Create app.py

app.py

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+import streamlit as st
+from utils.cv_processor import CVProcessor
+from utils.rag_agent import RAGInterviewAgent
+from utils.evaluator import evaluate_answers
+from utils.report_generator import generate_report
+import os
+
+def main():
+    st.set_page_config(page_title="RAG Interview Agent", layout="wide")
+    st.title("🧠 RAG-Powered Interview Agent")
+    
+    # Initialize session state
+    if 'stage' not in st.session_state:
+        st.session_state.stage = "upload"
+    if 'answers' not in st.session_state:
+        st.session_state.answers = []
+    
+    # CV Upload and Processing
+    if st.session_state.stage == "upload":
+        with st.form("candidate_info"):
+            job_role = st.selectbox("Select job role:", 
+                                  ["Software Engineer", "Data Scientist"])
+            cv_file = st.file_uploader("Upload CV (PDF/DOCX)", 
+                                     type=["pdf", "docx"])
+            if st.form_submit_button("Submit"):
+                if cv_file:
+                    cv_path = f"data/temp_cv.{'pdf' if cv_file.type == 'application/pdf' else 'docx'}"
+                    with open(cv_path, "wb") as f:
+                        f.write(cv_file.getbuffer())
+                    
+                    processor = CVProcessor()
+                    evaluation = processor.evaluate(cv_path, job_role)
+                    
+                    if evaluation["is_qualified"]:
+                        st.session_state.cv_summary = evaluation["cv_summary"]
+                        st.session_state.job_role = job_role
+                        st.session_state.stage = "interview"
+                        st.session_state.agent = RAGInterviewAgent(job_role, evaluation["cv_summary"])
+                        st.rerun()
+                    else:
+                        st.error("CV doesn't meet requirements")
+
+    # Interview Process
+    elif st.session_state.stage == "interview":
+        agent = st.session_state.agent
+        question = agent.get_current_question()
+        
+        st.subheader(f"Question {agent.current_q + 1}/{len(agent.questions)}")
+        st.write(question["text"])
+        
+        answer = st.text_area("Your answer:")
+        
+        if st.button("Submit Answer"):
+            evaluation = agent.evaluate_answer(answer)
+            st.session_state.answers.append({
+                "question": question,
+                "answer": answer,
+                "evaluation": evaluation
+            })
+            
+            if agent.current_q < len(agent.questions) - 1:
+                agent.next_question()
+            else:
+                st.session_state.stage = "results"
+                st.rerun()
+    
+    # Results Display
+    elif st.session_state.stage == "results":
+        final_eval = evaluate_answers(st.session_state.answers)
+        st.success("Interview Complete!")
+        st.subheader(f"Overall Score: {final_eval['score']}/10")
+        
+        report_path = generate_report(
+            st.session_state.job_role,
+            st.session_state.cv_summary,
+            st.session_state.answers,
+            final_eval
+        )
+        
+        with open(report_path, "rb") as f:
+            st.download_button("Download Report", f, "interview_report.pdf")
+
+if __name__ == "__main__":
+    main()