONLY with valid JSON.
-- Do NOT include text before or after JSON.
-- Be concise but detailed.
-
-
-
-Example JSON format:
-{{
-  "strengths": {{
-    "technical": ["...", "..."],
-    "projects": ["...", "..."],
-    "education": ["...", "..."],
-    "soft_skills": ["...", "..."]
-  }},
-  "weaknesses": {{
-    "technical_gaps": ["...", "..."],
-    "project_or_experience_gaps": ["...", "..."],
-    "missing_context": ["...", "..."]
-  }},
-  "suggestions": [
-    "...",
-    "...",
-    "..."
-  ]
-}}
-""".strip()
-
-
-
-
-def job_fit_prompt(sections: Dict[str, str], target_role: str) -> str:
-    return f"""
-You are an expert career coach and recruiter.
-
-Compare the following candidate profile against the typical requirements for the role of "{target_role}".
-
-Candidate Profile:
-- Headline: {sections.get('headline', '')}
-- About: {sections.get('about', '')}
-- Job Title: {sections.get('job_title', '')}
-- Company: {sections.get('company_name', '')}
-- Industry: {sections.get('company_industry', '')}
-- Current Job Duration: {sections.get('current_job_duration', '')}
-- Skills: {sections.get('skills', '')}
-- Projects: {sections.get('projects', '')}
-- Educations: {sections.get('educations', '')}
-- Certifications: {sections.get('certifications', '')}
-- Honors & Awards: {sections.get('honors_and_awards', '')}
-- Experiences: {sections.get('experiences', '')}
-
-**Instructions:**
-- Respond ONLY with valid JSON.
-- Your JSON must exactly match the following schema:
-{{
-  "match_score": 85,
-  "missing_skills": ["Skill1", "Skill2"],
-  "suggestions": ["...", "...", "..."]
-}}
-- "match_score": integer from 0–100 estimating how well the profile fits the target role.
-- "missing_skills": key missing or weakly mentioned skills.
-- "suggestions": 3 actionable recommendations to improve fit (e.g., learn tools, rewrite headline).
-
-Do NOT include explanations, text outside JSON, or markdown.
-Start with '{{' and end with '}}'.
-The JSON must be directly parseable.
-""".strip()
-
-
-# --- Tool: Profile Analyzer ---
-@tool
-def profile_analyzer(state: Annotated[ChatbotState, InjectedState]) -> dict:
-    """
-    Tool: Analyze the overall full user's profile to give strengths, weaknesses, suggestions.
-    This is needed only if full analysis of profile is needed. 
-    Returns the full analysis in the form of a json.
-
-    - It takes no arguments
-    """
-
-
-    # Get summarized profile (dictionary of strings)
-    profile = getattr(state, "profile", {}) or {}
-
-    # Build prompt
-    prompt = profile_analysis_prompt(profile)
-
-    # Call the LLM & parse structured result
-    analysis_model = call_llm_and_parse(groq_client,prompt, ProfileAnalysisModel)
-    analysis_dict = analysis_model.model_dump()
-
-    # Save to state and user memory
-    state.profile_analysis = analysis_dict
-    user_memory.save("profile_analysis", analysis_dict)
-
-    print("💾 [DEBUG] Saved analysis to user memory.")
-    print("📦 [DEBUG] Updated state.profile_analysis with analysis.")
-
-    return analysis_dict
-
-# --- Tool: Job Matcher ---
-
-
-@tool
-def job_matcher(
-    state: Annotated[ChatbotState, InjectedState],
-    target_role: str = None
-) -> dict:
-    """
-    Tool: Analyze how well the user's profile fits the target role.
-    - If user is asking if he is a good fit for a certain role, or needs to see if his profile is compatible with a certain role, call this.
-    - Takes target_role as an argument.
-    - this tool is needed when match score, missing skills, suggestions are needed based on a job name given.
-    """
-    print(f"target role is {target_role}")
-    # Update state.target_role if provided
-
-    sections = getattr(state, "sections", {})
-
-    # Build prompt
-    prompt = job_fit_prompt(sections, target_role)
-
-    # Call LLM and parse
-    try:
-        job_fit_model = call_llm_and_parse(groq_client,prompt, JobFitModel)
-        job_fit_dict = job_fit_model.model_dump()
-        job_fit_dict["target_role"] = target_role
-    except Exception as e:
-        job_fit_dict = {
-            "target_role":target_role,
-            "match_score": 0,
-            "missing_skills": [],
-            "suggestions": ["Parsing failed or incomplete response."]
-        }
-
-    # Save to state and user memory
-    state.job_fit = job_fit_dict
-    user_memory.save("job_fit", job_fit_dict)
-
-    return job_fit_dict
-
-
-
-
-
-
-@tool
-def extract_from_state_tool(
-    state: Annotated[ChatbotState, InjectedState],
-    key: str
-) -> dict:
-    """
-    This tool is used if user wants to ask about any particular part of this profile. Use this if a singe section is targeted. It expects key as an arguement, that represents what
-    the user is wanting to look at, from his profile.
-    Argument:
-      key: only pass one from the below list, identify one thing the user wants to look into and choose that:
-        "sections.about", "sections.headline", "sections.skills", "sections.projects",
-        "sections.educations", "sections.certifications", "sections.honors_and_awards",
-        "sections.experiences", "sections.publications", "sections.patents",
-        "sections.courses", "sections.test_scores", "sections.verifications",
-        "sections.highlights", "sections.job_title", "sections.company_name",
-        "sections.company_industry", "sections.current_job_duration", "sections.full_name",
-        "enhanced_content,"profile_analysis", "job_fit", "target_role", "editing_section"
-    """
-    value = state
-    try:
-        for part in key.split('.'):
-            # Support both dict and Pydantic model
-            if isinstance(value, dict):
-                value = value.get(part)
-            elif hasattr(value, part):
-                value = getattr(value, part)
-            else:
-                value = None
-            if value is None:
-                break
-    except Exception:
-        value = None
-    return {"result": value}
-
-
-tools = [
-    profile_analyzer,
-   job_matcher,
-    extract_from_state_tool
-]
-llm = ChatOpenAI(
-    api_key=groq_key,
-    base_url="https://api.groq.com/openai/v1",
-    model="llama3-8b-8192",
-    temperature=0
-)
-llm_with_tools = llm.bind_tools(tools)
-
-
-
-# ========== 8. LANGGRAPH PIPELINE ==========
-
-
-def chatbot_node(state: ChatbotState) -> ChatbotState:
-    validate_state(state)
-
-    messages = state.get("messages", [])
-
-    system_prompt = """
-You are a helpful AI assistant specialized in LinkedIn profile coaching.
