Update app.py

app.py

@@ -1,7 +1,7 @@
 # app.py
 """
-Quantum Scrutiny Platform | Groq-Powered
-Single-file Streamlit app (refactored, Groq streaming-compatible)
+Quantum Scrutiny Platform — Groq-Powered Resume Analyzer
+Fully updated + cleaned single-file Streamlit application
 """
 
 import os
@@ -12,6 +12,7 @@ import base64
 import traceback
 from typing import Optional, List
 
+# Env
 from dotenv import load_dotenv
 load_dotenv()
 
@@ -19,8 +20,8 @@ import streamlit as st
 import pandas as pd
 
 # File parsing
-import fitz                   # PyMuPDF
-from docx import Document     # python-docx
+import fitz  # PyMuPDF
+from docx import Document
 
 # Groq client
 from groq import Groq
@@ -28,14 +29,22 @@ from groq import Groq
 # Validation
 from pydantic import BaseModel, Field, ValidationError
 
-# --- Page config ---
-st.set_page_config(layout="wide", page_title="Quantum Scrutiny Platform | Groq-Powered")
 
-# --- Config / Secrets ---
+# ---------------------------------------------------------
+# Page config
+# ---------------------------------------------------------
+st.set_page_config(
+    page_title="Quantum Scrutiny Platform",
+    layout="wide"
+)
+
+
+# ---------------------------------------------------------
+# Secrets
+# ---------------------------------------------------------
 GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 ADMIN_PASSWORD = os.getenv("ADMIN_PASSWORD", "admin")
 
-# Initialize Groq client (no API key -> UI warning but app still loads)
 groq_client = None
 if GROQ_API_KEY:
     try:
@@ -43,25 +52,34 @@ if GROQ_API_KEY:
     except Exception as e:
         st.error(f"Failed to initialize Groq client: {e}")
 else:
-    st.warning("GROQ_API_KEY not found. Set it as an environment variable or in .env for model calls to work.")
+    st.warning("GROQ_API_KEY not found — model calls disabled.")
+
 
-# --- Session state defaults ---
+# ---------------------------------------------------------
+# Session State
+# ---------------------------------------------------------
 if 'is_admin_logged_in' not in st.session_state:
     st.session_state.is_admin_logged_in = False
+
+if 'run_analysis' not in st.session_state:
+    st.session_state.run_analysis = False
+
+if 'individual_analysis' not in st.session_state:
+    st.session_state.individual_analysis = []
+
 if 'analyzed_data' not in st.session_state:
-    initial_cols = [
+    cols = [
         'Name', 'Job Role', 'Resume Score (100)', 'Email', 'Phone', 'Shortlisted',
         'Experience Summary', 'Education Summary', 'Communication Rating (1-10)',
         'Skills/Technologies', 'Certifications', 'ABA Skills (1-10)',
         'RBT/BCBA Cert', 'Autism-Care Exp (1-10)'
     ]
-    st.session_state.analyzed_data = pd.DataFrame(columns=initial_cols)
-if 'individual_analysis' not in st.session_state:
-    st.session_state.individual_analysis = []
-if 'run_analysis' not in st.session_state:
-    st.session_state.run_analysis = False
+    st.session_state.analyzed_data = pd.DataFrame(columns=cols)
+
 
-# --- Pydantic schema ---
+# ---------------------------------------------------------
+# Pydantic Schema
+# ---------------------------------------------------------
 class ResumeAnalysis(BaseModel):
     name: str = Field(default="Unknown")
     email: str = Field(default="")
@@ -76,446 +94,300 @@ class ResumeAnalysis(BaseModel):
     autism_care_experience_score: Optional[str] = Field(default="N/A")
 
