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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.")
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