Update app.py

app.py

@@ -1,64 +1,71 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+from owl.agents import Agent
+from owl.toolkits import RedditToolkit
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Job Search Agent
+class JobSearchAgent(Agent):
+    def __init__(self):
+        super().__init__(
+            role="Job Search Agent",
+            goal="Find job postings",
+            tools=[RedditToolkit()],
+            llm="gpt-4"  # Replace with actual LLM or mock for demo
+        )
+   
+    def search_jobs(self, keywords, subreddit="jobs"):
+        results = self.tools.reddit.search(subreddit, keywords)
+        return [post.title for post in results[:5]]  # Limit to 5 results
 
+# Resume Tailoring Agent
+class ResumeTailoringAgent(Agent):
+    def __init__(self):
+        super().__init__(
+            role="Resume Tailoring Agent",
+            goal="Suggest resume keywords",
+            tools=[],
+            llm="gpt-4"  # Replace with actual LLM or mock for demo
+        )
+   
+    def suggest_keywords(self, job_description, resume):
+        job_words = set(job_description.lower().split())
+        resume_words = set(resume.lower().split())
+        missing = job_words - resume_words
+        return list(missing)[:10]  # Limit to 10 suggestions
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize agents
+job_search_agent = JobSearchAgent()
+resume_tailoring_agent = ResumeTailoringAgent()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Gradio functions
+def search_jobs(keywords):
+    try:
+        results = job_search_agent.search_jobs(keywords)
+        return "\n".join(results) if results else "No jobs found."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-    messages.append({"role": "user", "content": message})
+def tailor_resume(job_desc, resume):
+    try:
+        suggestions = resume_tailoring_agent.suggest_keywords(job_desc, resume)
+        return "Add these keywords: " + ", ".join(suggestions) if suggestions else "Resume matches job description."
+    except Exception as e:
+        return f"Error: {str(e)}"
 
-    response = ""
+# Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Autonomous Job Search and Prep Companion")
+   
+    with gr.Tab("Job Search"):
+        keywords = gr.Textbox(label="Enter keywords")
+        search_btn = gr.Button("Search")
+        job_output = gr.Textbox(label="Job Postings", lines=5)
+        search_btn.click(search_jobs, inputs=keywords, outputs=job_output)
+   
+    with gr.Tab("Resume Tailoring"):
+        job_desc = gr.Textbox(label="Job Description", lines=3)
+        resume = gr.Textbox(label="Your Resume", lines=3)
+        tailor_btn = gr.Button("Get Suggestions")
+        suggestions = gr.Textbox(label="Suggestions")
+        tailor_btn.click(tailor_resume, inputs=[job_desc, resume], outputs=suggestions)
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()