 
-# --- Helpers: file text extraction ---
+# ---------------------------------------------------------
+# Text Extraction
+# ---------------------------------------------------------
 def extract_text_from_file(uploaded_file) -> str:
-    """Extract text from PDF or DOCX. Returns empty string on failure."""
     try:
         content = uploaded_file.read()
-        filename = uploaded_file.name.lower()
-        if filename.endswith(".pdf") or content[:5] == b"%PDF-":
+        name = uploaded_file.name.lower()
+
+        # PDF
+        if name.endswith(".pdf") or content[:5] == b"%PDF-":
             try:
                 with fitz.open(stream=content, filetype="pdf") as doc:
-                    text = ""
-                    for p in doc:
-                        text += p.get_text()
-                return text.strip()
-            except Exception:
+                    return "".join([p.get_text() for p in doc]).strip()
+            except:
                 return ""
-        elif filename.endswith(".docx"):
+
+        # DOCX
+        elif name.endswith(".docx"):
             try:
                 doc = Document(io.BytesIO(content))
-                paragraphs = [p.text for p in doc.paragraphs if p.text and p.text.strip()]
-                return "\n".join(paragraphs).strip()
-            except Exception:
+                return "\n".join([p.text for p in doc.paragraphs]).strip()
+            except:
                 return ""
-        else:
-            # fallback: decode bytes as text
-            try:
-                return content.decode('utf-8', errors='ignore')
-            except Exception:
-                return ""
-    except Exception:
+
+        # Fallback
+        return content.decode("utf-8", errors="ignore")
+
+    except:
         return ""
 
 
-# --- Groq call with streaming (collects chunks) ---
-def call_groq_stream_collect(prompt: str, model_name: str = "llama-3.3-70b-versatile", temperature: float = 0.2, max_completion_tokens: int = 2048, top_p: float = 1.0) -> Optional[str]:
-    """
-    Calls Groq with streaming enabled and collects the textual output.
-    Returns the full model text, or None on failure.
-    """
+# ---------------------------------------------------------
+# Groq Streaming Wrapper
+# ---------------------------------------------------------
+def call_groq_stream_collect(prompt: str) -> Optional[str]:
+
     if not groq_client:
-        st.error("Groq client not initialized. Set GROQ_API_KEY in environment/secrets.")
+        st.error("Groq client not initialized.")
         return None
 
     try:
         completion = groq_client.chat.completions.create(
-            model=model_name,
+            model="llama-3.3-70b-versatile",
             messages=[
-                {"role": "system", "content": "You are a professional Resume Analyzer. Return JSON only when asked."},
+                {"role": "system", "content": "You are an AI resume analyzer."},
                 {"role": "user", "content": prompt}
             ],
-            temperature=temperature,
-            max_completion_tokens=max_completion_tokens,
-            top_p=top_p,
-            stream=True
+            stream=True,
+            temperature=0.0,
+            max_completion_tokens=2048
         )
 
-        # completion is an iterator/streamable object; collect chunks
         collected = ""
-        # some SDKs yield dict-like chunks, some objects; handle both
         for chunk in completion:
             try:
-                # Common pattern: chunk.choices[0].delta.content
-                delta = getattr(chunk.choices[0].delta, "content", None) if hasattr(chunk, "choices") else None
-                if delta is None:
-                    # fallback for dict-like object
-                    if isinstance(chunk, dict):
-                        delta = chunk.get("choices", [{}])[0].get("delta", {}).get("content")
+                delta = getattr(chunk.choices[0].delta, "content", None)
                 if delta:
                     collected += delta
-                else:
-                    # Some SDKs return final message in chunk.choices[0].message.content
-                    try:
-                        msg = getattr(chunk.choices[0].message, "content", None)
-                        if msg:
-                            collected += msg
-                    except Exception:
-                        pass
-            except Exception:
-                # last-resort: append str(chunk)
-                try:
-                    collected += str(chunk)
-                except Exception:
-                    pass
-
-        return collected.strip()
+            except:
+                pass
+        return collected
+
     except Exception as e:
-        st.error(f"Groq API call failed: {e}")
+        st.error(f"Groq API error: {e}")
         return None
 
