app.py

import os
import gradio as gr
import openai
from llama_index.readers.file import PDFReader
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI

openai.api_key = os.environ.get("OPENAI_API_KEY")

def process_pdf(file, question):
    try:
        reader = PDFReader()
        documents = reader.load_data(file=file.name)

        embed_model = OpenAIEmbedding()
        llm = OpenAI()

        index = VectorStoreIndex.from_documents(documents, embed_model=embed_model)
        query_engine = index.as_query_engine(llm=llm)
        response = query_engine.query(question)

        return str(response)

    except Exception as e:
        return f"❌ Error: {e}"

# Gradio Blocks UI
with gr.Blocks(title="Resume Analyzer by Advaith") as demo:
    gr.Markdown("""
    # 📄 Resume Analyzer
    Upload a resume and ask any question about the candidate!  
    Powered by **LlamaIndex** + **OpenAI**
    """)

    with gr.Row():
        pdf_file = gr.File(label="📁 Upload your resume (PDF)", file_types=[".pdf"])
        question = gr.Textbox(lines=2, label="💬 Ask something", placeholder="e.g., What are the candidate's technical strengths?")

    analyze_button = gr.Button("🔍 Analyze")

    result = gr.Textbox(label="🧠 Answer", lines=10)

    def run_analysis(file, question):
        return process_pdf(file, question)

    analyze_button.click(run_analysis, inputs=[pdf_file, question], outputs=result)

# Launch app
if __name__ == "__main__":
    demo.launch()