Update app.py

app.py

@@ -2,22 +2,27 @@ import warnings
 warnings.filterwarnings('ignore')
 
 import os
-os.system("pip install pymupdf")
+import requests
 import fitz  # PyMuPDF for PDF processing
-
 import docx  # python-docx for DOCX processing
 import gradio as gr
-import os
 from crewai import Agent, Task, Crew
 from crewai_tools import SerperDevTool
 
+# Retrieve the Gemini API key from environment variables
+gemini_key = os.getenv("geminiapikey")  # Ensure you have set the Gemini API key in your Hugging Face Space
 
-os.environ['OPENAI_API_KEY'] = os.getenv("openaikey")
+# Other API keys (assuming they are also set in the Hugging Face Space environment)
+os.environ["OPENAI_API_KEY"] = os.getenv("openaikey")
 os.environ["OPENAI_MODEL_NAME"] = 'gpt-4o-mini'
 os.environ["SERPER_API_KEY"] = os.getenv("serper_key")
 os.environ["GRADIO_SERVER_PORT"] = "7861"
+
+# Install necessary packages (ensure pymupdf and pydantic are installed)
 os.system('pip install pydantic --upgrade')
+os.system("pip install pymupdf")
 
+# Function to extract text from PDF
 def extract_text_from_pdf(file_path):
     """Extracts text from a PDF file using PyMuPDF."""
     doc = fitz.open(file_path)
@@ -26,6 +31,7 @@ def extract_text_from_pdf(file_path):
         text += page.get_text()
     return text
 
+# Function to extract text from DOCX
 def extract_text_from_docx(file_path):
     """Extracts text from a DOCX file using python-docx."""
     doc = docx.Document(file_path)
@@ -34,6 +40,7 @@ def extract_text_from_docx(file_path):
         fullText.append(para.text)
     return "\n".join(fullText)
 
+# Function to determine file type and extract text from the resume
 def extract_text_from_resume(file_path):
     """Determines file type and extracts text."""
     if file_path.endswith(".pdf"):
@@ -43,92 +50,94 @@ def extract_text_from_resume(file_path):
     else:
         return "Unsupported file format."
 
+# Function to call the Gemini API for processing
+def get_gemini_response(input_data):
+    """Calls the Gemini API with the provided input."""
+    url = "https://gemini.api.endpoint"  # Replace with the actual Gemini API endpoint
+    headers = {
+        "Authorization": f"Bearer {gemini_key}",
+        "Content-Type": "application/json"
+    }
+    payload = {
+        "input": input_data
+    }
+    
+    response = requests.post(url, json=payload, headers=headers)
+    
+    if response.status_code == 200:
+        return response.json()  # Return the Gemini API response
+    else:
+        return {"feedback": "Error fetching feedback from Gemini API."}
 
-
-# Agent 1: Resume Strategist
+# Resume feedback agent
 resume_feedback = Agent(
     role="Professional Resume Advisor",
     goal="Give feedback on the resume to make it stand out in the job market.",
     verbose=True,
     backstory="With a strategic mind and an eye for detail, you excel at providing feedback on resumes to highlight the most relevant skills and experiences."
-    )
-
+)
 
 # Task for Resume Strategist Agent: Align Resume with Job Requirements
 resume_feedback_task = Task(
-    description=(
-        """Give feedback on the resume to make it stand out for recruiters. 
-        Review every section, inlcuding the summary, work experience, skills, and education. Suggest to add relevant sections if they are missing.  
-        Also give an overall score to the resume out of 10.  This is the resume: {resume}"""
-    ),
+    description="""Give feedback on the resume to make it stand out for recruiters. Review every section, including the summary, work experience, skills, and education. Suggest to add relevant sections if they are missing. Also give an overall score to the resume out of 10.  This is the resume: {resume}""",
     expected_output="The overall score of the resume followed by the feedback in bullet points.",
     agent=resume_feedback
 )
 
-# Agent 2: Resume Strategist
+# Resume advisor agent
 resume_advisor = Agent(
     role="Professional Resume Writer",
-    goal="Based on the feedback recieved from Resume Advisor, make changes to the resume to make it stand out in the job market.",
+    goal="Based on the feedback received from Resume Advisor, make changes to the resume to make it stand out in the job market.",
     verbose=True,
     backstory="With a strategic mind and an eye for detail, you excel at refining resumes based on the feedback to highlight the most relevant skills and experiences."
 )
 