 
-# --- Parsing model output safely to JSON ---
-def extract_first_json(text: str) -> Optional[dict]:
-    """
-    Find the first JSON object in text and parse it; return dict or None.
-    """
+# ---------------------------------------------------------
+# JSON Extraction
+# ---------------------------------------------------------
+def extract_first_json(text: str):
     if not text:
         return None
-    # find first balanced braces block
-    # quick heuristic regex for {...}
-    try:
-        match = re.search(r"(\{(?:[^{}]|(?R))*\})", text, re.DOTALL)
-    except re.error:
-        # Python's re doesn't support (?R); fallback to simpler greedy
-        match = re.search(r"(\{.*\})", text, re.DOTALL)
-    if match:
-        json_text = match.group(1)
-    else:
-        # maybe the model returned only JSON-like lines -> try to parse full text
-        json_text = text
 
+    # Try simple balanced regex
+    match = re.search(r"\{[\s\S]*\}", text)
+    if not match:
+        return None
+
+    raw_json = match.group(0)
+
+    # Attempt parse
     try:
-        parsed = json.loads(json_text)
-        return parsed
-    except Exception:
-        # try to clean common issues: single quotes -> double quotes
+        return json.loads(raw_json)
+    except:
         try:
-            json_text_fixed = json_text.replace("'", '"')
-            parsed = json.loads(json_text_fixed)
-            return parsed
-        except Exception:
+            return json.loads(raw_json.replace("'", '"'))
+        except:
             return None
 
 
-# --- Analyze with Groq (cached by resume text + role) ---
+# ---------------------------------------------------------
+# Cached Analysis
+# ---------------------------------------------------------
 @st.cache_data(show_spinner=False)
 def analyze_resume_with_groq_cached(resume_text: str, job_role: str) -> ResumeAnalysis:
-    """
-    Calls Groq (streaming) and returns a ResumeAnalysis instance.
-    Uses caching to avoid duplicate calls for same resume_text+role.
-    """
-    # Build prompt instructing JSON structure
-    therapist_instructions = ""
-    if job_role.lower() == "therapist":
-        therapist_instructions = (
-            "Because the role is 'Therapist', carefully search for ABA Therapy Skills, "
-            "RBT/BCBA Certification, and Autism-Care Experience. Provide scores 1-10 as STRINGS, or 'N/A'."
-        )
-    else:
-        therapist_instructions = "If therapist-specific fields are not relevant, set them to 'N/A'."
-
-    system_user_prompt = (
-        "Return a single JSON object with the following keys exactly: "
-        "name (string), email (string), phone (string), certifications (array of strings), "
-        "experience_summary (string), education_summary (string), communication_skills (STRING, e.g., '8'), "
-        "technical_skills (array of strings), aba_therapy_skills (STRING or 'N/A'), "
-        "rbt_bcba_certification (STRING 'Yes'/'No'/'N/A'), autism_care_experience_score (STRING or 'N/A'). "
-        f"{therapist_instructions}\n\nResume Text:\n\n{resume_text}\n\nReturn only the JSON object."
+
+    therapist_instruction = (
+        "If role is Therapist, extract ABA skills, BCBA/RBT, and Autism-care scores."
+        if job_role.lower() == "therapist" else
+        "For non-therapist roles, set therapist fields to 'N/A'."
     )
 
-    raw = call_groq_stream_collect(system_user_prompt, model_name="llama-3.3-70b-versatile", temperature=0.0, max_completion_tokens=2048)
-
-    if not raw:
-        # fallback empty object
-        return ResumeAnalysis(
-            name="Extraction Failed",
-            email="",
-            phone="",
-            certifications=[],
-            experience_summary="",
-            education_summary="",
-            communication_skills="N/A",
-            technical_skills=[],
-            aba_therapy_skills="N/A",
-            rbt_bcba_certification="N/A",
-            autism_care_experience_score="N/A"
-        )
+    prompt = f"""
+Return a JSON object with keys:
+name, email, phone, certifications, experience_summary,
+education_summary, communication_skills, technical_skills,
+aba_therapy_skills, rbt_bcba_certification, autism_care_experience_score.
 
