app.py

import gradio as gr
import requests
import matplotlib.pyplot as plt
import io
import os

# Load Gemini API key securely from Hugging Face secrets
API_KEY = os.getenv("GEMINI_API_KEY")
API_URL = "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent"

def analyze_resume(resume_text, job_role):
    # Check API key
    if not API_KEY:
        return "❌ No API key found. Please add GEMINI_API_KEY in Hugging Face secrets.", None

    # Validate inputs
    if not resume_text.strip():
        return "❌ Please paste a resume first.", None
    if not job_role.strip():
        return "❌ Please enter a target job role.", None

    # Prompt for Gemini
    prompt = f"""
    You are an AI Resume Analyzer.
    Analyze the following resume for the job role: {job_role}.
    Resume:
    {resume_text}
    Provide output in this format clearly:
    1. Strengths (bullet points)
    2. Weaknesses (bullet points)
    3. Suggested Improvements (bullet points)
    4. Job Match Score (0-100)
    """

    headers = {
        "Content-Type": "application/json",
        "X-goog-api-key": API_KEY,
    }

    data = {
        "contents": [
            {"parts": [{"text": prompt}]}
        ]
    }

    # Call Gemini API
    response = requests.post(API_URL, headers=headers, json=data)

    if response.status_code != 200:
        return "❌ API Error: " + response.text, None

    result = response.json()
    ai_text = result["candidates"][0]["content"]["parts"][0]["text"]

    # Extract score if possible
    score = 0
    for line in ai_text.splitlines():
        if "Score" in line or "score" in line:
            try:
                score = int("".join([c for c in line if c.isdigit()]))
                if score > 100:
                    score = 100
                break
            except:
                score = 0

    # Make score chart
    fig, ax = plt.subplots(figsize=(4, 4))
    ax.bar(["Job Match"], [score], color="deepskyblue")
    ax.set_ylim(0, 100)
    ax.set_ylabel("Score")
    ax.set_title(f"Resume Match for {job_role}")

    buf = io.BytesIO()
    plt.savefig(buf, format="png", bbox_inches="tight")
    buf.seek(0)

    return ai_text, buf

# Gradio UI
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown("<h1 style='text-align: center;'>📑 AI Resume Analyzer 🎯</h1>")
    gr.Markdown("### Upload or paste your resume + enter job role → Get instant AI feedback & score!")

    with gr.Row():
        resume_input = gr.Textbox(
            label="Paste Resume Text",
            lines=18,
            placeholder="Paste your resume here..."
        )
        job_input = gr.Textbox(
            label="Target Job Role",
            placeholder="e.g. Data Scientist"
        )

    analyze_btn = gr.Button("🔍 Analyze Resume", variant="primary")

    with gr.Row():
        output_text = gr.Textbox(label="AI Feedback", lines=18)
        output_plot = gr.Image(type="pil", label="Job Match Score")

    analyze_btn.click(analyze_resume, inputs=[resume_input, job_input], outputs=[output_text, output_plot])

demo.launch()