Create app.py

app.py

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+import gradio as gr
+import openai
+import fitz  # PyMuPDF
+import torch
+from transformers import pipeline, BloomForCausalLM, BloomTokenizerFast
+from huggingface_hub import login
+import requests
+import os
+
+from models import evaluate_with_gpt,evaluate_with_gemma,evaluate_with_bloom,evaluate_with_jabir,evaluate_with_llama
+
+
+
+def extract_text_from_pdf(pdf_file):
+    document = fitz.open(pdf_file)
+    text = ""
+    for page_num in range(len(document)):
+        page = document.load_page(page_num)
+        text += page.get_text()
+    return text
+
+
+
+def evaluate_all_models(pdf_file, job_description):
+    gpt_result = evaluate_with_gpt(pdf_file, job_description)
+    gemma_result = evaluate_with_gemma(pdf_file, job_description)
+    bloom_result = evaluate_with_bloom(pdf_file, job_description)
+    jabir_result = evaluate_with_jabir(pdf_file, job_description)
+    llama_result=evaluate_with_llama(pdf_file, job_description)
+    return f"GPT-4o Result:\n{gpt_result}\n\nGemma Result:\n{gemma_result}\n\nBloom Result:\n{bloom_result}\n\njabir Result:\n{jabir_result}\n\nllama Result:\n{llam_result}"
+
+iface = gr.Interface(
+    fn=lambda pdf, jd, model: evaluate_with_gpt(pdf, jd) if model == "GPT-4o" else evaluate_with_gemma(pdf, jd) if model == "Gemma" else evaluate_with_bloom(pdf, jd) if model == "Bloom" else evaluate_with_jabir(pdf, jd) if model == "jabir" else evaluate_all_models(pdf, jd) if model == "llama" else evaluate_all_models(pdf, jd),     
+    inputs=[
+        gr.File(label="Upload Resume PDF"),
+        gr.Textbox(lines=10, label="Job Description"),
+        gr.Radio(choices=["GPT-4o", "Gemma", "Bloom", "jabir"," llama", "All"], label="Choose Model")
+    ],
+    outputs="text",
+    title="Resume Evaluator"
+)
+
+iface.launch()