Update app.py

app.py

@@ -18,74 +18,78 @@ job_description = st.text_area("Paste the job description below:")
 # Upload Resumes
 uploaded_files = st.file_uploader("Upload your resumes (PDF only):", type="pdf", accept_multiple_files=True)
 
-if uploaded_files and job_description:
-    resumes_data = []
-
-    # Initialize progress bar
-    progress_bar = st.progress(0)
-    total_resumes = len(uploaded_files)
-
-    # Placeholder to show the current processing file
-    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, skill_analysis['missing'])
-        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)
-
-    # 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:**", 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)
+# Add a submit button
+if st.button("Submit"):
+    if uploaded_files and job_description:
+        resumes_data = []
+
+        # Initialize progress bar
+        progress_bar = st.progress(0)
+        total_resumes = len(uploaded_files)
+
+        # Placeholder to show the current processing file
+        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, skill_analysis['missing'])
+            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)
+
+        # 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:**", 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)
+    else:
+        st.warning("Please upload resumes and provide a job description before submitting.")