Update app.py

app.py

@@ -1,100 +1,177 @@
 import gradio as gr
-import pdfplumber
+import json
+import os
+import PyPDF2
 import docx
 
-# -------- Resume Text Extraction -------- #
+
+# -----------------------------
+# Resume Parsing
+# -----------------------------
 
 def extract_text(file):
+
     if file is None:
-        return "Please upload a resume."
+        return ""
 
     file_name = file.name
 
-    try:
-        # PDF
-        if file_name.endswith(".pdf"):
-            text = ""
-            with pdfplumber.open(file) as pdf:
-                for page in pdf.pages:
-                    page_text = page.extract_text()
-                    if page_text:
-                        text += page_text
+    if file_name.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 file_name.endswith(".txt"):
+        return file.read().decode("utf-8")
 
-        # DOCX
-        elif file_name.endswith(".docx"):
-            doc = docx.Document(file)
-            text = "\n".join([p.text for p in doc.paragraphs])
+    return ""
 
-        else:
-            return "Unsupported file format. Upload PDF or DOCX."
 
-        return analyze_resume(text)
+# -----------------------------
+# Simple Resume Analyzer
+# -----------------------------
 
-    except Exception as e:
-        return f"Error reading file: {str(e)}"
+def analyze_resume(resume_text):
 
+    if not resume_text or len(resume_text.strip()) == 0:
+        return {
+            "score": 0,
+            "message": "No resume text detected"
+        }
 
-# -------- Resume Analyzer -------- #
+    text = resume_text.lower()
 
-def analyze_resume(text):
+    technical_keywords = [
+        "python","java","c++","machine learning","ai",
+        "data","sql","tensorflow","pytorch","pandas",
+        "numpy","git","linux"
+    ]
 
-    skills = [
-        "python","machine learning","deep learning","data science",
-        "sql","pandas","numpy","tensorflow","pytorch",
-        "nlp","git","docker","flask","fastapi"
+    soft_keywords = [
+        "teamwork","communication","leadership",
+        "problem solving","adaptability"
     ]
 
-    found_skills = []
+    tech_found = [k for k in technical_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))
 
-    for skill in skills:
-        if skill.lower() in text.lower():
-            found_skills.append(skill)
+    result = {
+        "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."
+    }
 
-    score = min(len(found_skills) * 10, 100)
+    return result
 
-    result = f"""
-Resume Score: {score}/100
 
-Detected Skills:
-{', '.join(found_skills) if found_skills else 'No major skills detected'}
+# -----------------------------
+# Export Functions
+# -----------------------------
 
-Suggestions:
-• Add more technical skills
-• Include projects
-• Mention measurable achievements
-• Use clear headings
-"""
+def export_json(data):
 
-    return result
+    if data is None:
+        return None
+
+    file_path = "analysis.json"
+
+    with open(file_path,"w") as f:
+        json.dump(data,f,indent=4)
+
+    return file_path
+
+
+def export_text(data):
+
+    if data is None:
+        return None
+
+    file_path = "analysis.txt"
+
+    with open(file_path,"w") as f:
+        f.write(str(data))
 
+    return file_path
 
-# -------- Gradio UI -------- #
+
+# -----------------------------
+# Processing Pipeline
+# -----------------------------
+
+def process_resume(file):
+
+    text = extract_text(file)
+
+    analysis = analyze_resume(text)
+
+    return text, analysis
+
+
+# -----------------------------
+# UI
+# -----------------------------
 
 with gr.Blocks(title="Resume Analyzer") as demo:
 
-    gr.Markdown("# 📄 AI Resume Analyzer")
-    gr.Markdown("Upload your resume and get instant feedback.")
+    gr.Markdown("# AI Resume Analyzer")
+    gr.Markdown("Upload your resume and get an instant score.")
+
+    with gr.Row():
 
-    resume_input = gr.File(
-        label="Upload Resume",
-        file_types=[".pdf", ".docx"]
+        resume_file = gr.File(label="Upload Resume (PDF, DOCX, TXT)")
+
+    load_btn = gr.Button("Analyze Resume")
+
+    resume_text = gr.Textbox(
+        label="Extracted Resume Text",
+        lines=10
     )
 
-    analyze_btn = gr.Button("Analyze Resume")
+    analysis_output = gr.JSON(label="Analysis Result")
+
+    with gr.Row():
+
+        export_json_btn = gr.Button("Export JSON")
+        export_text_btn = gr.Button("Export Text")
+
+    download_file = gr.File(label="Download Analysis")
+
+    # -----------------------------
+    # Button Actions
+    # -----------------------------
+
+    load_btn.click(
+        process_resume,
+        inputs=resume_file,
+        outputs=[resume_text, analysis_output]
+    )
 
-    output_box = gr.Textbox(
-        label="Analysis Result",
-        lines=15
+    export_json_btn.click(
+        export_json,
+        inputs=analysis_output,
+        outputs=download_file
     )
 
-    analyze_btn.click(
-        fn=extract_text,
-        inputs=resume_input,
-        outputs=output_box
+    export_text_btn.click(
+        export_text,
+        inputs=analysis_output,
+        outputs=download_file
     )
 
 
-# -------- Launch App -------- #
+# -----------------------------
+# Launch
+# -----------------------------
 
 if __name__ == "__main__":
     demo.launch()