Update app.py

app.py

@@ -13,7 +13,7 @@ st.title("Resume Screening Assistance")
 required_skills = ["Python", "Machine Learning", "Cloud (AWS/Azure)", "Data Analysis", "React", "Docker"]
 
 # Job Description Input
-job_description = st.text_area("Paste the job description below:")
+job_description = st.text_area("Paste the job description below:", placeholder="Enter the job description here...")
 
 # Upload Resumes
 uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)
@@ -31,67 +31,80 @@ if st.button("Submit"):
         current_file_placeholder = st.empty()
 
         for idx, file in enumerate(uploaded_files, start=1):
-            # Update the current file being processed in the placeholder
-            current_file_placeholder.text(f"Currently processing: {file.name}")
-
-            # Process the file
-            text = ExtractPDFText(file)
-            ats_score = calculateATSscore(text, job_description)
-            skill_analysis = skill_gap_analysis(text, required_skills)
-            feedback = modelFeedback(ats_score, text)
-            resumes_data.append({
-                "name": file.name,
-                "ATS Score": ats_score,
-                "Skills Present": skill_analysis['present'],
-                "Missing Skills": skill_analysis['missing'],
-                "Feedback": feedback
-            })
-
-            # Update progress bar
-            progress_bar.progress(idx / total_resumes)
+            try:
+                # Update the current file being processed in the placeholder
+                current_file_placeholder.text(f"Currently processing: {file.name}")
+
+                # Extract resume text
+                text = ExtractPDFText(file)
+
+                # Calculate ATS score and skill gap analysis
+                ats_score = calculateATSscore(text, job_description)
+                skill_analysis = skill_gap_analysis(text, required_skills)
+
+                # Generate AI feedback
+                feedback = modelFeedback(ats_score, text, job_description)
+
+                # Append results for each resume
+                resumes_data.append({
+                    "name": file.name,
+                    "ATS Score": ats_score,
+                    "Skills Present": skill_analysis["present"],
+                    "Missing Skills": skill_analysis["missing"],
+                    "Feedback": feedback
+                })
+
+                # Update progress bar
+                progress_bar.progress(idx / total_resumes)
+
+            except Exception as e:
+                st.error(f"Error processing {file.name}: {e}")
 
         # Clear the current file placeholder after processing all files
         current_file_placeholder.empty()
 
-        # Sort resumes by ATS score
-        sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True)
-
-        # Add rankings
-        for rank, resume in enumerate(sorted_resumes, start=1):
-            resume["Rank"] = rank
-
-        # Display results
-        st.subheader("Top Matches:")
-        for resume in sorted_resumes:
-            st.write(f"#### Rank {resume['Rank']}: {resume['name']}")
-            st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%")
-            st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None")
-            st.write("**Feedback:**")
-            st.markdown(f"> {resume['Feedback']}")
-            st.write("---")
-
-        # Skill Gap Analysis Table
-        st.subheader("Skill Gap Analysis")
-        for resume in sorted_resumes:
-            st.write(f"**{resume['name']} (Rank {resume['Rank']})**")
-            skill_data = {
-                "Required Skills": required_skills,
-                "Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills],
-                "Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills]
-            }
-            df = pd.DataFrame(skill_data)
-            st.table(df)
-
-        # Visualize ATS Scores with Rankings
-        st.subheader("ATS Score Distribution")
-        names_with_ranks = [f"Rank {r['Rank']}: {r['name']}" for r in sorted_resumes]
-        scores = [r["ATS Score"]*100 for r in sorted_resumes]
-        plt.figure(figsize=(8, 5))
-        plt.bar(names_with_ranks, scores)
-        plt.title("ATS Scores by Resume (Ranked)")
-        plt.ylabel("ATS Score (%)")
-        plt.xticks(rotation=45, ha="right")
-        st.pyplot(plt)
+        if resumes_data:
+            # Sort resumes by ATS score
+            sorted_resumes = sorted(resumes_data, key=lambda x: x["ATS Score"], reverse=True)
+
+            # Add rankings
+            for rank, resume in enumerate(sorted_resumes, start=1):
+                resume["Rank"] = rank
+
+            # Display results
+            st.subheader("Top Matches:")
+            for resume in sorted_resumes:
+                st.write(f"#### Rank {resume['Rank']}: {resume['name']}")
+                st.write(f"**ATS Score:** {resume['ATS Score']*100:.0f}%")
+                st.write("**Missing Skills:**", ", ".join(resume["Missing Skills"]) if resume["Missing Skills"] else "None")
+                st.write("**Feedback:**")
+                st.markdown(f"> {resume['Feedback']}")
+                st.write("---")
+
+            # Skill Gap Analysis Table
+            st.subheader("Skill Gap Analysis")
+            for resume in sorted_resumes:
+                st.write(f"**{resume['name']} (Rank {resume['Rank']})**")
+                skill_data = {
+                    "Required Skills": required_skills,
+                    "Present in Resume": ["✅" if skill in resume["Skills Present"] else "❌" for skill in required_skills],
+                    "Missing": [skill if skill in resume["Missing Skills"] else "—" for skill in required_skills]
+                }
+                df = pd.DataFrame(skill_data)
+                st.table(df)
+
+            # Visualize ATS Scores with Rankings
+            st.subheader("ATS Score Distribution")
+            names_with_ranks = [f"Rank {r['Rank']}: {r['name']}" for r in sorted_resumes]
+            scores = [r["ATS Score"]*100 for r in sorted_resumes]
+            plt.figure(figsize=(8, 5))
+            plt.bar(names_with_ranks, scores, color='skyblue')
+            plt.title("ATS Scores by Resume (Ranked)")
+            plt.ylabel("ATS Score (%)")
+            plt.xticks(rotation=45, ha="right")
+            st.pyplot(plt)
+        else:
+            st.error("No resumes could be processed. Please check the uploaded files.")
 
     else:
         st.warning("Please upload resumes and provide a job description before submitting.")