improve UI and enable LLM selection

app.py

@@ -4,7 +4,7 @@ import os
 import re
 import json
 from dotenv import load_dotenv
-from openai import OpenAI
+from llm_client import LLMClient
 from pypdf import PdfReader
 import gradio as gr
 
@@ -26,17 +26,41 @@ def read_resume(file_obj):
     else:
         return file_obj.read().decode("utf-8")
 
-def analyze_resume(resume: str, job_title: str, job_desc: str) -> dict:
-    """Call the LLM to evaluate the resume vs the job title & description."""
+def analyze_resume(resume_content: str, job_title: str, job_description: str) -> dict:
+    """Analyzes a candidate's resume against a specific job title and description.
+
+    This tool evaluates the compatibility of a resume with a given job, providing a comprehensive assessment including an overall score, interview likelihood, matching skills, and areas for improvement.
+
+    Args:
+        resume_content (str): The full text content of the candidate's resume.
+        job_title (str): The title of the job for which the resume is being evaluated.
+        job_description (str): The detailed description of the job, including responsibilities and requirements.
+
+    Returns:
+        dict: A JSON object containing the analysis results with the following keys:
+            - overall_score (int): A score from 1-10 indicating overall fit.
+            - interview_likelihood (str): "High", "Medium", or "Low" likelihood of an interview.
+            - matching_skills (list[str]): A list of skills from the resume that match the job description.
+            - evaluation_summary (str): A concise summary of the resume's evaluation.
+            - experience_match (str): Assessment of how well the candidate's experience matches the job.
+            - education_match (str): Assessment of how well the candidate's education matches the job.
+            - strengths (list[str]): Key strengths identified in the resume relevant to the job.
+            - missing_skills (list[str]): Skills required by the job but missing from the resume.
+            - improvement_areas (list[str]): Areas where the resume could be improved for better fit.
+            - recommendations (list[str]): Actionable recommendations for the candidate.
+    """
+    llm_provider = os.getenv("LLM_PROVIDER", "openai")
+    llm_model = os.getenv("LLM_MODEL", "gpt-4o-mini")
+    llm_client = LLMClient(provider=llm_provider, model=llm_model)
     prompt = {
         "role": "user",
         "content": (
             "You are an expert career coach.\n\n"
             f"**Job Title:** {job_title}\n\n"
             "**Job Description (first 5k chars):**\n```\n"
-            + job_desc.strip()[:5000] + "\n```\n\n"
-            "**Candidate’s Resume** (first 5k chars):\n```\n"
-            + resume[:5000] + "\n```\n\n"
+            + job_description.strip()[:5000] + "\n```\n\n"
+            "**Candidate's Resume** (first 5k chars):\n```\n"
+            + resume_content[:5000] + "\n```\n\n"
             "Reply only in JSON with these keys:\n"
             "  overall_score (int 1–10),\n"
             "  interview_likelihood (\"High\"/\"Medium\"/\"Low\"),\n"
@@ -47,11 +71,10 @@ def analyze_resume(resume: str, job_title: str, job_desc: str) -> dict:
             "  strengths […],\n"
             "  missing_skills […],\n"
             "  improvement_areas […],\n"
-            "  recommendations […]\n"
+            "  recommendations […]"
         )
     }
-    resp = openai.chat.completions.create(
-        model="gpt-4o-mini",
+    resp = llm_client.chat_completion(
         messages=[prompt]
     )
     raw = resp.choices[0].message.content
@@ -99,7 +122,6 @@ def format_recs(res):
 # ─── Main Logic & UI ─────────────────────────────────────────────────────────────
 
 load_dotenv()
-openai = OpenAI()
 
 def run_agent(resume_file, job_title, job_desc):
     # Validate inputs
@@ -110,7 +132,6 @@ def run_agent(resume_file, job_title, job_desc):
     # Read and analyze
     resume = read_resume(resume_file)
     result = analyze_resume(resume, job_title, job_desc)
-
     # Handle parse errors
     if "error" in result:
         err_md = f"**Error:** {result['error']}\n\n```{result.get('raw','')}```"
@@ -126,32 +147,186 @@ def run_agent(resume_file, job_title, job_desc):
     return summary_md, overview_md, strengths_md, gaps_md, recs_md
 