-
-You can:
-- Answer user questions.
-- If user is greeting , greet him back also telling how you can help him.
-- You should proactively use specialized tools whenever possible to give richer, data-driven answers.
-IMPORTANT RULES:
-- You must call at most one tool at a time.
-- Never call multiple tools together in the same step.
-- If user asks to show any section, use extract_from_state_tool, and after that, show the exact result from it.
-- If information about that section is already known, dont call extract_from_state_tool, directly answer the user query.
-- call profile_analyzer function only when full profile analysis is needed, otherwise rely on extract_from_state_tool.
-- If user asks to enhance any section, check if it is there in history, otherwise call extract_from_state_tool first.
-- Prefer to call a tool when answering instead of directly replying, especially if it can add new, useful insights or up-to-date data.
-- If a tool has been recently used and new info isn’t needed, you may answer directly.
-- Use tools to verify assumptions, enrich answers, or when the user asks about strengths, weaknesses, job fit, or wants improvements.
-
-Always respond helpfully, clearly, and with actionable advice to guide the user in improving their LinkedIn profile.
-"""
-
-    # Build messages & invoke LLM
-    messages = [SystemMessage(content=system_prompt)] + messages[-2:]
-    # messages = [SystemMessage(content=system_prompt)]
-    response = llm_with_tools.invoke(messages)
-    if hasattr(response, "tool_calls") and response.tool_calls:
-        first_tool = response.tool_calls[0]
-        tool_name = first_tool.get("name") if isinstance(first_tool, dict) else getattr(first_tool, "name", None)
-        tool_args = first_tool.get("args") if isinstance(first_tool, dict) else getattr(first_tool, "args", {})
-        print(f"[DEBBBBUUUUGGG] using tool {tool_name}")
-
-    # DEBUG
-    print("[DEBUG] LLM response:", response)
-    state.setdefault("messages", []).append(response)
-
-    return state
-
-
-
-
-
-# --- Graph definition ---
-graph = StateGraph(state_schema=ChatbotState)
-graph.add_node("chatbot", chatbot_node)
-graph.add_node("tools", ToolNode(tools))
-graph.add_edge(START, "chatbot")
-graph.add_conditional_edges("chatbot", tools_condition)
-graph.add_edge("tools","chatbot")
-graph.set_entry_point("chatbot")
-
-# --- Streamlit UI ---
-st.set_page_config(page_title="💼 LinkedIn AI Career Assistant", page_icon="🤖", layout="wide")
-st.title("🧑‍💼 LinkedIn AI Career Assistant")
-
-# --- Checkpointer and graph initialization ---
-if "checkpointer" not in st.session_state:
-    if SQLITE_AVAILABLE:
-        conn = sqlite3.connect("checkpoints1.db", check_same_thread=False)
-        st.session_state["checkpointer"] = SqliteSaver(conn)
-    else:
-        st.session_state["checkpointer"] = MemorySaver()
-checkpointer = st.session_state["checkpointer"]
-
-if "app_graph" not in st.session_state:
-    st.session_state["app_graph"] = graph.compile(checkpointer=checkpointer)
-app_graph = st.session_state["app_graph"]
-# Find or create thread
-def find_thread_id_for_url(checkpointer, url, max_threads=100):
-    search_url = normalize_url(url)
-    for tid in range(max_threads):
-        config = {"configurable": {"thread_id": str(tid), "checkpoint_ns": ""}}
-        state = checkpointer.get(config)
-        if state and "channel_values" in state:
-            user_state = state["channel_values"]
-            stored_url = normalize_url(user_state.get("profile_url", ""))
-            if stored_url == search_url:
-                return str(tid), user_state
-    return None, None
-
-def delete_thread_checkpoint(checkpointer, thread_id):
-    # For SqliteSaver, use the delete_thread method if available
-    if hasattr(checkpointer, "delete_thread"):
-        checkpointer.delete_thread(thread_id)
-    else:
-        # For in-memory or custom checkpointers, implement as needed
-        pass
-
-
-def get_next_thread_id(checkpointer, max_threads=100):
-    used = set()
-    for tid in range(max_threads):
-        config = {"configurable": {"thread_id": str(tid), "checkpoint_ns": ""}}
-        if checkpointer.get(config):
-            used.add(tid)
-    for tid in range(max_threads):
-        if tid not in used:
-            return str(tid)
-    raise RuntimeError("No available thread_id")
-
-# --- Session selection and state initialization ---
-
-if "chat_mode" not in st.session_state:
-    profile_url = st.text_input("Profile URL (e.g., https://www.linkedin.com/in/username/)")
-    if not profile_url:
-        st.info("Please enter a valid LinkedIn profile URL above to start.")
-        st.stop()
-
-    valid_pattern = r"^https://www\.linkedin\.com/in/[^/]+/?$"
-    if not re.match(valid_pattern, profile_url.strip()):
-        st.error("❌ Invalid LinkedIn profile URL. Make sure it matches the format.")
-        st.stop()
-    url = profile_url.strip()
-
-    existing_thread_id, previous_state = find_thread_id_for_url(checkpointer, url)
-    # Defensive: ensure required fields
-    required_fields = ["profile", "sections"]
-    if previous_state and not all(f in previous_state and previous_state[f] for f in required_fields):
-        st.warning("Previous session is missing required data. Please start a new chat.")