-# Task for Resume Strategist Agent: Align Resume with Job Requirements
+# Task for Resume Advisor Agent: Align Resume with Job Requirements
 resume_advisor_task = Task(
-    description=(
-        """Rewrite the resume based on the feedback to make it stand out for recruiters. You can adjust and enhance the resume but don't make up facts. 
-        Review and update every section, including the summary, work experience, skills, and education to better reflect the candidates abilities. This is the resume: {resume}"""
-    ),
-    expected_output= "Resume in markdown format that effectively highlights the candidate's qualifications and experiences",
-    # output_file="improved_resume.md",
+    description="""Rewrite the resume based on the feedback to make it stand out for recruiters. You can adjust and enhance the resume but don't make up facts. Review and update every section, including the summary, work experience, skills, and education to better reflect the candidate's abilities. This is the resume: {resume}""",
+    expected_output="Resume in markdown format that effectively highlights the candidate's qualifications and experiences",
     context=[resume_feedback_task],
     agent=resume_advisor
 )
 
+# Job researcher agent
 search_tool = SerperDevTool()
 
-
-# Agent 3: Researcher
 job_researcher = Agent(
-    role = "Senior Recruitment Consultant",
-    goal = "Find the 5 most relevant, recently posted jobs based on the improved resume recieved from resume advisor and the location preference",
-    tools = [search_tool],
-    verbose = True,
-    backstory = """As a senior recruitment consultant your prowess in finding the most relevant jobs based on the resume and location preference is unmatched. 
-    You can scan the resume efficiently, identify the most suitable job roles and search for the best suited recently posted open job positions at the preffered location."""
-    )
+    role="Senior Recruitment Consultant",
+    goal="Find the 5 most relevant, recently posted jobs based on the improved resume received from resume advisor and the location preference",
+    tools=[search_tool],
+    verbose=True,
+    backstory="As a senior recruitment consultant, your prowess in finding the most relevant jobs based on the resume and location preference is unmatched."
+)
 
+# Task for Job Researcher Agent
 research_task = Task(
-    description = """Find the 5 most relevant recent job postings based on the resume recieved from resume advisor and location preference. This is the preferred location: {location} . 
-    Use the tools to gather relevant content and shortlist the 5 most relevant, recent, job openings""",
-    expected_output=(
-        "A bullet point list of the 5 job openings, with the appropriate links and detailed description about each job, in markdown format" 
-    ),
-#    output_file="relevant_jobs.md",
+    description="""Find the 5 most relevant recent job postings based on the resume received from resume advisor and location preference. This is the preferred location: {location}. Use the tools to gather relevant content and shortlist the 5 most relevant, recent job openings""",
+    expected_output="A bullet point list of the 5 job openings, with the appropriate links and detailed description about each job, in markdown format",
     agent=job_researcher
 )
 
-
+# Creating the Crew
 crew = Crew(
     agents=[resume_feedback, resume_advisor, job_researcher],
     tasks=[resume_feedback_task, resume_advisor_task, research_task],
     verbose=True
 )
 
-
-
+# Function to run the entire agent pipeline
 def resume_agent(file_path, location):
     resume_text = extract_text_from_resume(file_path)
 
-    result = crew.kickoff(inputs={"resume": resume_text, "location": location})
-
-        # Extract outputs
-    feedback = resume_feedback_task.output.raw.strip("```markdown").strip("```").strip()
-    improved_resume = resume_advisor_task.output.raw.strip("```markdown").strip("```").strip()
-    job_roles = research_task.output.raw.strip("```markdown").strip("```").strip()
+    # Call the Gemini API to process the resume or provide feedback
+    gemini_response = get_gemini_response(resume_text)
+    
+    # Extract feedback, improved resume, and job roles from the Gemini API response
+    feedback = gemini_response.get("feedback", "No feedback from Gemini API.")
+    improved_resume = gemini_response.get("improved_resume", "No improvements from Gemini API.")
+    job_roles = gemini_response.get("job_roles", "No job roles from Gemini API.")
 
     return feedback, improved_resume, job_roles
-        
 
 # Gradio Interface
 with gr.Blocks() as demo:
@@ -148,14 +157,11 @@ with gr.Blocks() as demo:
 
     # Define the click event for the submit button
     def format_outputs(feedback, improved_resume, job_roles):
-        # Add bold headings to each section
-        feedback_with_heading = f"## Resume Feedback:**\n\n{feedback}"
+        feedback_with_heading = f"## Resume Feedback:\n\n{feedback}"
         improved_resume_with_heading = f"## Improved Resume:\n\n{improved_resume}"
         job_roles_with_heading = f"## Relevant Job Roles:\n\n{job_roles}"
         return feedback_with_heading, improved_resume_with_heading, job_roles_with_heading
 
-
-
     submit_button.click(
         lambda: gr.update(value="Processing..."),
         inputs=[],
@@ -174,5 +180,6 @@ with gr.Blocks() as demo:
         outputs=submit_button
     )
 
+# Launch the Gradio app
 demo.queue()
 demo.launch()