 # Build the Gradio app
-app = gr.Blocks()
+app = gr.Blocks(
+    title="Skills Gap Advisor",
+    theme=gr.themes.Soft(
+        primary_hue="indigo",
+        secondary_hue="purple",
+        neutral_hue="slate",
+        font=gr.themes.GoogleFont("Inter")
+    ),
+    css="""
+    .gradio-container {
+        max-width: 1200px !important;
+        margin: 0 auto !important;
+        background: #0f172a !important;
+        color: #e2e8f0 !important;
+    }
+    .main-header {
+        text-align: center;
+        margin-bottom: 2rem;
+        padding: 2rem 0;
+        background: linear-gradient(135deg, #4f46e5 0%, #7c3aed 100%);
+        color: #ffffff !important;
+        border-radius: 12px;
+        margin-bottom: 2rem;
+        box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1);
+    }
+    .input-section {
+        background: #1e293b !important;
+        padding: 1.5rem;
+        border-radius: 12px;
+        border: 1px solid #334155;
+        margin-bottom: 1rem;
+        box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1);
+    }
+    .results-section {
+        background: #1e293b !important;
+        border-radius: 12px;
+        border: 1px solid #334155;
+        overflow: hidden;
+        box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1);
+    }
+    .summary-card {
+        background: linear-gradient(135deg, #4f46e5 0%, #7c3aed 100%);
+        color: #ffffff !important;
+        padding: 1.5rem;
+        border-radius: 12px;
+        margin-bottom: 1rem;
+        box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1);
+    }
+    .tabs {
+        background: #1e293b !important;
+        border-radius: 12px;
+        padding: 1rem;
+    }
+    .tab-nav {
+        border-bottom: 1px solid #334155 !important;
+    }
+    .tab-nav button {
+        color: #94a3b8 !important;
+    }
+    .tab-nav button.selected {
+        color: #4f46e5 !important;
+        border-bottom: 2px solid #4f46e5 !important;
+    }
+    .tab-content {
+        background: #1e293b !important;
+        color: #e2e8f0 !important;
+        padding: 1rem;
+    }
+    .markdown {
+        color: #e2e8f0 !important;
+    }
+    .markdown h1, .markdown h2, .markdown h3 {
+        color: #ffffff !important;
+    }
+    .markdown strong {
+        color: #4f46e5 !important;
+    }
+    .markdown ul li {
+        color: #e2e8f0 !important;
+    }
+    .accordion {
+        background: #1e293b !important;
+        border: 1px solid #334155 !important;
+    }
+    .accordion-title {
+        color: #e2e8f0 !important;
+    }
+    .accordion-content {
+        color: #94a3b8 !important;
+    }
+    .button-primary {
+        background: linear-gradient(135deg, #4f46e5 0%, #7c3aed 100%) !important;
+        color: white !important;
+        border: none !important;
+        box-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1) !important;
+    }
+    .button-primary:hover {
+        background: linear-gradient(135deg, #4338ca 0%, #6d28d9 100%) !important;
+        transform: translateY(-1px);
+        box-shadow: 0 6px 8px -1px rgb(0 0 0 / 0.1) !important;
+    }
+    """
+)
 
 with app:
-    gr.Markdown("## 🤖 AI-Powered Resume vs Job Description Analyzer")
+    gr.Markdown(
+        """
+        <div class="main-header">
+            <h1>🤖 Skills Gap Advisor</h1>
+            <p>AI-Powered Resume Analysis & Career Guidance</p>
+            <p><em>Powered by configurable LLM providers • Available as MCP Tool</em></p>
+        </div>
+        """,
+        elem_classes=["main-header"]
+    )
 