-        previous_state = None
-
-    if previous_state:
-        st.info("A previous session found. Choose:")
-        col1, col2 = st.columns(2)
-        if col1.button("Continue previous chat"):
-            st.session_state["chat_mode"] = "continue"
-            st.session_state["thread_id"] = existing_thread_id
-            st.session_state.state = previous_state
-            st.rerun()
-        elif col2.button("Start new chat"):
-            delete_thread_checkpoint(checkpointer, existing_thread_id)
-            with st.spinner("Fetching and processing profile... ⏳"):
-                raw=scrape_linkedin_profile(url)
-            thread_id = existing_thread_id
-            st.session_state["chat_mode"] = "new"
-            st.session_state["thread_id"] = thread_id
-            st.session_state.state = initialize_state(raw)
-            st.session_state.state["profile_url"] = normalize_url(url)
-            st.session_state.state["messages"] = []
-            st.rerun()
-        st.stop()
-    else:
-        with st.spinner("Fetching and processing profile... ⏳"):
-                raw=scrape_linkedin_profile(url)
-        thread_id = get_next_thread_id(checkpointer)
-        st.session_state["thread_id"] = thread_id
-        st.session_state["chat_mode"] = "new"
-        st.session_state.state = initialize_state(raw)
-        st.session_state.state["profile_url"] = normalize_url(url)
-        st.session_state.state["messages"] = []
-        st.rerun()
-
-# --- Main chat UI (only after chat_mode is set) ---
-state = st.session_state.state
-thread_id = st.session_state.get("thread_id")
-
-st.subheader("💬 Chat with your AI Assistant")
-messages = state.get("messages", [])
-chat_container = st.container()
-
-with chat_container:
-    st.markdown(
-        """
-        <style>
-        .chat-row { display: flex; width: 100%; margin-bottom: 12px; animation: fadeIn 0.5s; }
-        .chat-row.user { justify-content: flex-end; }
-        .chat-row.ai { justify-content: flex-start; }
-        .chat-bubble { font-family: 'Segoe UI', 'Roboto', 'Arial', sans-serif; font-size: 1.08rem; line-height: 1.65; padding: 14px 22px; border-radius: 20px; min-width: 60px; max-width: 75vw; box-shadow: 0 2px 12px rgba(0,0,0,0.10); word-break: break-word; display: inline-block; position: relative; margin-bottom: 2px; }
-        .bubble-user { background: linear-gradient(90deg, #43e97b 0%, #38f9d7 100%); color: #fff; border-bottom-right-radius: 6px; border-top-right-radius: 22px; text-align: right; box-shadow: 0 4px 16px rgba(67,233,123,0.13); }
-        .bubble-ai { background: linear-gradient(90deg, #e3f0ff 0%, #c9eaff 100%); color: #1a237e; border-bottom-left-radius: 6px; border-top-left-radius: 22px; text-align: left; border: 1.5px solid #b3e0fc; box-shadow: 0 4px 16px rgba(44, 62, 80, 0.08); }
-        .bubble-unknown { background: #fffbe6; color: #8a6d3b; border-radius: 14px; text-align: center; border: 1px solid #ffe082; display: inline-block; }
-        .sender-label { font-size: 0.93em; font-weight: 600; opacity: 0.7; margin-bottom: 4px; display: block; }
-        .avatar { width: 38px; height: 38px; border-radius: 50%; margin-right: 10px; margin-top: 2px; background: #e0e0e0; object-fit: cover; box-shadow: 0 2px 6px rgba(0,0,0,0.07); }
-        @keyframes fadeIn { from { opacity: 0; transform: translateY(12px);} to { opacity: 1; transform: translateY(0);} }
-        </style>
-        """,
-        unsafe_allow_html=True,
-    )
-
-    job_fit = state.get("job_fit")
-    for msg in messages:
-        if isinstance(msg, HumanMessage):
-            st.markdown(
-                f"""
-                <div class="chat-row user">
-                    <div class="chat-bubble bubble-user">
-                        <span class="sender-label">🧑‍💻 You</span>
-                        {msg.content}
-                    </div>
-                </div>
-                """,
-                unsafe_allow_html=True,
-            )
-        elif isinstance(msg, AIMessage):
-            if not msg.content or not msg.content.strip():
-                continue
-            st.markdown(
-                f"""
-                <div class="chat-row ai">
-                    <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="AI"/>
-                    <div class="chat-bubble bubble-ai">
-                        <span class="sender-label">🤖 AI</span>
-                        {msg.content}
-                    </div>
-                </div>
-                """,
-                unsafe_allow_html=True,
-            )
-        elif isinstance(msg, ToolMessage):
-            raw_content = msg.content or "(no content)"
-            try:
-                parsed = json.loads(raw_content)
-            except Exception:
-                parsed = None
-
-            if parsed and isinstance(parsed, dict):
-                # --- Profile analysis format ---
-                if all(k in parsed for k in ("strengths", "weaknesses", "suggestions")):
-                    strengths = parsed["strengths"]
-                    weaknesses = parsed["weaknesses"]
-                    suggestions = parsed["suggestions"]
-
-                    formatted = (
-                        "### 💪 **Strengths**\n"
-                        f"- **Technical:** {', '.join(strengths.get('technical', []) or ['None'])}\n"
-                        f"- **Projects:** {', '.join(strengths.get('projects', []) or ['None'])}\n"
-                        f"- **Education:** {', '.join(strengths.get('education', []) or ['None'])}\n"
-                        f"- **Soft Skills:** {', '.join(strengths.get('soft_skills', []) or ['None'])}\n\n"
-                        "### ⚠️ **Weaknesses**\n"
-                        f"- **Technical Gaps:** {', '.join(weaknesses.get('technical_gaps', []) or ['None'])}\n"
-                        f"- **Project/Experience Gaps:** {', '.join(weaknesses.get('project_or_experience_gaps', []) or ['None'])}\n"
-                        f"- **Missing Context:** {', '.join(weaknesses.get('missing_context', []) or ['None'])}\n\n"
-                        "### 🛠 **Suggestions to improve**\n"
-                        + "\n".join(f"- {s}" for s in suggestions)
-                    )
-
-                    st.markdown(f"""
-                        <div class="chat-row ai">
-                            <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
