Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
-import requests
 from sentence_transformers import SentenceTransformer, util
 import docx
 import os
 from PyPDF2 import PdfReader
 import re
+import requests
+import pandas as pd
 
 # Load pre-trained model for sentence embedding
 model = SentenceTransformer('paraphrase-MiniLM-L6-v2')
@@ -52,15 +53,24 @@ def read_docx_file(file_path):
         text += para.text
     return text
 
-# Extract leadership experience (looking for keywords like manager, team lead, leadership)
-def extract_leadership_experience(resume_text):
-    leadership_keywords = ['manager', 'management', 'team lead', 'supervised', 'leadership', 'head', 'coordinator']
-    for keyword in leadership_keywords:
-        if keyword.lower() in resume_text.lower():
-            return "Has leadership experience"
-    return "No leadership experience"
-
-# System prompt to extract candidate details using Gemini API
+# System prompt to extract candidate details from the resume
+def system_prompt_to_extract_info(resume_text):
+    prompt = f"""
+    Extract the following information from the resume:
+    1. Candidate's Full Name
+    2. Candidate's Email Address
+    3. Candidate's Contact Number
+    
+    Resume Text: {resume_text}
+    
+    Return the results in the following format:
+    - Name: [Extracted Name]
+    - Email: [Extracted Email]
+    - Contact: [Extracted Contact Number]
+    """
+    return prompt
+
+# Function to extract candidate information from resume text
 def extract_entities_via_gemini(resume_text):
     api_key = get_google_api_key()  # Fetch the API key from environment variables
     endpoint = "https://gemini.googleapis.com/v1/documents:analyzeEntities"  # Placeholder API endpoint (adjust as necessary)
@@ -81,11 +91,11 @@ def extract_entities_via_gemini(resume_text):
     response = requests.post(endpoint, headers=headers, json=document)
 
     if response.status_code != 200:
-        return {"error": "Failed to extract entities from resume"}
+        return {"error": "Failed to extract entities from resume", "status_code": response.status_code, "response": response.json()}
     
     # Process the response from the Gemini API (or similar NLP API)
     entities = response.json().get('entities', [])
-    extracted_info = {}
+    extracted_info = {"name": "Unknown Candidate", "email": "No Email", "contact": "No Contact"}
 
     for entity in entities:
         if entity['type'] == 'PERSON':
@@ -162,13 +172,22 @@ results_output = gr.Dataframe(headers=[
     label="Analysis Results"
 )
 
+# Function to allow CSV download
+def download_results(results):
+    df = pd.DataFrame(results, columns=["Resume File", "Similarity Score (%)", "Eligibility", "Candidate Name", "Leadership Experience", "Email", "Contact"])
+    return df.to_csv(index=False)
+
 # Gradio Interface
 interface = gr.Interface(
     fn=check_similarity,
     inputs=[job_desc_input, resumes_input],
     outputs=[results_output],
     title="HR Assistant - Resume Screening & Leadership Experience",
-    description="Upload job description and resumes to screen candidates for managerial and team leadership roles and extract candidate details."
+    description="Upload job description and resumes to screen candidates for managerial and team leadership roles and extract candidate details.",
+    allow_flagging="never"
 )
 
+# Adding the download button for CSV
+interface.add_component(gr.File, "Download CSV", fn=download_results)
+
 interface.launch()