app.py

import streamlit as st
import os
import pdfplumber  # Proper PDF text extraction
from dotenv import load_dotenv
from groq import Groq

# Load API Key from .env
load_dotenv()
api_key = os.getenv("GROQ_API_KEY")

# Initialize Groq Client
client = Groq(api_key=api_key)

# Chatbot System Prompt
system_prompt = """
You are an AI Resume Analyzer designed to provide feedback on resumes. 
Your goal is to analyze resumes, identify missing keywords, rate resumes, 
and suggest better skills to highlight.

### Response Format:
**📊 Resume Rating:** _(Score out of 10)_
**🔍 Missing Keywords:** _(Comma-separated list)_
**🛠 Suggested Skills:** _(Bullet points)_
**💡 Expert Improvement Tips:** _(Clear and actionable advice)_
"""

# Function to Extract Text from PDF
def extract_text_from_pdf(pdf_file):
    """Extracts text from an uploaded PDF resume using pdfplumber."""
    text = ""
    try:
        with pdfplumber.open(pdf_file) as pdf:
            for page in pdf.pages:
                text += page.extract_text() + "\n" if page.extract_text() else ""
    except Exception as e:
        st.error(f"Error extracting text from PDF: {e}")
    return text.strip() or "No text found in the PDF."

# Streamlit UI
st.title("📄 AI-Powered Resume Analyzer 🤖")
st.write("Upload your resume, and AI will analyze it for improvements!")

# File Uploader for PDF
uploaded_file = st.file_uploader("Upload Your Resume (PDF)", type=["pdf"])

if uploaded_file is not None:
    with st.spinner("Analyzing your resume..."):
        resume_text = extract_text_from_pdf(uploaded_file)
        
        if resume_text and resume_text != "No text found in the PDF.":
            user_message = f"Resume Content:\n{resume_text}\n\nAnalyze the resume based on the given criteria."

            # Send request to Groq API using "llama-3.3-70b-versatile"
            completion = client.chat.completions.create(
                model="llama-3.3-70b-versatile",
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_message},
                ],
                temperature=0.6,
                max_tokens=4096,
                top_p=0.95,
                stream=True,
            )

            # Stream response and format correctly
            st.subheader("💡 Resume Analysis")
            full_response = ""
            for chunk in completion:
                chunk_text = chunk.choices[0].delta.content or ""
                full_response += chunk_text

            # Format Response
            st.markdown("### **📊 Resume Rating**")
            rating_part = full_response.split("**🔍 Missing Keywords:**")[0].replace("**📊 Resume Rating:**", "").strip()
            st.markdown(f"✅ **Rating:** {rating_part}")

            st.markdown("### **🔍 Missing Keywords**")
            missing_keywords_part = full_response.split("**🔍 Missing Keywords:**")[1].split("**🛠 Suggested Skills:**")[0].strip()
            st.markdown(f"🔹 {missing_keywords_part}")

            st.markdown("### **🛠 Suggested Skills**")
            suggested_skills_part = full_response.split("**🛠 Suggested Skills:**")[1].split("**💡 Expert Improvement Tips:**")[0].strip()
            st.markdown(f"🔸 {suggested_skills_part.replace('-', '🔹')}")  # Convert hyphens to bullet points

            st.markdown("### **💡 Expert Improvement Tips**")
            improvement_tips_part = full_response.split("**💡 Expert Improvement Tips:**")[1].strip()
            st.markdown(f"📌 {improvement_tips_part.replace('-', '📌')}")  # Convert hyphens to bullet points

            # Display success message
            st.success("✅ Resume analyzed successfully!")
        else:
            st.error("⚠️ No readable text found in the PDF. Please upload a valid resume.")