Update app.py

app.py

@@ -3,7 +3,9 @@ import google.generativeai as genai
 import os
 import PyPDF2 as pdf
 from dotenv import load_dotenv
-import json  # Import json module for safe parsing
+import json
+import re
+import time
 
 load_dotenv()
 genai.configure(api_key=os.getenv('GOOGLE_API_KEY'))
@@ -24,47 +26,95 @@ def input_pdf_text(upload_file):
     return text
 
 
-# Prompt engineering
+def parse_response(response):
+    """Validate and parse JSON response string."""
+    # Ensure response is JSON-like with regex before parsing
+    if re.match(r'^\{.*\}$', response.strip(), re.DOTALL):
+        try:
+            return json.loads(response)
+        except json.JSONDecodeError:
+            return None
+    return None
+
+
+# Enhanced Prompt Engineering
 input_prompt = """
-hey Act like a skilled or very experienced ATS (Application Tracking System) with a deep understanding of tech field, software engineering,
-data science, data analytics, and big data engineering. Your task is to evaluate the resume based on the given job description. You must 
-consider the job market is very competitive and provide the best assistance for improving the resume. Assign the percentage
-Matching based on JD (Job Description) and the missing keywords with high accuracy.
+Act as a highly skilled ATS (Applicant Tracking System) expert with strong knowledge of software engineering, data science, analytics, and big data engineering. 
+Your goal is to analyze the resume content and compare it to the provided job description. Provide the following:
+1. An overall Job Description (JD) Match percentage.
+2. Top missing keywords or phrases relevant to the job description.
+3. A summary of the profile, highlighting strengths and areas for improvement.
+
+Structure the output as a JSON with the following keys:
+- "JD Match": percentage match as a string with "%" sign.
+- "MissingKeywords": a list of missing but important keywords.
+- "ProfileSummary": a summary as a single string.
+
+Example format:
+{{"JD Match":"85%", "MissingKeywords":["data analysis", "team leadership"], "ProfileSummary":"Highly skilled engineer with 10 years of experience in data engineering."}}
 resume: {text}
 description: {jd}
-
-I want the response in a structured JSON format like this:
-{{"JD Match":"%", "MissingKeywords":[], "Profile Summary":""}}
 """
 
 # Streamlit app setup
 st.title('Smart ATS')
-st.text("Improve your Resume ATS")
-jd = st.text_area("Paste the Job Description")
-uploaded_file = st.file_uploader("Upload your Resume", type="pdf", help="Please upload your resume in PDF format")
-submit = st.button("Submit")
+st.write(
+    "Welcome! This tool helps you improve your resume's alignment with the job description, ensuring it is optimized for ATS.")
+
+# Job Description input
+jd = st.text_area("Paste the Job Description:", help="Paste the job description you want your resume to match.")
+
+# Resume upload
+uploaded_file = st.file_uploader("Upload your Resume (PDF format only):", type="pdf",
+                                 help="Ensure your resume is in PDF format for analysis.")
+
+# Display submit button with progress
+submit = st.button("Analyze Resume")
 
 if submit:
-    if uploaded_file is not None:
-        text = input_pdf_text(uploaded_file)
-        response = get_gemeni_response(input_prompt.format(text=text, jd=jd))
+    if uploaded_file is not None and jd:
+        # Show loading spinner while processing
+        with st.spinner("Analyzing your resume, please wait..."):
+            text = input_pdf_text(uploaded_file)
+            response = get_gemeni_response(input_prompt.format(text=text, jd=jd))
 
-        try:
-            # Parse the response using json.loads
-            response_dict = json.loads(response)  # Use json.loads instead of eval
+            # Retry parsing up to 2 times
+            response_dict = None
+            for _ in range(2):
+                response_dict = parse_response(response)
+                if response_dict:
+                    break
+                time.sleep(1)  # Slight delay before retrying
 
-            # Display structured output
-            st.subheader("Job Description Match")
-            st.write(f"{response_dict['JD Match']}")
+            if response_dict:
+                # Display Job Description Match
+                st.subheader("Job Description Match")
+                st.metric(label="Matching Score", value=response_dict.get("JD Match", "N/A"))
 
-            st.subheader("Missing Keywords")
-            st.write(", ".join(response_dict["MissingKeywords"]))
+                # Display Missing Keywords
+                st.subheader("Top Missing Keywords")
+                missing_keywords = response_dict.get("MissingKeywords", [])
+                if missing_keywords:
+                    st.write(", ".join(missing_keywords))
+                else:
+                    st.write("All key terms are covered!")
 
-            st.subheader("Profile Summary")
-            st.text(response_dict["Profile Summary"])
+                # Display Profile Summary
+                st.subheader("Profile Summary")
+                st.text(response_dict.get("ProfileSummary", "No summary available."))
 
-        except json.JSONDecodeError:
-            st.error("Error parsing response. Check the response format.")
-            st.write(response)  # Display raw response for debugging
+                # Generate downloadable report
+                st.download_button(
+                    label="Download ATS Report",
+                    data=json.dumps(response_dict, indent=4),
+                    file_name="ATS_Analysis_Report.json",
+                    mime="application/json"
+                )
+            else:
+                st.error("Error parsing response. Please try again.")
+                st.write(response)  # Display raw response for debugging
+
+    elif not jd:
+        st.warning("Please paste a job description.")
     else:
-        st.warning("Please upload your resume.")
+        st.warning("Please upload your resume in PDF format.")