added server name and port

app.py

@@ -1,136 +1,136 @@
-import io
-import json
-from typing import Optional
-
-import gradio as gr
-import PyPDF2
-
-from resume_ai import score, improve
-
-def extract_text_from_pdf(file_obj: io.IOBase) -> str:
-    """Extract text from a PDF file-like object."""
-    try:
-        reader = PyPDF2.PdfReader(file_obj)
-        text_chunks = []
-        for page in reader.pages:
-            page_text = page.extract_text() or ""
-            text_chunks.append(page_text)
-        text = "\n".join(text_chunks).strip()
-        if not text:
-            raise ValueError("No extractable text found in PDF.")
-        return text
-    except Exception as e:
-        raise ValueError(f"Error reading PDF: {e}")
-
-def read_resume_to_text(resume_file_path) -> str:
-    """
-    Accepts a file path and returns text content.
-    Supports PDF and plain text files.
-    """
-    if resume_file_path is None:
-        raise ValueError("Please upload a resume file.")
-
-    filename = str(resume_file_path).lower()
-    if filename.endswith(".pdf"):
-        with open(resume_file_path, "rb") as f:
-            return extract_text_from_pdf(f)
-    else:
-        with open(resume_file_path, "rb") as f:
-            data = f.read()
-        if not data:
-            raise ValueError("Uploaded file is empty.")
-        try:
-            return data.decode("utf-8").strip()
-        except UnicodeDecodeError:
-            return data.decode("latin-1").strip()
-
-def score_fn(resume_file_path, job_desc: str) -> str:
-    try:
-        if not job_desc or not job_desc.strip():
-            raise ValueError("Please paste a job description.")
-        resume_text = read_resume_to_text(resume_file_path)
-        result = score(resume_text, job_desc)
-        return json.dumps(result, indent=2, ensure_ascii=False)
-    except Exception as e:
-        return f"Error: {e}"
-
-def improve_fn(resume_file_path, job_desc: Optional[str]) -> str:
-    try:
-        resume_text = read_resume_to_text(resume_file_path)
-        jd_text = job_desc if job_desc and job_desc.strip() else None
-        suggestions = improve(resume_text, jd_text)
-        if isinstance(suggestions, (list, tuple)):
-            bullets = "\n".join(f"- {s}" for s in suggestions)
-            return f"### Suggestions\n{bullets}"
-        elif isinstance(suggestions, dict):
-            return "```json\n" + json.dumps(suggestions, indent=2, ensure_ascii=False) + "\n```"
-        else:
-            return str(suggestions)
-    except Exception as e:
-        return f"Error: {e}"
-
-def format_score_display(result_json) -> str:
-    """
-    Takes the result JSON (as dict or str), parses it, and returns a Markdown string for display.
-    """
-    if isinstance(result_json, str):
-        try:
-            result = json.loads(result_json)
-        except Exception:
-            return f"```\n{result_json}\n```"
-    else:
-        result = result_json
-
-    md = f"## 🏆 ATS Compatibility Score: **{result.get('overall_score', 0)}%**\n\n"
-    md += "### Category Scores\n"
-    md += "| Skills | Experience | Education |\n"
-    md += "|--------|------------|-----------|\n"
-    cs = result.get("category_scores", {})
-    md += f"| {cs.get('skills',0)}% | {cs.get('experience',0)}% | {cs.get('education',0)}% |\n\n"
-
-    gaps = result.get("top_skill_gaps", [])
-    if gaps:
-        md += "### 🚩 Top Skill Gaps\n"
-        for gap in gaps:
-            md += f"- {gap}\n"
-    return md
-
-# ...existing code...
-
-with gr.Blocks(title="Resume AI (Score & Improve)") as demo:
-    gr.Markdown(
-        """
-        # 📄 Resume AI — Score & Improve
-        Upload your resume (PDF or TXT), paste a Job Description, and get:
-        - **Score**: A formatted breakdown of your resume's ATS compatibility
-        - **Improve**: A healthy set of suggestions to enhance your resume
-        """
-    )
-
-    with gr.Row():
-        resume = gr.File(label="Upload Resume (PDF or TXT)", file_types=[".pdf", ".txt"], type="filepath")
-        jd = gr.Textbox(label="Job Description (paste here)", lines=10, placeholder="Paste JD text...")
-
-    with gr.Row():
-        score_btn = gr.Button("⚖️ Score Resume", variant="primary")
-        improve_btn = gr.Button("✨ Improve Resume")
-
-    score_out = gr.Markdown(label="Score (Formatted)")
-    improve_out = gr.Markdown(label="Improvement Suggestions")
-
-    def score_fn_display(resume_file_path, job_desc: str) -> str:
-        try:
-            if not job_desc or not job_desc.strip():
-                raise ValueError("Please paste a job description.")
-            resume_text = read_resume_to_text(resume_file_path)
-            result = score(resume_text, job_desc)
-            return format_score_display(result)
-        except Exception as e:
-            return f"Error: {e}"
-
-    score_btn.click(fn=score_fn_display, inputs=[resume, jd], outputs=score_out)
-    improve_btn.click(fn=improve_fn, inputs=[resume, jd], outputs=improve_out)
-
-
-if __name__ == "__main__":
-    demo.queue().launch()
+import io
+import json
+from typing import Optional
+
+import gradio as gr
+import PyPDF2
+
+from resume_ai import score, improve
+
+def extract_text_from_pdf(file_obj: io.IOBase) -> str:
+    """Extract text from a PDF file-like object."""