+{therapist_instruction}
+
+Resume Text:
+{resume_text}
+
+Return only JSON.
+"""
+
+    raw = call_groq_stream_collect(prompt)
     parsed = extract_first_json(raw)
+
     if not parsed:
-        # show raw output for debugging when developer runs app locally (admin panel will show too)
-        st.warning("Failed to parse model JSON output. See raw output below for debugging.")
-        st.text_area("Raw model output (debug)", raw, height=200)
-        return ResumeAnalysis(
-            name="Extraction Failed",
-            email="",
-            phone="",
-            certifications=[],
-            experience_summary="",
-            education_summary="",
-            communication_skills="N/A",
-            technical_skills=[],
-            aba_therapy_skills="N/A",
-            rbt_bcba_certification="N/A",
-            autism_care_experience_score="N/A"
-        )
+        return ResumeAnalysis(name="Extraction Failed")
 
-    # Ensure keys exist and coerce types
-    parsed.setdefault("name", "Unknown")
-    parsed.setdefault("email", "")
-    parsed.setdefault("phone", "")
-    parsed.setdefault("certifications", [])
-    parsed.setdefault("experience_summary", "")
-    parsed.setdefault("education_summary", "")
-    parsed.setdefault("communication_skills", "N/A")
-    parsed.setdefault("technical_skills", [])
-    parsed.setdefault("aba_therapy_skills", "N/A")
-    parsed.setdefault("rbt_bcba_certification", "N/A")
-    parsed.setdefault("autism_care_experience_score", "N/A")
-
-    # Ensure string coercions for some fields
     try:
-        parsed["communication_skills"] = str(parsed.get("communication_skills") or "N/A")
-        parsed["aba_therapy_skills"] = str(parsed.get("aba_therapy_skills") or "N/A")
-        parsed["rbt_bcba_certification"] = str(parsed.get("rbt_bcba_certification") or "N/A")
-        parsed["autism_care_experience_score"] = str(parsed.get("autism_care_experience_score") or "N/A")
-    except Exception:
-        pass
-
-    # Validate via Pydantic
-    try:
-        analysis = ResumeAnalysis.parse_obj(parsed)
-        return analysis
-    except ValidationError as ve:
-        st.error("Model output failed schema validation.")
-        st.text_area("Raw model output (debug)", raw, height=200)
-        st.exception(ve)
-        return ResumeAnalysis(
-            name="Extraction Failed",
-            email="",
-            phone="",
-            certifications=[],
-            experience_summary="",
-            education_summary="",
-            communication_skills="N/A",
-            technical_skills=[],
-            aba_therapy_skills="N/A",
-            rbt_bcba_certification="N/A",
-            autism_care_experience_score="N/A"
-        )
+        return ResumeAnalysis.parse_obj(parsed)
+    except:
+        return ResumeAnalysis(name="Extraction Failed")
 
 
-# --- Scoring logic ---
+# ---------------------------------------------------------
+# Scoring
+# ---------------------------------------------------------
 def calculate_resume_score(analysis: ResumeAnalysis, role: str) -> float:
-    total_score = 0.0
+    score = 0
 
-    # Experience summary: up to 40
-    exp_len = len(analysis.experience_summary or "")
-    exp_factor = min(exp_len / 100.0, 1.0)
-    total_score += exp_factor * 40.0
+    # Experience length (40)
+    score += min(len(analysis.experience_summary) / 100, 1) * 40
 
-    # Skills count: up to 30
-    skills_count = len(analysis.technical_skills or [])
-    skills_factor = min(skills_count / 10.0, 1.0)
-    total_score += skills_factor * 30.0
+    # Skills count (30)
+    score += min(len(analysis.technical_skills) / 10, 1) * 30
 
-    # Communication: up to 20 (expects 0-10 in string)
+    # Communication (20)
     try:
-        m = re.search(r"(\d+(\.\d+)?)", str(analysis.communication_skills))
-        comm_val = float(m.group(1)) if m else float(str(analysis.communication_skills))
-        comm_val = max(0.0, min(10.0, comm_val))
-    except Exception:
-        comm_val = 5.0
-    total_score += (comm_val / 10.0) * 20.0
+        c = float(re.findall(r"\d+", analysis.communication_skills)[0])
+    except:
+        c = 5
+    score += (min(c, 10) / 10) * 20
 