-                            <div class="chat-bubble bubble-ai">
-                                <span class="sender-label">📊 Profile Analysis</span>
-                                {formatted}
-                            </div>
-                        </div>
-                    """, unsafe_allow_html=True)
-
-                # --- Job fit format ---
-                elif "match_score" in parsed:
-                    percent = parsed["match_score"]
-                    suggestions = parsed.get("suggestions", [])
-                    missing = parsed.get("missing_skills", [])
-                    target_role = parsed.get('target_role', 'unspecified')
-                    state["target_role"]=target_role
-                    suggestions_html = "<br>".join(f"• {s}" for s in suggestions)
-                    missing_html = "<br>".join(f"• {s}" for s in missing)
-
-                    st.markdown(f"""
-                        <div class="chat-row ai">
-                            <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
-                            <div class="chat-bubble bubble-ai">
-                                <span class="sender-label">📊 Job Fit</span>
-                                <b>🎯 Target Role:</b> {target_role}<br>
-                                <div style="
-                                    width: 120px; height: 120px; border-radius: 50%;
-                                    background: conic-gradient(#25D366 {percent * 3.6}deg, #e0e0e0 0deg);
-                                    display: flex; align-items: center; justify-content: center;
-                                    font-size: 1.8rem; color: #333; margin: 10px auto;">
-                                    {percent}%
-                                </div>
-                                <b>Missing Skills:</b><br>{missing_html}<br><br>
-                                <b>Suggestions:</b><br>{suggestions_html}
-                            </div>
-                        </div>
-                    """, unsafe_allow_html=True)
-
-                # --- Section text format ---
-                elif "result" in parsed:
-                    text = parsed["result"]
-                    st.markdown(f"""
-                        <div class="chat-row ai">
-                            <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
-                            <div class="chat-bubble bubble-ai">
-                                <span class="sender-label">📄 Section Content</span>
-                                {text}
-                            </div>
-                        </div>
-                    """, unsafe_allow_html=True)
-
-        else:
-            st.markdown(
-                f"""
-                <div class="chat-row">
-                    <div class="chat-bubble bubble-unknown">
-                        <span class="sender-label">⚠️ Unknown</span>
-                        {getattr(msg, 'content', str(msg))}
-                    </div>
-                </div>
-                """,
-                unsafe_allow_html=True,
-            )
-    st.markdown('<div style="clear:both"></div>', unsafe_allow_html=True)
-
-st.markdown("---")
-
-user_input = st.chat_input(
-    placeholder="Ask about your LinkedIn profile, e.g., 'Analyze my profile, how do I fit for AI role, how is my about section?'" 
-)
-
-if user_input and user_input.strip():
-    state.setdefault("messages", []).append(HumanMessage(content=user_input.strip()))
-    validate_state(state)
-    thread_id = st.session_state.get("thread_id")
-    config = {"configurable": {"thread_id": thread_id}}
-    with st.spinner("Processing your request..."):
-        st.session_state.state = app_graph.invoke(state, config)
-    st.rerun()
+import os
+import json
+import re
+import time
+from typing import Dict, Any, List, Optional, Annotated
+from chatbot_model import (
+    UserMemory,
+    ChatbotState,
+    ProfileAnalysisModel,
+    JobFitModel,
+    ContentGenerationModel,
+    
+)
+from llm_utils import call_llm_and_parse
+from profile_preprocessing import (
+    preprocess_profile,
+    initialize_state,
+    normalize_url
+)
+from openai import OpenAI
+import streamlit as st
+import hashlib
+from dotenv import load_dotenv
+from pydantic import BaseModel, Field,ValidationError
+# import pdb; pdb.set_trace()
+from scraping_profile import scrape_linkedin_profile
+from langchain_openai import ChatOpenAI
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage,BaseMessage,ToolMessage
+from langchain_core.tools import tool
+from langgraph.graph import StateGraph, END,START
+from langgraph.checkpoint.memory import MemorySaver
+from langgraph.graph import add_messages  # if your framework exposes this
+from langgraph.prebuilt import ToolNode,tools_condition,InjectedState
+import dirtyjson
+import sqlite3
+try:
+    from langgraph.checkpoint.sqlite import SqliteSaver
+    SQLITE_AVAILABLE = True
+except ImportError:
+    SQLITE_AVAILABLE = False
+
+
+
+# ========== 1. ENVIRONMENT & LLM SETUP ==========
+load_dotenv()
+groq_key = os.getenv("GROQ_API_KEY")
+assert groq_key, "GROQ_API_KEY not found in environment!"
+groq_client=OpenAI(
+            api_key=os.getenv("GROQ_API_KEY"),
+            base_url="https://api.groq.com/openai/v1"
+        )
+
+def normalize_url(url):
+    return url.strip().rstrip('/')
+
+def validate_state(state: dict) -> None:
+    """
+    Validate given state dict against ChatbotState schema.
+    Displays result in Streamlit instead of printing.
+    """
+    # st.write("=== Validating chatbot state ===")
+    try:
+        ChatbotState.model_validate(state)
+        # st.success("✅ State is valid!")
+    except ValidationError as e:
+        st.error("❌ Validation failed!")
+        errors_list = []
+        for error in e.errors():
+            loc = " → ".join(str(item) for item in error['loc'])
+            msg = error['msg']
+            errors_list.append(f"- At: {loc}\n  Error: {msg}")
+        st.write("\n".join(errors_list))
+        # Optionally show raw validation error too:
+        st.expander("See raw validation error").write(str(e))
+        st.stop()
+
+
+user_memory = UserMemory()
+
+# ========== 7. AGENT FUNCTIONS ==========
+
+def profile_analysis_prompt(profile: Dict[str, str]) -> str:
+    return f"""
+You are a top-tier LinkedIn career coach and AI analyst.
+
+Analyze the following candidate profile carefully.