+    try:
+        reader = PyPDF2.PdfReader(file_obj)
+        text_chunks = []
+        for page in reader.pages:
+            page_text = page.extract_text() or ""
+            text_chunks.append(page_text)
+        text = "\n".join(text_chunks).strip()
+        if not text:
+            raise ValueError("No extractable text found in PDF.")
+        return text
+    except Exception as e:
+        raise ValueError(f"Error reading PDF: {e}")
+
+def read_resume_to_text(resume_file_path) -> str:
+    """
+    Accepts a file path and returns text content.
+    Supports PDF and plain text files.
+    """
+    if resume_file_path is None:
+        raise ValueError("Please upload a resume file.")
+
+    filename = str(resume_file_path).lower()
+    if filename.endswith(".pdf"):
+        with open(resume_file_path, "rb") as f:
+            return extract_text_from_pdf(f)
+    else:
+        with open(resume_file_path, "rb") as f:
+            data = f.read()
+        if not data:
+            raise ValueError("Uploaded file is empty.")
+        try:
+            return data.decode("utf-8").strip()
+        except UnicodeDecodeError:
+            return data.decode("latin-1").strip()
+
+def score_fn(resume_file_path, job_desc: str) -> str:
+    try:
+        if not job_desc or not job_desc.strip():
+            raise ValueError("Please paste a job description.")
+        resume_text = read_resume_to_text(resume_file_path)
+        result = score(resume_text, job_desc)
+        return json.dumps(result, indent=2, ensure_ascii=False)
+    except Exception as e:
+        return f"Error: {e}"
+
+def improve_fn(resume_file_path, job_desc: Optional[str]) -> str:
+    try:
+        resume_text = read_resume_to_text(resume_file_path)
+        jd_text = job_desc if job_desc and job_desc.strip() else None
+        suggestions = improve(resume_text, jd_text)
+        if isinstance(suggestions, (list, tuple)):
+            bullets = "\n".join(f"- {s}" for s in suggestions)
+            return f"### Suggestions\n{bullets}"
+        elif isinstance(suggestions, dict):
+            return "```json\n" + json.dumps(suggestions, indent=2, ensure_ascii=False) + "\n```"
+        else:
+            return str(suggestions)
+    except Exception as e:
+        return f"Error: {e}"
+
+def format_score_display(result_json) -> str:
+    """
+    Takes the result JSON (as dict or str), parses it, and returns a Markdown string for display.
+    """
+    if isinstance(result_json, str):
+        try:
+            result = json.loads(result_json)
+        except Exception:
+            return f"```\n{result_json}\n```"
+    else:
+        result = result_json
+
+    md = f"## 🏆 ATS Compatibility Score: **{result.get('overall_score', 0)}%**\n\n"
+    md += "### Category Scores\n"
+    md += "| Skills | Experience | Education |\n"
+    md += "|--------|------------|-----------|\n"
+    cs = result.get("category_scores", {})
+    md += f"| {cs.get('skills',0)}% | {cs.get('experience',0)}% | {cs.get('education',0)}% |\n\n"
+
+    gaps = result.get("top_skill_gaps", [])
+    if gaps:
+        md += "### 🚩 Top Skill Gaps\n"
+        for gap in gaps:
+            md += f"- {gap}\n"
+    return md
+
+# ...existing code...
+
+with gr.Blocks(title="Resume AI (Score & Improve)") as demo:
+    gr.Markdown(
+        """
+        # 📄 Resume AI — Score & Improve
+        Upload your resume (PDF or TXT), paste a Job Description, and get:
+        - **Score**: A formatted breakdown of your resume's ATS compatibility
+        - **Improve**: A healthy set of suggestions to enhance your resume
+        """
+    )
+
+    with gr.Row():
+        resume = gr.File(label="Upload Resume (PDF or TXT)", file_types=[".pdf", ".txt"], type="filepath")
+        jd = gr.Textbox(label="Job Description (paste here)", lines=10, placeholder="Paste JD text...")
+
+    with gr.Row():
+        score_btn = gr.Button("⚖️ Score Resume", variant="primary")
+        improve_btn = gr.Button("✨ Improve Resume")
+
+    score_out = gr.Markdown(label="Score (Formatted)")
+    improve_out = gr.Markdown(label="Improvement Suggestions")
+
+    def score_fn_display(resume_file_path, job_desc: str) -> str:
+        try:
+            if not job_desc or not job_desc.strip():
+                raise ValueError("Please paste a job description.")
+            resume_text = read_resume_to_text(resume_file_path)
+            result = score(resume_text, job_desc)
+            return format_score_display(result)
+        except Exception as e:
+            return f"Error: {e}"
+
+    score_btn.click(fn=score_fn_display, inputs=[resume, jd], outputs=score_out)
+    improve_btn.click(fn=improve_fn, inputs=[resume, jd], outputs=improve_out)
+
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0",server_port=7860, pwa=True)