-    # Certifications: up to 10
-    total_score += min(len(analysis.certifications or []), 10) * 1.0
+    # Certifications (10)
+    score += min(len(analysis.certifications), 10)
 
-    # Therapist bonus up to 10
+    # Therapist bonus (10)
     if role.lower() == "therapist":
-        def safe_score(x):
-            try:
-                m = re.search(r"(\d+(\.\d+)?)", str(x))
-                return float(m.group(1)) if m else 0.0
-            except Exception:
-                return 0.0
-        aba = safe_score(analysis.aba_therapy_skills)
-        autism = safe_score(analysis.autism_care_experience_score)
-        spec_bonus = ((aba + autism) / 20.0) * 10.0
-        total_score += spec_bonus
-
-    final = round(min(total_score, 100))
-    return float(final)
-
-
-# --- Append to DataFrame ---
-def append_analysis_to_dataframe(job_role: str, analysis: ResumeAnalysis, score: float):
-    data = analysis.dict()
-    tech = ", ".join(data.get("technical_skills") or [])
-    certs = ", ".join(data.get("certifications") or [])
-    row = {
-        'Name': data.get("name") or "",
-        'Job Role': job_role,
-        'Resume Score (100)': score,
-        'Email': data.get("email") or "",
-        'Phone': data.get("phone") or "",
-        'Shortlisted': 'No',
-        'Experience Summary': data.get("experience_summary") or "",
-        'Education Summary': data.get("education_summary") or "",
-        'Communication Rating (1-10)': str(data.get("communication_skills") or "N/A"),
-        'Skills/Technologies': tech,
-        'Certifications': certs,
-        'ABA Skills (1-10)': str(data.get("aba_therapy_skills") or "N/A"),
-        'RBT/BCBA Cert': str(data.get("rbt_bcba_certification") or "N/A"),
-        'Autism-Care Exp (1-10)': str(data.get("autism_care_experience_score") or "N/A"),
-    }
-    new_df = pd.DataFrame([row])
-    st.session_state.analyzed_data = pd.concat([st.session_state.analyzed_data, new_df], ignore_index=True)
-
-
-# --- Excel export helper ---
-def df_to_excel_bytes(df: pd.DataFrame) -> bytes:
+        try:
+            aba = float(re.findall(r"\d+", analysis.aba_therapy_skills)[0])
+            autism = float(re.findall(r"\d+", analysis.autism_care_experience_score)[0])
+            score += ((aba + autism) / 20) * 10
+        except:
+            pass
+
+    return float(round(min(score, 100)))
+
+
+# ---------------------------------------------------------
+# Add Row
+# ---------------------------------------------------------
+def append_analysis_to_dataframe(role, analysis: ResumeAnalysis, score: float):
+
+    df = st.session_state.analyzed_data
+
+    df.loc[len(df)] = [
+        analysis.name,
+        role,
+        score,
+        analysis.email,
+        analysis.phone,
+        "No",
+        analysis.experience_summary,
+        analysis.education_summary,
+        analysis.communication_skills,
+        ", ".join(analysis.technical_skills),
+        ", ".join(analysis.certifications),
+        analysis.aba_therapy_skills,
+        analysis.rbt_bcba_certification,
+        analysis.autism_care_experience_score
+    ]
+
+    st.session_state.analyzed_data = df
+
+
+# ---------------------------------------------------------
+# Excel Export
+# ---------------------------------------------------------
+def df_to_excel_bytes(df):
     output = io.BytesIO()
-    with pd.ExcelWriter(output, engine="openpyxl") as writer:
-        df.to_excel(writer, index=False, sheet_name="Resume Analysis Data")
+    with pd.ExcelWriter(output, engine="openpyxl") as w:
+        df.to_excel(w, index=False, sheet_name="Resume Analysis")
     return output.getvalue()
 