+
+Candidate profile data:
+FullName: {profile.get("FullName", "")}
+Headline: {profile.get("Headline", "")}
+JobTitle: {profile.get("JobTitle", "")}
+CompanyName: {profile.get("CompanyName", "")}
+CompanyIndustry: {profile.get("CompanyIndustry", "")}
+CurrentJobDuration: {profile.get("CurrentJobDuration", "")}
+About: {profile.get("About", "")}
+Experiences: {profile.get("Experiences", "")}
+Skills: {profile.get("Skills", "")}
+Educations: {profile.get("Educations", "")}
+Certifications: {profile.get("Certifications", "")}
+HonorsAndAwards: {profile.get("HonorsAndAwards", "")}
+Verifications: {profile.get("Verifications", "")}
+Highlights: {profile.get("Highlights", "")}
+Projects: {profile.get("Projects", "")}
+Publications: {profile.get("Publications", "")}
+Patents: {profile.get("Patents", "")}
+Courses: {profile.get("Courses", "")}
+TestScores: {profile.get("TestScores", "")}
+
+
+Identify and summarize:
+1. strengths:
+    - technical strengths (skills, tools, frameworks)
+    - project strengths (impactful projects, innovation)
+    - educational strengths (degrees, certifications, awards)
+    - soft skills and personality traits (teamwork, leadership)
+2. weaknesses:
+    - missing or weak technical skills
+    - gaps in projects, experience, or education
+    - unclear profile sections or missing context
+3. actionable suggestions:
+    - concrete ways to improve profile headline, about section, or add projects
+    - suggestions to learn or highlight new skills
+    - ideas to make the profile more attractive for recruiters
+
+Important instructions:
+- Respond ONLY with valid JSON.
+- Do NOT include text before or after JSON.
+- Be concise but detailed.
+
+
+
+Example JSON format:
+{{
+  "strengths": {{
+    "technical": ["...", "..."],
+    "projects": ["...", "..."],
+    "education": ["...", "..."],
+    "soft_skills": ["...", "..."]
+  }},
+  "weaknesses": {{
+    "technical_gaps": ["...", "..."],
+    "project_or_experience_gaps": ["...", "..."],
+    "missing_context": ["...", "..."]
+  }},
+  "suggestions": [
+    "...",
+    "...",
+    "..."
+  ]
+}}
+""".strip()
+
+
+
+
+def job_fit_prompt(sections: Dict[str, str], target_role: str) -> str:
+    return f"""
+You are an expert career coach and recruiter.
+
+Compare the following candidate profile against the typical requirements for the role of "{target_role}".
+
+Candidate Profile:
+- Headline: {sections.get('headline', '')}
+- About: {sections.get('about', '')}
+- Job Title: {sections.get('job_title', '')}
+- Company: {sections.get('company_name', '')}
+- Industry: {sections.get('company_industry', '')}
+- Current Job Duration: {sections.get('current_job_duration', '')}
+- Skills: {sections.get('skills', '')}
+- Projects: {sections.get('projects', '')}
+- Educations: {sections.get('educations', '')}
+- Certifications: {sections.get('certifications', '')}
+- Honors & Awards: {sections.get('honors_and_awards', '')}
+- Experiences: {sections.get('experiences', '')}
+
+**Instructions:**
+- Respond ONLY with valid JSON.
+- Your JSON must exactly match the following schema:
+{{
+  "match_score": 85,
+  "missing_skills": ["Skill1", "Skill2"],
+  "suggestions": ["...", "...", "..."]
+}}
+- "match_score": integer from 0–100 estimating how well the profile fits the target role.
+- "missing_skills": key missing or weakly mentioned skills.
+- "suggestions": 3 actionable recommendations to improve fit (e.g., learn tools, rewrite headline).
+
+Do NOT include explanations, text outside JSON, or markdown.
+Start with '{{' and end with '}}'.
+The JSON must be directly parseable.
+""".strip()
+
+
+# --- Tool: Profile Analyzer ---
+@tool
+def profile_analyzer(state: Annotated[ChatbotState, InjectedState]) -> dict:
+    """
+    Tool: Analyze the overall full user's profile to give strengths, weaknesses, suggestions.
+    This is needed only if full analysis of profile is needed. 
+    Returns the full analysis in the form of a json.
+
+    - It takes no arguments
+    """
+
+
+    # Get summarized profile (dictionary of strings)
+    profile = getattr(state, "profile", {}) or {}
+
+    # Build prompt
+    prompt = profile_analysis_prompt(profile)
+
+    # Call the LLM & parse structured result
+    analysis_model = call_llm_and_parse(groq_client,prompt, ProfileAnalysisModel)
+    analysis_dict = analysis_model.model_dump()
+
+    # Save to state and user memory
+    state.profile_analysis = analysis_dict
+    user_memory.save("profile_analysis", analysis_dict)
+
+    print("💾 [DEBUG] Saved analysis to user memory.")
+    print("📦 [DEBUG] Updated state.profile_analysis with analysis.")
+
+    return analysis_dict
+
+# --- Tool: Job Matcher ---
+
+
+@tool
+def job_matcher(
+    state: Annotated[ChatbotState, InjectedState],
+    target_role: str = None
+) -> dict:
+    """
+    Tool: Analyze how well the user's profile fits the target role.
+    - If user is asking if he is a good fit for a certain role, or needs to see if his profile is compatible with a certain role, call this.
+    - Takes target_role as an argument.
+    - this tool is needed when match score, missing skills, suggestions are needed based on a job name given.
+    """
+    print(f"target role is {target_role}")
+    # Update state.target_role if provided
+
+    sections = getattr(state, "sections", {})
+
+    # Build prompt
+    prompt = job_fit_prompt(sections, target_role)
+
+    # Call LLM and parse
+    try:
+        job_fit_model = call_llm_and_parse(groq_client,prompt, JobFitModel)
+        job_fit_dict = job_fit_model.model_dump()
+        job_fit_dict["target_role"] = target_role
+    except Exception as e:
+        job_fit_dict = {
+            "target_role":target_role,
+            "match_score": 0,
+            "missing_skills": [],
+            "suggestions": ["Parsing failed or incomplete response."]
