import streamlit as st
import os
import io
import pdfplumber
import pandas as pd
from dotenv import load_dotenv
from groq import Groq
import gspread
from oauth2client.service_account import ServiceAccountCredentials
from docx import Document

# Page config
st.set_page_config(page_title="📄 AI ResumeSync Analyzer", layout="centered")

# Load env vars
load_dotenv()
GROQ_API_KEY = os.getenv("GROQ_API_KEY")

if not GROQ_API_KEY:
    st.error("❌ GROQ API Key is missing. Please add it in the .env file or Hugging Face Secrets.")
    st.stop()

# Load Google Sheet
df = pd.DataFrame()
sheet_loaded = False
scope = ["https://spreadsheets.google.com/feeds", "https://www.googleapis.com/auth/drive"]

if os.path.exists("credentials.json"):
    try:
        creds = ServiceAccountCredentials.from_json_keyfile_name("credentials.json", scope)
        client = gspread.authorize(creds)
        sheet = client.open("Job_Listings").sheet1
        data = sheet.get_all_records()
        df = pd.DataFrame(data)
        sheet_loaded = True
    except Exception as e:
        st.warning(f"⚠️ Could not load job listings from Google Sheet: {e}")

# Fallback job listing
if not sheet_loaded:
    df = pd.DataFrame([{
        "Title": "Software Engineer",
        "Skills": "Python, Django, SQL",
        "Experience": "2+ years",
        "Education": "BS in Computer Science"
    }])

# Groq client
groq_client = Groq(api_key=GROQ_API_KEY)

# UI: Title
st.title("📄 AI ResumeSync Analyzer")
st.markdown("Upload your resume below to receive tailored job match analysis, skill suggestions, and a personalized cover letter.")

# File uploader
uploaded_file = st.file_uploader("📤 Upload your resume (PDF, DOCX, or TXT)", type=["pdf", "docx", "txt"])

# Resume text extractor
def extract_resume_text(uploaded_file):
    try:
        filename = uploaded_file.name.lower()
        buffer = io.BytesIO(uploaded_file.getvalue())  # Use getvalue() instead of read()
        buffer.seek(0)

        if filename.endswith(".pdf"):
            text = ""
            with pdfplumber.open(buffer) as pdf:
                for page in pdf.pages:
                    page_text = page.extract_text()
                    if page_text:
                        text += page_text + "\n"
            return text.strip()

        elif filename.endswith(".docx"):
            doc = Document(buffer)
            return "\n".join(para.text for para in doc.paragraphs if para.text.strip())

        elif filename.endswith(".txt"):
            return buffer.getvalue().decode("utf-8", errors="ignore")

        else:
            return None
    except Exception as e:
        st.error(f"❌ Error reading resume file: {e}")
        return None

# Main logic
if uploaded_file:
    with st.spinner("🔍 Analyzing your resume..."):
        resume_text = extract_resume_text(uploaded_file)
        if not resume_text or resume_text.strip() == "":
            st.error("❌ No readable content in the uploaded resume.")
            st.stop()

        # AI Prompt
        prompt = f"""
You are an expert AI Resume Analyzer. Based on the resume and job listings provided, return a detailed, formatted response covering:
1. ✅ Match Score (as percentage)
2. 🎯 Recommended Jobs (top 3 that match well)
3. 📊 Qualification Ranking (1 to 10)
4. 🧩 Skill Gaps & Suggestions
5. 🛠️ Keyword Optimization Suggestions
6. 🎓 Education & Experience Alignment
7. 💰 Estimated Salary Range
8. 📈 Job Market Trends (optional)
9. ✍️ Personalized Cover Letter
Return the output with clear headings and markdown formatting.

Resume:
{resume_text}

Available Jobs:
{df.to_string(index=False)}
"""

        try:
            response = groq_client.chat.completions.create(
                model="llama3-8b-8192",
                messages=[{"role": "user", "content": prompt}]
            )

            message = response.choices[0].message.content.strip()

            if not message:
                st.error("❌ Groq returned an empty response.")
            else:
                st.success("✅ Analysis Complete!")

                # Output full analysis
                st.markdown(message, unsafe_allow_html=True)

                # Job recommendations
                st.markdown("---")
                st.subheader("📌 Job Listing Overview")
                st.dataframe(df)

                # Call-to-action
                st.markdown("### 🔓 Unlock More Features or Take Action")
                col1, col2 = st.columns(2)
                with col1:
                    if st.button("✅ Apply Now"):
                        st.info("🔗 Redirecting to application portal... (hook this into your system)")
                with col2:
                    if st.button("🚀 Upgrade to Pro Templates"):
                        st.warning("Premium templates coming soon! Contact support to activate.")

        except Exception as e:
            st.error(f"❌ Failed to generate analysis: {e}")