 
-# --- UI Layout ---
-st.title("🌌 Quantum Scrutiny Platform: AI Resume Analysis (Single-file)")
+# ---------------------------------------------------------
+# UI
+# ---------------------------------------------------------
+st.title("🌌 Quantum Scrutiny Platform — AI Resume Analyzer")
 
-tab_user, tab_admin = st.tabs(["👤 Resume Uploader (User Panel)", "🔒 Admin Dashboard (Password Protected)"])
+tab_user, tab_admin = st.tabs([
+    "👤 User Resume Panel",
+    "🔒 Admin Dashboard"
+])
 
-# --- User Panel ---
+# ---------------------------------------------------------
+# USER PANEL
+# ---------------------------------------------------------
 with tab_user:
-    st.header("Upload Resumes for Analysis")
-    st.info("Upload multiple PDF or DOCX files. The Groq AI engine will extract and score fields.")
 
-    job_role_options = ["Software Engineer", "ML Engineer", "Therapist", "Data Analyst", "Project Manager"]
-    selected_role = st.selectbox("1. Select the Target Job Role", options=job_role_options, key="selected_role")
+    st.header("Upload Resumes")
+
+    job_role = st.selectbox(
+        "Select Job Role",
+        ["Software Engineer", "ML Engineer", "Therapist", "Data Analyst", "Project Manager"]
+    )
 
-    uploaded_files = st.file_uploader("2. Upload Resumes (PDF or DOCX)", type=["pdf", "docx"], accept_multiple_files=True)
+    files = st.file_uploader(
+        "Upload PDF or DOCX",
+        type=["pdf", "docx"],
+        accept_multiple_files=True
+    )
 
-    if st.button("🚀 Analyze All Uploaded Resumes"):
-        if not uploaded_files:
-            st.warning("Please upload one or more resume files to begin analysis.")
+    if st.button("🚀 Analyze All"):
+        if not files:
+            st.warning("Upload at least one file.")
         else:
             st.session_state.run_analysis = True
             st.rerun()
 
-    if st.session_state.get("run_analysis", False):
-        if not uploaded_files:
-            st.warning("No files found. Upload files and try again.")
+    if st.session_state.run_analysis:
+
+        if not files:
+            st.error("No files found.")
             st.session_state.run_analysis = False
+
         else:
-            total = len(uploaded_files)
+            total = len(files)
             progress = st.progress(0)
-            st.session_state.individual_analysis = []
-            idx = 0
-            with st.spinner("Processing resumes..."):
-                for f in uploaded_files:
-                    idx += 1
-                    try:
-                        st.write(f"Analyzing **{f.name}**...")
-                        resume_text = extract_text_from_file(f)
-                        if not resume_text:
-                            st.error(f"Could not extract text from {f.name}. Skipping.")
-                            progress.progress(idx / total)
-                            continue
-
-                        analysis = analyze_resume_with_groq_cached(resume_text, selected_role)
-
-                        if analysis.name == "Extraction Failed":
-                            st.error(f"Extraction failed for {f.name}. See debug output.")
-                            progress.progress(idx / total)
-                            continue
-
-                        score = calculate_resume_score(analysis, selected_role)
-                        append_analysis_to_dataframe(selected_role, analysis, score)
-
-                        st.session_state.individual_analysis.append({
-                            'name': analysis.name,
-                            'score': score,
-                            'role': selected_role,
-                            'file_name': f.name
-                        })
-                    except Exception as e:
-                        st.error(f"Error analyzing {f.name}: {e}")
-                        st.exception(traceback.format_exc())
-                    finally:
-                        progress.progress(idx / total)
-
-            st.success(f"✅ Successfully processed {len(st.session_state.individual_analysis)} of {total} resumes.")
+
+            for i, f in enumerate(files, 1):
+                st.write(f"Analyzing **{f.name}**...")
+                text = extract_text_from_file(f)
+
+                if not text:
+                    st.error(f"Could not extract text from {f.name}. Skipped.")
+                    progress.progress(i / total)
+                    continue
+
+                analysis = analyze_resume_with_groq_cached(text, job_role)
+                score = calculate_resume_score(analysis, job_role)
+
+                append_analysis_to_dataframe(job_role, analysis, score)
+                progress.progress(i / total)
+
+            st.success("All files processed!")
             st.session_state.run_analysis = False
 