     with gr.Row():
         # ── Sidebar for inputs ─────────────────────────
-        with gr.Column(scale=1):
-            title_input  = gr.Textbox(label="🏷️ Job Title", placeholder="e.g. Data Scientist")
-            job_input    = gr.Textbox(label="📋 Job Description", lines=4,
-                                     placeholder="Copy-paste the full JD here…")
-            resume_input = gr.File(label="📄 Resume (PDF or TXT)")
-            run_btn      = gr.Button("🔍 Analyze")
+        with gr.Column(scale=1, elem_classes=["input-section"]):
+            gr.Markdown("### 📝 Job & Resume Information")
+            title_input  = gr.Textbox(
+                label="🏷️ Job Title", 
+                placeholder="e.g. Senior Data Scientist",
+                info="Enter the exact job title you're applying for"
+            )
+            job_input    = gr.Textbox(
+                label="📋 Job Description", 
+                lines=6,
+                placeholder="Copy-paste the complete job description here...",
+                info="Include requirements, responsibilities, and qualifications"
+            )
+            resume_input = gr.File(
+                label="📄 Resume Upload", 
+                file_types=[".pdf", ".txt"]
+            )
+            
+            with gr.Accordion("⚙️ Advanced Settings", open=False):
+                gr.Markdown("**LLM Configuration** (via environment variables)")
+                gr.Markdown(
+                    """
+                    - `LLM_PROVIDER`: openai, gemini, deepseek
+                    - `LLM_MODEL`: Model name (e.g., gpt-4o-mini, gemini-2.0-flash, deepseek-chat)
+                    - API keys: `OPENAI_API_KEY`, `GEMINI_API_KEY`, `DEEPSEEK_API_KEY`
+                    """
+                )
+            
+            run_btn = gr.Button(
+                "🔍 Analyze Resume", 
+                variant="primary", 
+                size="lg",
+                scale=1,
+                elem_classes=["button-primary"]
+            )
 
         # ── Main area for results ───────────────────────
-        with gr.Column(scale=2):
-            summary_display   = gr.Markdown()
-            with gr.Tabs():
-                with gr.TabItem("Overview"):
-                    overview_display = gr.Markdown()
-                with gr.TabItem("Strengths"):
-                    strengths_display = gr.Markdown()
-                with gr.TabItem("Gaps"):
-                    gaps_display = gr.Markdown()
-                with gr.TabItem("Recommendations"):
-                    recs_display = gr.Markdown()
+        with gr.Column(scale=2, elem_classes=["results-section"]):
+            gr.Markdown("### 📊 Analysis Results")
+            
+            summary_display = gr.Markdown(
+                elem_classes=["summary-card"],
+                value="Upload a resume and job description to see the analysis results here."
+            )
+            
+            with gr.Tabs(elem_classes=["tabs"]):
+                with gr.TabItem("📋 Overview", elem_id="overview-tab"):
+                    overview_display = gr.Markdown(
+                        value="Detailed evaluation summary will appear here after analysis."
+                    )
+                with gr.TabItem("💪 Strengths", elem_id="strengths-tab"):
+                    strengths_display = gr.Markdown(
+                        value="Your matching skills and key strengths will be highlighted here."
+                    )
+                with gr.TabItem("🎯 Gaps", elem_id="gaps-tab"):
+                    gaps_display = gr.Markdown(
+                        value="Missing skills and improvement areas will be identified here."
+                    )
+                with gr.TabItem("💡 Recommendations", elem_id="recommendations-tab"):
+                    recs_display = gr.Markdown(
+                        value="Personalized recommendations for improving your application will appear here."
+                    )
 
     # Wire up the callback
     run_btn.click(
@@ -171,6 +346,6 @@ if __name__ == "__main__":
     app.launch(
         server_name="0.0.0.0",
         server_port=7860,
-        debug=True
-        # for hackathon submission, add mcp_server=True
-    )
+        debug=True,
+        mcp_server=True
+    )

llm_client.py

@@ -0,0 +1,35 @@
+import os
+from openai import OpenAI
+
+class LLMClient:
+    def __init__(self, provider: str = "openai", model: str = "gpt-4o-mini"):
+        self.provider = provider
+        self.model = model
+        self.client = self._initialize_client()
+
+    def _initialize_client(self):
+        if self.provider == "openai":
+            return OpenAI()
+        elif self.provider == "gemini":
+            return OpenAI(
+                api_key=os.getenv("GEMINI_API_KEY"),
+                base_url="https://generativelanguage.googleapis.com/v1beta/openai/"
+            )
+        elif self.provider == "deepseek":
+            return OpenAI(
+                api_key=os.getenv("DEEPSEEK_API_KEY"),
+                base_url="https://api.deepseek.com/v1"
+            )
+        # Add other providers here later
+        else:
+            raise ValueError(f"Unsupported LLM provider: {self.provider}")
+
+    def chat_completion(self, messages: list):
+        if self.provider == "openai" or self.provider == "gemini" or self.provider == "deepseek":
+            return self.client.chat.completions.create(
+                model=self.model,
+                messages=messages
+            )
+        # Add other providers' chat completion logic here later
+        else:
+            raise ValueError(f"Unsupported LLM provider: {self.provider}")

requirements.txt

@@ -1,4 +1,4 @@
-gradio
+gradio[mcp]
 openai
 pypdf
 python-dotenv