update

README.md

@@ -17,7 +17,7 @@ AI Career Fair Matcher helps students prioritize career fair companies by analyz
 - Accepts a resume PDF.
 - Uses a built-in company CSV and optionally accepts a user-uploaded CSV.
 - Extracts resume text and builds a structured profile JSON.
-- Supports AI resume parsing through an OpenAI-compatible API with fallback parsing.
+- Uses AI resume parsing through Hugging Face InferenceClient with fallback parsing.
 - Detects ATS providers from company careers URLs.
 - Fetches jobs using requests first and Playwright fallback.
 - Scores jobs with explainable rules.
@@ -57,6 +57,9 @@ AI Career Fair Matcher helps students prioritize career fair companies by analyz
 	`pip install -r ../requirements.txt`
 2. Optional but recommended for Playwright fallback:
 	`playwright install chromium`
+3. For AI resume parsing, set in `.env`:
+  `HF_TOKEN=...`
+  `HF_MODEL=meta-llama/Llama-3.1-8B-Instruct`
 
 ## Output
 - Ranked companies

app.py

@@ -129,7 +129,7 @@ with gr.Blocks(title="AI Career Fair Matcher") as demo:
 
     use_ai_parser_input = gr.Checkbox(
         value=True,
-        label="Use AI Resume Parser (OPENAI_API_KEY or HF_TOKEN)",
+        label="Use AI Resume Parser (HF_TOKEN)",
     )
 
     max_companies_input = gr.Slider(

src/resume/profile_builder.py

@@ -174,7 +174,7 @@ def _parse_resume_with_ai(resume_text: str) -> Optional[ResumeProfile]:
     if not token:
         return None
 
-    model = os.getenv("HF_MODEL", "meta-llama/Llama-3.1-8B-Instruct").strip()
+    model = os.getenv("HF_MODEL", "openai/gpt-oss-120b").strip()
 
     schema_str = json.dumps(SCHEMA_TEMPLATE, indent=2)
     system_prompt = (