Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import fitz  # PyMuPDF
-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  ,evaluate_with_qwen
 
 def extract_text_from_pdf(pdf_file):
     document = fitz.open(pdf_file)
@@ -17,9 +17,12 @@ def evaluate_all_models(pdf_file, job_description):
     bloom_result = evaluate_with_bloom(resume_text, job_description)
     jabir_result = evaluate_with_jabir(resume_text, job_description)
     llama_result = evaluate_with_llama(resume_text, 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{llama_result}"
+    qwen_result=evaluate_with_qwen(resume_text, 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{llama_result}\n\nqwen_result:\n{qwen_result}"
 import gradio as gr
-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,evaluate_with_qwen
 
 def evaluate_multiple_resumes(resume_texts, job_description, model):
     results = []
@@ -34,13 +37,16 @@ def evaluate_multiple_resumes(resume_texts, job_description, model):
             result = evaluate_with_jabir(resume_text, job_description)
         elif model == "llama":
             result = evaluate_with_llama(resume_text, job_description)
+        elif model=="qwen":
+            evaluate_with_qwen(resume_text, job_description)
         else:
             gpt_result = evaluate_with_gpt(resume_text, job_description)
             gemma_result = evaluate_with_gemma(resume_text, job_description)
             bloom_result = evaluate_with_bloom(resume_text, job_description)
             jabir_result = evaluate_with_jabir(resume_text, job_description)
             llama_result = evaluate_with_llama(resume_text, job_description)
-            result = 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{llama_result}"
+            qwen_result=evaluate_with_qwen(resume_text, job_description)
+            result = 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{llama_result}\n\nqwen_result:\n{qwen_result}"
         results.append(f"Result for Resume:\n{result}\n\n")
     return "\n".join(results)
 
@@ -49,7 +55,7 @@ iface = gr.Interface(
     inputs=[
         gr.Textbox(lines=20, label="Paste Resumes (separate multiple resumes by two newlines)"),
         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", "qwen","All"], label="Choose Model")
     ],
     outputs="text",
     title="Multiple Resume Evaluator"