Update app.py

app.py

@@ -5,15 +5,15 @@ import csv
 import tempfile
 import time
 from typing import List, Dict, Any, Tuple
-import requests
 import PyPDF2
 import docx2txt
 import gradio as gr
 import pandas as pd
 import logging
+from openai import OpenAI
 
 # Configure logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+logging.basicConfig(level=logging.INFO, format="%(asctime)s - %(levelname)s - %(message)s")
 
 # Global Configuration
 DEEPINFRA_API_KEY = "kPEm10rrnxXrCf0TuB6Xcd7Y7lp3YgKa"
@@ -21,6 +21,12 @@ DEEPINFRA_BASE_URL = "https://api.deepinfra.com/v1/openai"
 DEFAULT_MODEL = "Qwen/Qwen3-32B"
 REQUEST_TIMEOUT_SECS = 120
 
+# OpenAI client for DeepInfra
+default_client = OpenAI(
+    api_key=DEEPINFRA_API_KEY,
+    base_url=DEEPINFRA_BASE_URL,
+)
+
 # Prompts for LLM Calls
 JD_SYSTEM = """You are an expert recruitment analyst. Extract a job description into STRICT JSON.
 Rules:
@@ -57,7 +63,6 @@ Schema:
 }
 """
 
-# New feedback system prompt with detailed scoring
 FEEDBACK_SYSTEM_DETAILED = """You are an expert technical recruiter. Compare a job and a candidate and return STRICT JSON with actionable feedback and a detailed score breakdown.
 Respond in the job description's language.
 Scores should be out of 100.
@@ -79,14 +84,13 @@ Keep each bullet short (max ~12 words).
 Output ONLY JSON.
 """
 
-# New recommendation system prompt
 RECOMMEND_SYSTEM = """You are a senior technical recruiter writing a concise recommendation summary for a hiring manager.
 Based on the provided candidate and job description, write a 2-3 sentence summary explaining why this candidate is a good match.
 Focus on key skills, relevant experience, and overall fit. Do not use a conversational tone.
-Output ONLY the summary text, no markdown or extra formatting."""
-
+Output ONLY the summary text, no markdown or extra formatting.
+"""
 
-# Helper Functions
+# Helpers for file parsing
 def _pdf_to_text(path: str) -> str:
     text = []
     with open(path, "rb") as f:
@@ -125,28 +129,20 @@ def safe_json_loads(text: str) -> dict:
         logging.error(f"Failed to parse JSON: {e}\nRaw Text: {text[:500]}...")
         return {}
 
+# LLM chat wrapper
 def deepinfra_chat(messages: List[Dict[str, str]], api_key: str, model: str, temperature: float = 0.2) -> str:
-    if not api_key:
-        raise RuntimeError("Missing API Key.")
-    payload = {
-        "model": model,
-        "messages": messages,
-        "temperature": temperature,
-    }
     try:
-        resp = requests.post(
-            DEEPINFRA_BASE_URL,
-            headers={
-                "Authorization": f"Bearer {api_key}",
-                "Content-Type": "application/json",
-            },
-            data=json.dumps(payload),
-            timeout=REQUEST_TIMEOUT_SECS,
+        client = default_client
+        if api_key and api_key != DEEPINFRA_API_KEY:
+            client = OpenAI(api_key=api_key, base_url=DEEPINFRA_BASE_URL)
+
+        resp = client.chat.completions.create(
+            model=model,
+            messages=messages,
+            temperature=temperature,
         )
-        resp.raise_for_status()
-        data = resp.json()
-        return (data.get("choices", [{}])[0].get("message", {}).get("content", "") or "").strip()
-    except requests.exceptions.RequestException as e:
+        return (resp.choices[0].message.content or "").strip()
+    except Exception as e:
         logging.error(f"API request failed: {e}")
         raise gr.Error(f"API request failed: {e}. Check your API key and model name.")
 
@@ -174,7 +170,7 @@ def load_resume(resume_file) -> Tuple[str, str]:
     text = read_file_safely(resume_file.name)
     return text, fname
 
-# LLM-based Extraction Functions
+# Extraction Functions
 def llm_extract_jd(jd_text: str, api_key: str, model: str, temperature: float = 0.1) -> Dict:
     messages = [
         {"role": "system", "content": JD_SYSTEM},
@@ -191,7 +187,6 @@ def llm_extract_resume(resume_text: str, api_key: str, model: str, temperature:
     raw = deepinfra_chat(messages, api_key=api_key, model=model, temperature=temperature)
     return safe_json_loads(raw)
 
-# New function for detailed feedback and scoring
 def llm_detailed_feedback(jd_struct: Dict, resume_struct: Dict, api_key: str, model: str, temperature: float = 0.2) -> Dict:
     prompt = json.dumps({"job": jd_struct, "candidate": resume_struct}, ensure_ascii=False)
     messages = [
@@ -201,7 +196,6 @@ def llm_detailed_feedback(jd_struct: Dict, resume_struct: Dict, api_key: str, mo
     raw = deepinfra_chat(messages, api_key=api_key, model=model, temperature=temperature)
     return safe_json_loads(raw)
 
-# New function for candidate recommendation summary
 def llm_recommend(jd_struct: Dict, resume_struct: Dict, api_key: str, model: str, temperature: float = 0.2) -> str:
     prompt = json.dumps({"job": jd_struct, "candidate": resume_struct}, ensure_ascii=False)
     messages = [