-    # Display last results summary
-    if st.session_state.individual_analysis:
-        st.subheader("Last Analysis Summary")
-        for item in st.session_state.individual_analysis:
-            st.markdown(f"**{item['name']}** (for **{item['role']}**) - **Score: {item['score']}/100**")
-        st.markdown("---")
-        st.caption("All analyzed data is stored in the Admin Dashboard.")
 
-# --- Admin Panel ---
+# ---------------------------------------------------------
+# ADMIN PANEL
+# ---------------------------------------------------------
 with tab_admin:
+
     if not st.session_state.is_admin_logged_in:
-        st.header("Admin Login")
-        password = st.text_input("Enter Admin Password", type="password")
-        if st.button("🔑 Login"):
-            if password == ADMIN_PASSWORD:
+
+        pwd = st.text_input("Admin Password", type="password")
+        if st.button("Login"):
+            if pwd == ADMIN_PASSWORD:
                 st.session_state.is_admin_logged_in = True
                 st.rerun()
             else:
                 st.error("Incorrect password.")
-        st.stop()
 
-    st.header("🎯 Recruitment Dashboard")
-    if st.button("🚪 Logout"):
-        st.session_state.is_admin_logged_in = False
-        st.rerun()
-
-    if st.session_state.analyzed_data.empty:
-        st.warning("No resume data has been analyzed yet. Please upload files in the User Panel.")
     else:
-        df = st.session_state.analyzed_data.copy()
-        st.subheader("Candidate Data Table")
-        st.success(f"**Total Candidates Analyzed: {len(df)}**")
-
-        display_cols = ['Name', 'Job Role', 'Resume Score (100)', 'Shortlisted', 'Email', 'Skills/Technologies']
-
-        edited_df = st.data_editor(
-            df[display_cols],
-            column_config={
-                "Shortlisted": st.column_config.SelectboxColumn(
-                    "Shortlisted",
-                    help="Mark the candidate as Shortlisted or Rejected.",
-                    options=["No", "Yes"],
-                    required=True
-                )
-            },
-            key="dashboard_editor",
-            hide_index=True
-        )
-
-        try:
-            st.session_state.analyzed_data.loc[:, 'Shortlisted'] = edited_df['Shortlisted'].values
-        except Exception:
-            for i, val in enumerate(edited_df['Shortlisted'].tolist()):
-                if i < len(st.session_state.analyzed_data):
-                    st.session_state.analyzed_data.at[i, 'Shortlisted'] = val
-
-        st.markdown("---")
-        st.subheader("📥 Download Data")
-        df_export = st.session_state.analyzed_data.copy()
-        excel_bytes = df_to_excel_bytes(df_export)
-
-        st.download_button(
-            label="💾 Download All Data as Excel (.xlsx)",
-            data=excel_bytes,
-            file_name="quantum_scrutiny_report.xlsx",
-            mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet",
-            help="Downloads the full table including all extracted fields and shortlist status."
-        )
-
-# --- End of file ---
+        st.subheader("Admin Dashboard — Analyzed Data")
+
+        df = st.session_state.analyzed_data
+        st.dataframe(df, use_container_width=True)
+
+        if st.button("Download Excel"):
+            xls = df_to_excel_bytes(df)
+            st.download_button(
+                label="Download File",
+                data=xls,
+                file_name="resume_analysis.xlsx",
+                mime="application/vnd.openxmlformats-officedocument.spreadsheetml.sheet"
+            )
+
+        if st.button("Clear Database"):
+            st.session_state.analyzed_data = st.session_state.analyzed_data.iloc[0:0]
+            st.success("Cleared.")