Add app.py

app.py

@@ -0,0 +1,114 @@
+import os
+import yaml
+import gradio as gr
+from dotenv import load_dotenv
+from agno.agent import Agent
+from agno.models.nebius import Nebius
+from agno.tools.github import GithubTools
+from agno.tools.exa import ExaTools
+from agno.tools.thinking import ThinkingTools
+from agno.tools.reasoning import ReasoningTools
+
+# Load environment variables from .env if present
+load_dotenv()
+
+# Load prompts from YAML file
+with open("hiring_prompts.yaml", "r", encoding="utf-8") as f:
+    prompts = yaml.safe_load(f)
+
+# Extract prompt sections
+description_multi = prompts.get("description_for_multi_candidates", "")
+instructions_multi = prompts.get("instructions_for_multi_candidates", "")
+description_single = prompts.get("description_for_single_candidate", "")
+instructions_single = prompts.get("instructions_for_single_candidate", "")
+
+def analyze_multi(github_usernames, job_role, nebius_api_key, model_id, github_api_key, exa_api_key):
+    """
+    Analyze multiple GitHub users for a given job role.
+    github_usernames: string with newline separated usernames.
+    Returns a markdown report.
+    """
+    if not github_usernames or not job_role:
+        return "Please provide GitHub usernames and job role."
+    usernames = [u.strip() for u in github_usernames.split("\n") if u.strip()]
+    if not usernames:
+        return "No valid GitHub usernames found."
+    query = f"Evaluate GitHub candidates for role '{job_role}': {', '.join(usernames)}"
+    agent = Agent(
+        description=description_multi,
+        instructions=instructions_multi,
+        model=Nebius(id=model_id, api_key=nebius_api_key),
+        name="CandilyzerMulti",
+        tools=[
+            ThinkingTools(think=True, instructions="Strict GitHub candidate evaluation"),
+            GithubTools(access_token=github_api_key),
+            ExaTools(api_key=exa_api_key, include_domains=["github.com"], type="keyword"),
+            ReasoningTools(add_instructions=True),
+        ],
+        markdown=True,
+        show_tool_calls=True,
+    )
+    stream = agent.run(query, stream=True)
+    output = ""
+    for chunk in stream:
+        if hasattr(chunk, "content") and isinstance(chunk.content, str):
+            output += chunk.content
+    return output
+
+def analyze_single(github_username, linkedin_url, job_role, nebius_api_key, model_id, github_api_key, exa_api_key):
+    """
+    Analyze a single GitHub (and optional LinkedIn) candidate for a given job role.
+    """
+    if not github_username or not job_role:
+        return "Please provide GitHub username and job role."
+    query = f"Analyze candidate for {job_role}. GitHub: {github_username}"
+    if linkedin_url:
+        query += f", LinkedIn: {linkedin_url}"
+    agent = Agent(
+        description=description_single,
+        instructions=instructions_single,
+        model=Nebius(id=model_id, api_key=nebius_api_key),
+        name="CandilyzerSingle",
+        tools=[
+            ThinkingTools(add_instructions=True),
+            GithubTools(access_token=github_api_key),
+            ExaTools(api_key=exa_api_key, include_domains=["linkedin.com", "github.com"], type="keyword"),
+            ReasoningTools(add_instructions=True),
+        ],
+        markdown=True,
+        show_tool_calls=True,
+        add_datetime_to_instructions=True,
+    )
+    stream = agent.run(query, stream=True)
+    output = ""
+    for chunk in stream:
+        if hasattr(chunk, "content") and isinstance(chunk.content, str):
+            output += chunk.content
+    return output
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Candilyzer\nAI Candidate Analyzer using Agno and Nebius AI")
+    with gr.Tab("Multi-Candidate Analyzer"):
+        github_usernames = gr.Textbox(label="GitHub Usernames (one per line)", lines=4, placeholder="username1\nusername2\n...")
+        job_role = gr.Textbox(label="Job Role", placeholder="Backend Engineer")
+        nebius_api_key = gr.Textbox(label="Nebius API Key", type="password")
+        model_id = gr.Textbox(label="Model ID", placeholder="meta-llama/Llama-3-70B-Instruct")
+        github_api_key = gr.Textbox(label="GitHub API Key", type="password")
+        exa_api_key = gr.Textbox(label="Exa API Key", type="password")
+        multi_button = gr.Button("Analyze Candidates")
+        multi_output = gr.Markdown()
+        multi_button.click(analyze_multi, inputs=[github_usernames, job_role, nebius_api_key, model_id, github_api_key, exa_api_key], outputs=multi_output)
+
+    with gr.Tab("Single Candidate Analyzer"):
+        github_username = gr.Textbox(label="GitHub Username", placeholder="username")
+        linkedin_url = gr.Textbox(label="LinkedIn Profile (optional)", placeholder="https://linkedin.com/in/...")
+        job_role2 = gr.Textbox(label="Job Role", placeholder="ML Engineer")
+        nebius_api_key2 = gr.Textbox(label="Nebius API Key", type="password")
+        model_id2 = gr.Textbox(label="Model ID", placeholder="meta-llama/Llama-3-70B-Instruct")
+        github_api_key2 = gr.Textbox(label="GitHub API Key", type="password")
+        exa_api_key2 = gr.Textbox(label="Exa API Key", type="password")
+        single_button = gr.Button("Analyze Candidate")
+        single_output = gr.Markdown()
+        single_button.click(analyze_single, inputs=[github_username, linkedin_url, job_role2, nebius_api_key2, model_id2, github_api_key2, exa_api_key2], outputs=single_output)
+
+    demo.launch()