Update app.py

app.py

@@ -1,88 +1,109 @@
 import gradio as gr
 import json
-import os
 import PyPDF2
 import docx
 
 
-# -----------------------------
-# Resume Parsing
-# -----------------------------
+# ----------------------------
+# Resume Text Extraction
+# ----------------------------
 
 def extract_text(file):
 
     if file is None:
         return ""
 
-    file_name = file.name
+    filename = file.name
+    text = ""
 
-    if file_name.endswith(".pdf"):
+    if filename.endswith(".pdf"):
         reader = PyPDF2.PdfReader(file)
-        text = ""
         for page in reader.pages:
             text += page.extract_text() or ""
-        return text
 
-    elif file_name.endswith(".docx"):
-        doc = docx.Document(file)
-        text = "\n".join([p.text for p in doc.paragraphs])
-        return text
+    elif filename.endswith(".docx"):
+        document = docx.Document(file)
+        for para in document.paragraphs:
+            text += para.text + "\n"
 
-    elif file_name.endswith(".txt"):
-        return file.read().decode("utf-8")
+    elif filename.endswith(".txt"):
+        text = file.read().decode("utf-8")
 
-    return ""
+    return text
 
 
-# -----------------------------
-# Simple Resume Analyzer
-# -----------------------------
+# ----------------------------
+# Resume Analyzer
+# ----------------------------
 
 def analyze_resume(resume_text):
 
-    if not resume_text or len(resume_text.strip()) == 0:
+    if not resume_text.strip():
         return {
             "score": 0,
-            "message": "No resume text detected"
+            "technical_skills": [],
+            "soft_skills": [],
+            "recommendation": "No text detected in resume"
         }
 
     text = resume_text.lower()
 
-    technical_keywords = [
-        "python","java","c++","machine learning","ai",
-        "data","sql","tensorflow","pytorch","pandas",
-        "numpy","git","linux"
+    tech_keywords = [
+        "python","java","c++","sql","machine learning",
+        "data analysis","tensorflow","pandas","numpy",
+        "git","linux","ai"
     ]
 
     soft_keywords = [
-        "teamwork","communication","leadership",
+        "communication","teamwork","leadership",
         "problem solving","adaptability"
     ]
 
-    tech_found = [k for k in technical_keywords if k in text]
+    tech_found = [k for k in tech_keywords if k in text]
     soft_found = [k for k in soft_keywords if k in text]
 
-    score = min(100, (len(tech_found)*8 + len(soft_found)*5))
+    score = min(100, len(tech_found)*8 + len(soft_found)*5)
 
-    result = {
+    recommendation = (
+        "Add more technical skills and measurable achievements."
+        if score < 50 else
+        "Good resume. Minor improvements recommended."
+    )
+
+    return {
         "score": score,
         "technical_skills": tech_found,
         "soft_skills": soft_found,
-        "recommendation": "Add more quantified achievements and skills." if score < 50 else "Good resume. Minor improvements recommended."
+        "recommendation": recommendation
     }
 
-    return result
 
+# ----------------------------
+# Format analysis for UI
+# ----------------------------
+
+def format_analysis(result):
+
+    return f"""
+## Resume Score: {result['score']}/100
+
+### Technical Skills Found
+{', '.join(result['technical_skills']) if result['technical_skills'] else "None"}
+
+### Soft Skills Found
+{', '.join(result['soft_skills']) if result['soft_skills'] else "None"}
+
+### Recommendation
+{result['recommendation']}
+"""
 
-# -----------------------------
+
+# ----------------------------
 # Export Functions
-# -----------------------------
+# ----------------------------
 
 def export_json(data):
 
-    if data is None:
-        return None
-
     file_path = "analysis.json"
 
     with open(file_path,"w") as f:
@@ -93,9 +114,6 @@ def export_json(data):
 
 def export_text(data):
 
-    if data is None:
-        return None
-
     file_path = "analysis.txt"
 
     with open(file_path,"w") as f:
@@ -104,9 +122,9 @@ def export_text(data):
     return file_path
 
 
-# -----------------------------
+# ----------------------------
 # Processing Pipeline
-# -----------------------------
+# ----------------------------
 
 def process_resume(file):
 
@@ -114,30 +132,32 @@ def process_resume(file):
 
     analysis = analyze_resume(text)
 
-    return text, analysis
+    formatted = format_analysis(analysis)
+
+    return text, formatted, analysis
 
 
-# -----------------------------
+# ----------------------------
 # UI
-# -----------------------------
+# ----------------------------
 
 with gr.Blocks(title="Resume Analyzer") as demo:
 
     gr.Markdown("# AI Resume Analyzer")
-    gr.Markdown("Upload your resume and get an instant score.")
+    gr.Markdown("Upload your resume and get instant feedback.")
 
-    with gr.Row():
-
-        resume_file = gr.File(label="Upload Resume (PDF, DOCX, TXT)")
+    resume_file = gr.File(label="Upload Resume (PDF / DOCX / TXT)")
 
-    load_btn = gr.Button("Analyze Resume")
+    analyze_btn = gr.Button("Analyze Resume")
 
     resume_text = gr.Textbox(
         label="Extracted Resume Text",
         lines=10
     )
 
-    analysis_output = gr.JSON(label="Analysis Result")
+    analysis_output = gr.Markdown(label="Analysis Result")
+
+    analysis_state = gr.State()
 
     with gr.Row():
 
@@ -146,32 +166,32 @@ with gr.Blocks(title="Resume Analyzer") as demo:
 
     download_file = gr.File(label="Download Analysis")
 
-    # -----------------------------
+    # ----------------------------
     # Button Actions
-    # -----------------------------
+    # ----------------------------
 
-    load_btn.click(
+    analyze_btn.click(
         process_resume,
         inputs=resume_file,
-        outputs=[resume_text, analysis_output]
+        outputs=[resume_text, analysis_output, analysis_state]
     )
 
     export_json_btn.click(
         export_json,
-        inputs=analysis_output,
+        inputs=analysis_state,
         outputs=download_file
     )
 
     export_text_btn.click(
         export_text,
-        inputs=analysis_output,
+        inputs=analysis_state,
         outputs=download_file
     )
 
 
-# -----------------------------
+# ----------------------------
 # Launch
-# -----------------------------
+# ----------------------------
 
 if __name__ == "__main__":
     demo.launch()