Update app.py

app.py

@@ -1,43 +1,47 @@
 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
-
-
+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):
+    """Extracts and returns the text from a 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(resume_text, 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}"
+    return "".join([page.get_text() for page in document])
+
+def evaluate_resume(resume_text, job_description, model):
+    """Evaluates the resume text using the specified model."""
+    if model == "GPT-4o":
+        return evaluate_with_gpt(resume_text, job_description)
+    elif model == "Gemma":
+        return evaluate_with_gemma(resume_text, job_description)
+    elif model == "Bloom":
+        return evaluate_with_bloom(resume_text, job_description)
+    elif model == "jabir":
+        return evaluate_with_jabir(resume_text, job_description)
+    elif model == "llama":
+        return evaluate_with_llama(resume_text, job_description)
+    else:
+        # If "All" is selected, evaluate with all models and return combined results.
+        return evaluate_all_models(resume_text, job_description)
+
+def evaluate_multiple_resumes(resume_files, job_description, model):
+    """Evaluates multiple resumes and returns the results."""
+    results = []
+    for resume_file in resume_files:
+        title = resume_file.name
+        resume_text = extract_text_from_pdf(resume_file)
+        result = evaluate_resume(resume_text, job_description, model)
+        results.append(f"Result for {title}:\n{result}\n\n")
+    return "\n".join(results)
 
 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),     
+    fn=evaluate_multiple_resumes,
     inputs=[
-        gr.Textbox(lines=10,label="Upload Resume PDF"),
+        gr.File(type="file", label="Upload Resumes PDF", file_count="multiple"),
         gr.Textbox(lines=10, label="Job Description"),
-        gr.Radio(choices=["GPT-4o", "Gemma", "Bloom", "jabir"," llama", "All"], label="Choose Model")
+        gr.Radio(choices=["GPT-4o", "Gemma", "Bloom", "jabir", "llama", "All"], label="Choose Model")
     ],
     outputs="text",
-    title="Resume Evaluator"
+    title="Multiple Resume Evaluator"
 )
 
 iface.launch()