Update app.py

app.py

@@ -3,7 +3,7 @@ import re
 from datetime import datetime
 import PyPDF2
 import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForSeq2SeqLM
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
 from sentence_transformers import SentenceTransformer, util
 from groq import Groq
 import gradio as gr
@@ -65,59 +65,6 @@ def extract_experience(text):
     experience_years = [int(year[0]) for year in experience_years]
     return experience_years, job_titles
 
-# --- Sentiment Analysis --- #
-def analyze_sentiment(text):
-    """Analyzes the sentiment of the text."""
-    model_name = "mrm8488/distiluse-base-multilingual-cased-v2-finetuned-stsb_multi_mt-es"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        predicted_sentiment = torch.argmax(outputs.logits).item()
-    return ["Negative", "Neutral", "Positive"][predicted_sentiment]
-
-# --- Semantic Similarity Calculation --- #
-def calculate_semantic_similarity(text1, text2):
-    """Calculates semantic similarity using a sentence transformer model and returns the score as a percentage."""
-    model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
-    embeddings1 = model.encode(text1, convert_to_tensor=True)
-    embeddings2 = model.encode(text2, convert_to_tensor=True)
-    similarity_score = util.pytorch_cos_sim(embeddings1, embeddings2).item()
-    
-    # Convert similarity score to percentage
-    similarity_percentage = similarity_score * 100
-    return similarity_percentage
-
-# --- Communication Generation --- #
-def communication_generator(resume_skills, job_description_skills, skills_similarity, qualifications_similarity, experience_similarity, candidate_experience):
-    """Generates a detailed communication response based on similarity scores and additional criteria."""
-    # Assess candidate fit based on similarity scores
-    fit_status = "strong fit" if skills_similarity >= 80 and qualifications_similarity >= 80 and experience_similarity >= 80 else \
-                 "moderate fit" if skills_similarity >= 50 else "weak fit"
-    
-    # Build a message that includes a recommendation based on various assessments
-    if fit_status == "strong fit":
-        recommendation = "We recommend moving forward with this candidate, as they demonstrate a high level of alignment with the role requirements."
-    elif fit_status == "moderate fit":
-        recommendation = "This candidate shows potential; however, further assessment or interviews are recommended to clarify their fit for the role."
-    else:
-        recommendation = "We advise against moving forward with this candidate, as they do not meet the key technical requirements for the position."
-
-    message = (
-        f"After a detailed analysis of the candidate's resume, we found the following insights:\n\n"
-        f"- **Skills Match**: {skills_similarity:.2f}% (based on required technologies: {', '.join(job_description_skills)})\n"
-        f"- **Experience Match**: {experience_similarity:.2f}% (relevant experience: {candidate_experience} years)\n"
-        f"- **Qualifications Match**: {qualifications_similarity:.2f}%\n\n"
-        f"The overall assessment indicates that the candidate is a {fit_status} for the role. "
-        f"Their skills in {', '.join(resume_skills)} align with the job's requirements of {', '.join(job_description_skills)}. "
-        f"Based on their experience in web application development, particularly with technologies like {', '.join(resume_skills)}, they could contribute effectively to our team.\n\n"
-        f"**Recommendation**: {recommendation}\n"
-    )
-
-    return message
-
 # --- Updated Resume Analysis Function --- #
 def analyze_resume(resume_file, job_description_file):
     if not resume_file or not job_description_file:
@@ -192,6 +139,7 @@ def analyze_resume(resume_file, job_description_file):
     return summary_message, skills_message, qualifications_message, experience_message
 
 
+# --- Gradio Interface --- #
 def run_gradio_interface():
     with gr.Blocks() as demo:
         gr.Markdown("## Resume and Job Description Analyzer")
@@ -223,14 +171,15 @@ def run_gradio_interface():
 
         analyze_button = gr.Button("Analyze")
 
-     def analyze(resume, job_desc):
+        # Button action
+        analyze_button.click(analyze, inputs=[resume_file, job_description_file], outputs=[summary_output, skills_output, qualifications_output, experience_output])
+
+    demo.launch()
+
+def analyze(resume, job_desc):
     # Always ensure the correct number of return values
     summary, skills, qualifications, experience = analyze_resume(resume, job_desc)
     return summary, skills, qualifications, experience
 
-
-    demo.launch()
-
 if __name__ == "__main__":
     run_gradio_interface()
-