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app.py

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+
+import os
+import json
+import gradio as gr
+from pipeline import run_pipeline
+
+DEFAULT_CONFIG = {
+    "model": os.getenv("OPENAI_MODEL", "gpt-4o-mini"),
+    "rewrite": False
+}
+
+def process(files, config_json):
+    if not files:
+        return None
+
+    try:
+        config = json.loads(config_json) if config_json else DEFAULT_CONFIG
+    except Exception:
+        config = DEFAULT_CONFIG
+
+    input_paths = [f.name for f in files]
+
+    zip_path = run_pipeline(
+        input_files=input_paths,
+        config=config
+    )
+
+    return zip_path
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Resume Evaluator")
+
+    files = gr.File(
+        label="Upload PDF(s)",
+        file_count="multiple",
+        type="file"
+    )
+
+    config_json = gr.Textbox(
+        label="Config JSON (optional)",
+        value=json.dumps(DEFAULT_CONFIG, indent=2),
+        lines=6
+    )
+
+    btn = gr.Button("Process")
+    output = gr.File(label="Download Results ZIP")
+
+    btn.click(fn=process, inputs=[files, config_json], outputs=output)
+
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)

pipeline.py

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+import os
+import json
+import time
+import shutil
+import zipfile
+from pathlib import Path
+from typing import Dict, List, Any
+
+# ---------------------------------------------------------
+# IMPORTANT:
+# You MUST paste/call your existing notebook functions here.
+# Minimal changes:
+# - your PDF->text extractor
+# - your LLM evaluator
+# - your export_to_drive_clean logic (renamed to local export)
+# ---------------------------------------------------------
+
+def _safe_mkdir(p: str) -> None:
+    Path(p).mkdir(parents=True, exist_ok=True)
+
+def _zip_dir(folder: str, zip_path: str) -> str:
+    folder = str(Path(folder).resolve())
+    zip_path = str(Path(zip_path).resolve())
+    with zipfile.ZipFile(zip_path, "w", zipfile.ZIP_DEFLATED) as z:
+        for p in Path(folder).rglob("*"):
+            if p.is_file():
+                z.write(str(p), arcname=str(p).replace(folder + "/", ""))
+    return zip_path
+
+def run_pipeline(input_files: List[str], config: Dict[str, Any]) -> str:
+    """
+    input_files: absolute paths to PDFs
+    config: dict containing projects/thresholds/model/rewrite, etc.
+    returns: path to output zip
+    """
+    # Output dirs (HF-safe: use /tmp)
+    run_id = f"run_{int(time.time())}"
+    base_out = Path("/tmp") / run_id
+    input_dir = base_out / "INPUT_PDFS"
+    output_dir = base_out / "OUTPUT"
+
+    _safe_mkdir(str(input_dir))
+    _safe_mkdir(str(output_dir))
+
+    # 1) Copy inputs
+    pdf_paths = []
+    for f in input_files:
+        src = Path(f)
+        if not src.exists():
+            continue
+        if src.suffix.lower() != ".pdf":
+            continue
+        dst = input_dir / src.name
+        shutil.copy2(src, dst)
+        pdf_paths.append(str(dst))
+
+    if not pdf_paths:
+        raise RuntimeError("No PDFs provided.")
+
+    # 2) ---- YOUR PIPELINE HERE ----
+    # You must replace this placeholder with your real pipeline logic.
+    # The end result MUST be: evaluations = List[dict]
+    evaluations: List[dict] = []
+
+    # TODO: call your pdf->text + llm evaluation here
+    # evaluations = evaluate_pdfs(pdf_paths, config)
+
+    # Minimal placeholder to prove flow works:
+    for p in pdf_paths:
+        evaluations.append({
+            "filename": os.path.basename(p),
+            "candidate_name": os.path.splitext(os.path.basename(p))[0],
+            "scores": {"skill": 0, "experience": 0, "growth": 0, "context_fit": 0},
+            "tags": ["STANDARD"]
+        })
+
+    # 3) Export artifacts to output_dir
+    # TODO: replace with your real export logic (bucket folders, csv, master index)
+    with open(output_dir / "master_index.json", "w", encoding="utf-8") as f:
+        json.dump({"count": len(evaluations), "evaluations": evaluations}, f, indent=2)
+
+    # 4) Zip output
+    zip_path = str(base_out / "results.zip")
+    _zip_dir(str(output_dir), zip_path)
+
+    return zip_path

requirements.txt

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+gradio>=4.0.0
+openai>=1.0.0
+pypdf