Update app.py

app.py

@@ -3,23 +3,26 @@ import re
 from typing import Tuple, List
 import gradio as gr
 
-# -----------------------------
+# =============================
 # Config
-# -----------------------------
+# =============================
 OPENAI_API_KEY = os.getenv("OPENAI_API_KEY", "")
 USE_OPENAI = bool(OPENAI_API_KEY)
 
+# Prefer the most reliable model first, with fallbacks
+PREFERRED_MODELS = ["gpt-4o", "gpt-4o-mini", "gpt-3.5-turbo"]
+
+oai_client = None
 if USE_OPENAI:
     try:
         from openai import OpenAI
         oai_client = OpenAI(api_key=OPENAI_API_KEY)
-        OAI_MODEL = "gpt-4o-mini"
     except Exception:
         USE_OPENAI = False
 
-# -----------------------------
+# =============================
 # Helpers
-# -----------------------------
+# =============================
 def normalize_text(t: str) -> str:
     return re.sub(r"\s+", " ", (t or "").strip())
 
@@ -35,25 +38,31 @@ def keyword_match_score(resume: str, jd: str) -> Tuple[float, List[str]]:
     score = 100.0 * len(overlap) / max(1, len(j_set))
     return round(score, 1), overlap[:30]
 
-def call_openai(system_prompt: str, user_prompt: str) -> str:
-    if not USE_OPENAI:
+def call_openai_with_fallback(system_prompt: str, user_prompt: str) -> str:
+    """Try several models; return first successful response or empty string."""
+    if not (USE_OPENAI and oai_client):
         return ""
-    try:
-        resp = oai_client.chat.completions.create(
-            model=OAI_MODEL,
-            messages=[
-                {"role": "system", "content": system_prompt},
-                {"role": "user", "content": user_prompt},
-            ],
-            temperature=0.5,
-        )
-        return resp.choices[0].message.content.strip()
-    except Exception:
-        return "(Note: OpenAI call failed; using mock output.)"
-
-# -----------------------------
+    for model in PREFERRED_MODELS:
+        try:
+            resp = oai_client.chat.completions.create(
+                model=model,
+                messages=[
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": user_prompt},
+                ],
+                temperature=0.5,
+            )
+            content = resp.choices[0].message.content
+            if content:
+                return content.strip()
+        except Exception:
+            # Try next model in the list
+            continue
+    return ""
+
+# =============================
 # Prompts
-# -----------------------------
+# =============================
 SYS_BASE = (
     "You are CareerForge, a concise, supportive career assistant. "
     "Prioritize clarity, actionability, fairness, and transparency. "
@@ -105,9 +114,9 @@ BIAS_REFLECTION = (
     "Limitations: Human review is always essential."
 )
 
-# -----------------------------
+# =============================
 # Core Functions
-# -----------------------------
+# =============================
 def ingest_inputs(jd_text: str, resume_text: str):
     jd_clean = normalize_text(jd_text)
     resume_clean = normalize_text(resume_text)
@@ -118,10 +127,9 @@ def ingest_inputs(jd_text: str, resume_text: str):
 def generate_improvements(jd_clean: str, resume_clean: str):
     if not jd_clean or not resume_clean:
         return "Please provide both Job Description and Resume."
-    if USE_OPENAI:
-        out = call_openai(SYS_BASE, prompt_resume_improvements(resume_clean, jd_clean))
-        if out:
-            return out
+    out = call_openai_with_fallback(SYS_BASE, prompt_resume_improvements(resume_clean, jd_clean))
+    if out:
+        return out
     # Mock fallback
     score, matched = keyword_match_score(resume_clean, jd_clean)
     return (
@@ -137,10 +145,9 @@ def generate_improvements(jd_clean: str, resume_clean: str):
 def generate_cover_letter(jd_clean: str, resume_clean: str):
     if not jd_clean or not resume_clean:
         return "Please provide both Job Description and Resume."
-    if USE_OPENAI:
-        out = call_openai(SYS_BASE, prompt_cover_letter(resume_clean, jd_clean))
-        if out:
-            return out
+    out = call_openai_with_fallback(SYS_BASE, prompt_cover_letter(resume_clean, jd_clean))
+    if out:
+        return out
     return (
         "(Mock Cover Letter)\n"
         "Dear Hiring Manager,\n\n"
@@ -154,10 +161,9 @@ def generate_cover_letter(jd_clean: str, resume_clean: str):
 def generate_mock_interview(jd_clean: str, resume_clean: str, style: str):
     if not jd_clean or not resume_clean:
         return "Please provide both Job Description and Resume."
-    if USE_OPENAI:
-        out = call_openai(SYS_BASE, prompt_mock_interview(resume_clean, jd_clean, style))
-        if out:
-            return out
+    out = call_openai_with_fallback(SYS_BASE, prompt_mock_interview(resume_clean, jd_clean, style))
+    if out:
+        return out
     return (
         f"(Mock {style} Interview)\n\n"
         "Q1: Tell me about a project relevant to this role.\n"
@@ -177,15 +183,15 @@ def generate_mock_interview(jd_clean: str, resume_clean: str, style: str):
         "Tip: Reference 1–2 specifics from the JD."
     )
 
-# -----------------------------
+# =============================
 # Demo Defaults
-# -----------------------------
+# =============================
 DEMO_JD = "Responsibilities: Collaborate, improve processes, analyze metrics. Preferred: Python, SQL."
 DEMO_RESUME = "Experience: Assisted in process improvement; created reports. Skills: Python, SQL, Excel."
 
-# -----------------------------
+# =============================
 # UI
-# -----------------------------
+# =============================
 with gr.Blocks(title="CareerForge — Resume & Interview Coach") as demo:
     gr.Markdown("# CareerForge — Resume & Interview Coach")
     gr.Markdown(