Create app.py

app.py

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+import logging
+import os
+from pathlib import Path
+from dotenv import load_dotenv
+from crewai import Crew
+from langchain_groq import ChatGroq
+import gradio as gr
+from resume import extract_text_from_file
+from crew import FormFillingAgents, FormFillingTasks
+
+
+def build_gradio_app():
+    """Builds the Gradio interface for the form-filling app."""
+    logging.basicConfig(level=logging.INFO)
+
+    # Define input elements
+    resume_input = gr.File(label="Upload Resume", file_types=[".pdf", ".docx", ".txt"])
+    job_desc_input = gr.Textbox(label="Job Description", placeholder="Enter job description here")
+    questions_input = gr.Textbox(label="Questions", placeholder="Enter questions, separated by commas")
+    api_key_input = gr.Textbox(label="GROQ API Key", placeholder="Enter your GROQ API Key")
+    
+    # Define output elements
+    answers = gr.Textbox(label="Tailored Answer to the Question Based on Your Resume", interactive=False)
+
+    # Processing function
+    def process_inputs(api_key, resume_input, job_desc, questions):
+        try:
+            # Debugging
+            logging.info("Received API Key, Resume, Job Description, and Questions.")
+            logging.info(f"API Key: {api_key}")
+            # Save API key to .env file it the user has session active 
+            if api_key:
+                env_path = Path(__file__).parent / ".env"
+                with open(env_path, "w") as env_file:
+                    env_file.write(f"GROQ_API_KEY={api_key}")
+                logging.info("API Key saved to .env file.")
+            else:
+                logging.warning("No API Key provided.")
+
+            load_dotenv()
+            
+            # Initialize language model
+            llm = ChatGroq(
+                model="groq/llama-3.1-8b-instant",
+                api_key=os.getenv("GROQ_API_KEY"),
+            )
+            logging.info("Language model initialized successfully.")
+
+            # Extract text from resume
+            resume_text = extract_text_from_file(resume_input)
+            logging.info("Resume text extracted.")
+
+            # Initialize agents and tasks
+            agents = FormFillingAgents()
+            analysis_agent = agents.resume_analysis_agent(llm)
+            qa_agent = agents.question_answering_agent(llm)
+            tasks = FormFillingTasks()
+            profile_task = tasks.profile_analysis_task(analysis_agent, resume_text, job_desc)
+            qa_task = tasks.question_answering_task(qa_agent, questions)
+
+            # Run Crew pipeline
+            crew = Crew(
+                agents=[analysis_agent, qa_agent],
+                tasks=[profile_task, qa_task],
+                verbose=True,
+                max_rpm=29,
+            )
+            results = crew.kickoff()
+            logging.info("Pipeline executed successfully.")
+            return str(results)
+
+        except Exception as e:
+            logging.error(f"Error during processing: {e}")
+            return f"Error during processing: {str(e)}"
+    # Gradio interface
+    interface = gr.Interface(
+        fn=process_inputs,
+        inputs=[api_key_input, resume_input, job_desc_input, questions_input],
+        outputs=[answers],
+        title="Form Filling Assistant",
+        description="Upload a resume, provide a job description, input API key, and ask questions to get tailored responses.",
+    )
+    interface.launch()
+
+if __name__ == '__main__':
+    build_gradio_app()