Update app.py

app.py

@@ -6,8 +6,8 @@ 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_llama
+from models import evaluate_with_gpt,evaluate_with_gemma,evaluate_with_bloom
+# from models import evaluate_with_llama
 
 
 
@@ -22,23 +22,23 @@ def extract_text_from_pdf(pdf_file):
 
 
 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}"
-    return f"\n\nllama Result:\n{llam_result}"
+    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}"
+    # return f"\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),     
-    fn=lambda pdf, jd, model: evaluate_with_llama(pdf, jd),
+    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),     
+    # fn=lambda pdf, jd, model: evaluate_with_llama(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")
-        gr.Radio(choices=["llama"], label="Choose Model")
+        gr.Radio(choices=["GPT-4o", "Gemma", "Bloom", "jabir", "All"], label="Choose Model")
+        # gr.Radio(choices=["llama"], label="Choose Model")
     ],
     outputs="text",
     title="Resume Evaluator"