+        }
+
+    # Save to state and user memory
+    state.job_fit = job_fit_dict
+    user_memory.save("job_fit", job_fit_dict)
+
+    return job_fit_dict
+
+
+
+
+
+
+@tool
+def extract_from_state_tool(
+    state: Annotated[ChatbotState, InjectedState],
+    key: str
+) -> dict:
+    """
+    This tool is used if user wants to ask about any particular part of this profile. Use this if a singe section is targeted. It expects key as an arguement, that represents what
+    the user is wanting to look at, from his profile.
+    Argument:
+      key: only pass one from the below list, identify one thing the user wants to look into and choose that:
+        "sections.about", "sections.headline", "sections.skills", "sections.projects",
+        "sections.educations", "sections.certifications", "sections.honors_and_awards",
+        "sections.experiences", "sections.publications", "sections.patents",
+        "sections.courses", "sections.test_scores", "sections.verifications",
+        "sections.highlights", "sections.job_title", "sections.company_name",
+        "sections.company_industry", "sections.current_job_duration", "sections.full_name",
+        "enhanced_content,"profile_analysis", "job_fit", "target_role", "editing_section"
+    """
+    value = state
+    try:
+        for part in key.split('.'):
+            # Support both dict and Pydantic model
+            if isinstance(value, dict):
+                value = value.get(part)
+            elif hasattr(value, part):
+                value = getattr(value, part)
+            else:
+                value = None
+            if value is None:
+                break
+    except Exception:
+        value = None
+    return {"result": value}
+
+
+tools = [
+    profile_analyzer,
+   job_matcher,
+    extract_from_state_tool
+]
+llm = ChatOpenAI(
+    api_key=groq_key,
+    base_url="https://api.groq.com/openai/v1",
+    model="llama3-8b-8192",
+    temperature=0
+)
+llm_with_tools = llm.bind_tools(tools)
+
+
+
+# ========== 8. LANGGRAPH PIPELINE ==========
+
+
+def chatbot_node(state: ChatbotState) -> ChatbotState:
+    validate_state(state)
+
+    messages = state.get("messages", [])
+
+    system_prompt = """
+You are a helpful AI assistant specialized in LinkedIn profile coaching.
+
+Guidelines:
+- Greet the user if they greet you, and explain you can help analyze, enhance, and improve their LinkedIn profile.
+- Prefer using tools instead of answering directly whenever this can give better, data-backed answers.
+- Call only one tool at a time. Never call multiple tools together.
+
+When to use tools:
+- If the user asks to show a section (like About, Projects, etc.): call extract_from_state_tool, unless you already have that section stored.
+- If the user asks to enhance a section: use extract_from_state_tool first if you don’t already have that section, then enhance it.
+- If the user requests a full profile analysis: use profile_analyzer.
+- If the user wants to know how well they fit a target job role: use job_matcher with the given role.
+- Use tools to check strengths, weaknesses, missing skills, or improvement suggestions.
+- If the tool was just called recently and info is still fresh, you may answer directly.
+
+Important:
+- Never describe or print JSON of a tool call.
+- Never say "I'm about to call a tool" — just call the tool properly.
+- Keep answers clear, helpful, and actionable.
+
+Your goal: help the user see, improve, and analyze their LinkedIn profile.
+
+"""
+    recent_messages = []
+    for msg in messages[-6:]:  # last few, e.g., 6
+        if isinstance(msg, HumanMessage):
+            recent_messages.append({
+                "role": "user",
+                "content": f"User asked: {msg.content}"
+            })
+        elif isinstance(msg, AIMessage):
+        # keep only non-empty AI replies (actual answers)
+            if msg.content.strip():
+                recent_messages.append({
+                    "role": "assistant",
+                    "content": msg.content
+                })
+        elif isinstance(msg, ToolMessage):
+            recent_messages.append({
+                "role": "assistant",
+                "content": f"[Tool: {msg.name}] {msg.content}"
+            })
+
+
+    # Build messages & invoke LLM
+    messages = [SystemMessage(content=system_prompt)] + recent_messages
+    # messages = [SystemMessage(content=system_prompt)]
+    response = llm_with_tools.invoke(messages)
+    if hasattr(response, "tool_calls") and response.tool_calls:
+        first_tool = response.tool_calls[0]
+        tool_name = first_tool.get("name") if isinstance(first_tool, dict) else getattr(first_tool, "name", None)
+        tool_args = first_tool.get("args") if isinstance(first_tool, dict) else getattr(first_tool, "args", {})
+        print(f"[DEBBBBUUUUGGG] using tool {tool_name}")
+
+    # DEBUG
+    print("[DEBUG] LLM response:", response)
+    state.setdefault("messages", []).append(response)
+
+    return state
+
+
+
+
+
+# --- Graph definition ---
+graph = StateGraph(state_schema=ChatbotState)
+graph.add_node("chatbot", chatbot_node)
+graph.add_node("tools", ToolNode(tools))
+graph.add_edge(START, "chatbot")
+graph.add_conditional_edges("chatbot", tools_condition)
+graph.add_edge("tools","chatbot")
+graph.set_entry_point("chatbot")
+
+# --- Streamlit UI ---
+st.set_page_config(page_title="💼 LinkedIn AI Career Assistant", page_icon="🤖", layout="wide")
+st.title("🧑‍💼 LinkedIn AI Career Assistant")
+
+# --- Checkpointer and graph initialization ---
+if "checkpointer" not in st.session_state:
+    if SQLITE_AVAILABLE:
+        import os
+        print("Current working directory:", os.getcwd())
+        print("Files in working directory:", os.listdir("."))
