Init Commit

app.py

@@ -1,5 +1,4 @@
 from sentence_transformers import SentenceTransformer, util
-from transformers import pipeline
 import gradio as gr
 import nltk
 
@@ -9,61 +8,31 @@ try:
 except Exception as e:
     print(f"Error loading SentenceTransformer model: {e}")
 
-# Load a summarization pipeline
-try:
-    summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
-except Exception as e:
-    print(f"Error loading summarization pipeline: {e}")
-
 # Download NLTK punkt tokenizer if not already installed (run this once)
 nltk.download('punkt')
 
-def summarize_text(text, max_length=100, min_length=25):
-    try:
-        # Summarize the input text
-        summary = summarizer(text, max_length=max_length, min_length=min_length, do_sample=False)
-        return summary[0]["summary_text"]
-    except Exception as e:
-        return f"Error summarizing text: {e}"
-
 def match_cv_to_jobs(cv_text, job_descriptions):
     debug_info = "Debug Info:\n"
     results = []
     
-    # Summarize the CV text
-    try:
-        summarized_cv = summarize_text(cv_text, max_length=150)
-        debug_info += f"Summarized CV Text: {summarized_cv}\n"
-    except Exception as e:
-        debug_info += f"Error summarizing CV text: {e}\n"
-        return [], debug_info
-
-    # Summarize the job description
-    try:
-        summarized_job_desc = summarize_text(job_descriptions, max_length=150)
-        debug_info += f"Summarized Job Description Text: {summarized_job_desc}\n"
-    except Exception as e:
-        debug_info += f"Error summarizing job descriptions: {e}\n"
-        return [], debug_info
-    
-    # Encode the summarized CV text
+    # Encode the CV text directly without summarization
     try:
-        cv_embedding = model.encode(summarized_cv, convert_to_tensor=True)
+        cv_embedding = model.encode(cv_text, convert_to_tensor=True)
         debug_info += f"CV Embedding: {cv_embedding}\n"
     except Exception as e:
         debug_info += f"Error encoding CV text: {e}\n"
         return [], debug_info
     
-    # Split summarized job description into sentences
+    # Split job description into sentences
     try:
-        description_sentences = nltk.tokenize.sent_tokenize(summarized_job_desc)
+        description_sentences = nltk.tokenize.sent_tokenize(job_descriptions)
     except Exception as e:
         debug_info += f"Error tokenizing job description: {e}\n"
         return [], debug_info
     
     for sentence in description_sentences:
         try:
-            # Encode each sentence from the summarized job description
+            # Encode each sentence from the job description
             sentence_embedding = model.encode(sentence, convert_to_tensor=True)
             debug_info += f"\nJob Description Sentence Embedding: {sentence_embedding}\n"
             
@@ -89,7 +58,7 @@ def match_cv_to_jobs(cv_text, job_descriptions):
 
 # Gradio interface
 with gr.Blocks() as demo:
-    gr.Markdown("# CV and Job Description Matcher with Summarization and Sentence Similarity")
+    gr.Markdown("# CV and Job Description Matcher with Sentence Similarity")
     
     # Input fields for CV and job descriptions
     cv_text = gr.Textbox(label="CV Text", placeholder="Enter the CV text here", lines=10)