+
+        conn = sqlite3.connect("checkpoints1.db", check_same_thread=False)
+        st.session_state["checkpointer"] = SqliteSaver(conn)
+    else:
+        st.session_state["checkpointer"] = MemorySaver()
+checkpointer = st.session_state["checkpointer"]
+
+if "app_graph" not in st.session_state:
+    st.session_state["app_graph"] = graph.compile(checkpointer=checkpointer)
+app_graph = st.session_state["app_graph"]
+# Find or create thread
+def find_thread_id_for_url(checkpointer, url, max_threads=100):
+    search_url = normalize_url(url)
+    for tid in range(max_threads):
+        config = {"configurable": {"thread_id": str(tid), "checkpoint_ns": ""}}
+        state = checkpointer.get(config)
+        if state and "channel_values" in state:
+            user_state = state["channel_values"]
+            stored_url = normalize_url(user_state.get("profile_url", ""))
+            if stored_url == search_url:
+                return str(tid), user_state
+    return None, None
+
+def delete_thread_checkpoint(checkpointer, thread_id):
+    # For SqliteSaver, use the delete_thread method if available
+    if hasattr(checkpointer, "delete_thread"):
+        checkpointer.delete_thread(thread_id)
+    else:
+        # For in-memory or custom checkpointers, implement as needed
+        pass
+
+
+def get_next_thread_id(checkpointer, max_threads=100):
+    used = set()
+    for tid in range(max_threads):
+        config = {"configurable": {"thread_id": str(tid), "checkpoint_ns": ""}}
+        if checkpointer.get(config):
+            used.add(tid)
+    for tid in range(max_threads):
+        if tid not in used:
+            return str(tid)
+    raise RuntimeError("No available thread_id")
+
+# --- Session selection and state initialization ---
+
+if "chat_mode" not in st.session_state:
+    profile_url = st.text_input("Profile URL (e.g., https://www.linkedin.com/in/username/)")
+    if not profile_url:
+        st.info("Please enter a valid LinkedIn profile URL above to start.")
+        st.stop()
+
+    valid_pattern = r"^https://www\.linkedin\.com/in/[^/]+/?$"
+    if not re.match(valid_pattern, profile_url.strip()):
+        st.error("❌ Invalid LinkedIn profile URL. Make sure it matches the format.")
+        st.stop()
+    url = profile_url.strip()
+
+    existing_thread_id, previous_state = find_thread_id_for_url(checkpointer, url)
+    # Defensive: ensure required fields
+    required_fields = ["profile", "sections"]
+    if previous_state and not all(f in previous_state and previous_state[f] for f in required_fields):
+        st.warning("Previous session is missing required data. Please start a new chat.")
+        previous_state = None
+
+    if previous_state:
+        st.info("A previous session found. Choose:")
+        col1, col2 = st.columns(2)
+        if col1.button("Continue previous chat"):
+            st.session_state["chat_mode"] = "continue"
+            st.session_state["thread_id"] = existing_thread_id
+            st.session_state.state = previous_state
+            st.rerun()
+        elif col2.button("Start new chat"):
+            delete_thread_checkpoint(checkpointer, existing_thread_id)
+            with st.spinner("Fetching and processing profile... ⏳"):
+                raw=scrape_linkedin_profile(url)
+            thread_id = existing_thread_id
+            st.session_state["chat_mode"] = "new"
+            st.session_state["thread_id"] = thread_id
+            st.session_state.state = initialize_state(raw)
+            st.session_state.state["profile_url"] = normalize_url(url)
+            st.session_state.state["messages"] = []
+            st.rerun()
+        st.stop()
+    else:
+        with st.spinner("Fetching and processing profile... ⏳"):
+                raw=scrape_linkedin_profile(url)
+        thread_id = get_next_thread_id(checkpointer)
+        st.session_state["thread_id"] = thread_id
+        st.session_state["chat_mode"] = "new"
+        st.session_state.state = initialize_state(raw)
+        st.session_state.state["profile_url"] = normalize_url(url)
+        st.session_state.state["messages"] = []
+        st.rerun()
+
+# --- Main chat UI (only after chat_mode is set) ---
+state = st.session_state.state
+thread_id = st.session_state.get("thread_id")
+
+st.subheader("💬 Chat with your AI Assistant")
+messages = state.get("messages", [])
+chat_container = st.container()
+
+with chat_container:
+    st.markdown(
+        """
+        <style>
+        .chat-row { display: flex; width: 100%; margin-bottom: 12px; animation: fadeIn 0.5s; }
+        .chat-row.user { justify-content: flex-end; }
+        .chat-row.ai { justify-content: flex-start; }
+        .chat-bubble { font-family: 'Segoe UI', 'Roboto', 'Arial', sans-serif; font-size: 1.08rem; line-height: 1.65; padding: 14px 22px; border-radius: 20px; min-width: 60px; max-width: 75vw; box-shadow: 0 2px 12px rgba(0,0,0,0.10); word-break: break-word; display: inline-block; position: relative; margin-bottom: 2px; }
+        .bubble-user { background: linear-gradient(90deg, #43e97b 0%, #38f9d7 100%); color: #fff; border-bottom-right-radius: 6px; border-top-right-radius: 22px; text-align: right; box-shadow: 0 4px 16px rgba(67,233,123,0.13); }
+        .bubble-ai { background: linear-gradient(90deg, #e3f0ff 0%, #c9eaff 100%); color: #1a237e; border-bottom-left-radius: 6px; border-top-left-radius: 22px; text-align: left; border: 1.5px solid #b3e0fc; box-shadow: 0 4px 16px rgba(44, 62, 80, 0.08); }
+        .bubble-unknown { background: #fffbe6; color: #8a6d3b; border-radius: 14px; text-align: center; border: 1px solid #ffe082; display: inline-block; }
+        .sender-label { font-size: 0.93em; font-weight: 600; opacity: 0.7; margin-bottom: 4px; display: block; }
+        .avatar { width: 38px; height: 38px; border-radius: 50%; margin-right: 10px; margin-top: 2px; background: #e0e0e0; object-fit: cover; box-shadow: 0 2px 6px rgba(0,0,0,0.07); }
+        @keyframes fadeIn { from { opacity: 0; transform: translateY(12px);} to { opacity: 1; transform: translateY(0);} }
+        </style>
+        """,
+        unsafe_allow_html=True,
+    )
+
+    job_fit = state.get("job_fit")
+    for msg in messages:
+        if isinstance(msg, HumanMessage):
+            st.markdown(
+                f"""
+                <div class="chat-row user">
+                    <div class="chat-bubble bubble-user">
+                        <span class="sender-label">🧑‍💻 You</span>
+                        {msg.content}
+                    </div>
+                </div>
+                """,
+                unsafe_allow_html=True,
+            )
+        elif isinstance(msg, AIMessage):
+            if not msg.content or not msg.content.strip():
+                continue
+            st.markdown(
+                f"""
+                <div class="chat-row ai">
+                    <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="AI"/>
+                    <div class="chat-bubble bubble-ai">
+                        <span class="sender-label">🤖 AI</span>
+                        {msg.content}
+                    </div>
+                </div>
+                """,
+                unsafe_allow_html=True,
+            )
+        elif isinstance(msg, ToolMessage):
+            raw_content = msg.content or "(no content)"
+            try:
+                parsed = json.loads(raw_content)
+            except Exception:
+                parsed = None
+
+            if parsed and isinstance(parsed, dict):
+                # --- Profile analysis format ---
+                if all(k in parsed for k in ("strengths", "weaknesses", "suggestions")):
+                    strengths = parsed["strengths"]
+                    weaknesses = parsed["weaknesses"]
+                    suggestions = parsed["suggestions"]
+                    formatted_html = f"""
+                    <h3>💪 <b>Strengths</b></h3>
+                    <ul>
+                    <li><b>Technical:</b> {', '.join(strengths.get('technical', []) or ['None'])}</li>
+                    <li><b>Projects:</b> {', '.join(strengths.get('projects', []) or ['None'])}</li>
+                    <li><b>Education:</b> {', '.join(strengths.get('education', []) or ['None'])}</li>
+                    <li><b>Soft Skills:</b> {', '.join(strengths.get('soft_skills', []) or ['None'])}</li>
+                    </ul>
+
+                    <h3>⚠️ <b>Weaknesses</b></h3>
+                    <ul>
+                    <li><b>Technical Gaps:</b> {', '.join(weaknesses.get('technical_gaps', []) or ['None'])}</li>
+                    <li><b>Project/Experience Gaps:</b> {', '.join(weaknesses.get('project_or_experience_gaps', []) or ['None'])}</li>
+                    <li><b>Missing Context:</b> {', '.join(weaknesses.get('missing_context', []) or ['None'])}</li>
+                    </ul>
+
+                    <h3>🛠 <b>Suggestions to improve</b></h3>
+                    <ul>
+                    {''.join(f'<li>{s}</li>' for s in suggestions)}
+                    </ul>
+                    """
+
+                    st.markdown(f"""
+                    <div class="chat-row ai">
+                    <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
+                    <div class="chat-bubble bubble-ai">
+                        <span class="sender-label">📊 Profile Analysis</span>
+                        {formatted_html}
+                    </div>
+                    </div>
+                    """, unsafe_allow_html=True)
+
+
+
+                # --- Job fit format ---
+                elif "match_score" in parsed:
+                    percent = parsed["match_score"]
+                    suggestions = parsed.get("suggestions", [])
+                    missing = parsed.get("missing_skills", [])
+                    target_role = parsed.get('target_role', 'unspecified')
+                    state["target_role"]=target_role
+                    suggestions_html = "<br>".join(f"• {s}" for s in suggestions)
+                    missing_html = "<br>".join(f"• {s}" for s in missing)
+
+                    st.markdown(f"""
+                        <div class="chat-row ai">
+                            <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
+                            <div class="chat-bubble bubble-ai">
+                                <span class="sender-label">📊 Job Fit</span>
+                                <b>🎯 Target Role:</b> {target_role}<br>
+                                <div style="
+                                    width: 120px; height: 120px; border-radius: 50%;
+                                    background: conic-gradient(#25D366 {percent * 3.6}deg, #e0e0e0 0deg);
+                                    display: flex; align-items: center; justify-content: center;
+                                    font-size: 1.8rem; color: #333; margin: 10px auto;">
+                                    {percent}%
+                                </div>
+                                <b>Missing Skills:</b><br>{missing_html}<br><br>
+                                <b>Suggestions:</b><br>{suggestions_html}
+                            </div>
+                        </div>
+                    """, unsafe_allow_html=True)
+
+                # --- Section text format ---
+                elif "result" in parsed:
+                    text = parsed["result"]
+                    st.markdown(f"""
+                        <div class="chat-row ai">
+                            <img class="avatar" src="https://img.icons8.com/ios-filled/50/1a237e/robot-2.png" alt="Tool"/>
+                            <div class="chat-bubble bubble-ai">
+                                <span class="sender-label">📄 Section Content</span>
+                                {text}
+                            </div>
+                        </div>
+                    """, unsafe_allow_html=True)
+
+        else:
+            st.markdown(
+                f"""
+                <div class="chat-row">
+                    <div class="chat-bubble bubble-unknown">
+                        <span class="sender-label">⚠️ Unknown</span>
+                        {getattr(msg, 'content', str(msg))}
+                    </div>
+                </div>
+                """,
+                unsafe_allow_html=True,
+            )
+    st.markdown('<div style="clear:both"></div>', unsafe_allow_html=True)
+
+st.markdown("---")
+
+user_input = st.chat_input(
+    placeholder="Ask about your LinkedIn profile, e.g., 'Analyze my profile, how do I fit for AI role, how is my about section?'" 
+)
+
+if user_input and user_input.strip():
+    state.setdefault("messages", []).append(HumanMessage(content=user_input.strip()))
+    validate_state(state)
+    thread_id = st.session_state.get("thread_id")
+    config = {"configurable": {"thread_id": thread_id}}
+    with st.spinner("Processing your request..."):
+        st.session_state.state = app_graph.invoke(state, config)
+    st.rerun()