getting response from cohere based on the resume and jd provided

.gitignore

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+env

app.py

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+import streamlit as st
+from src.main import run_app
+
+# Run the application
+run_app()

requirements.txt

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+streamlit
+cohere
+python-dotenv

src/get_response.py

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+import cohere
+from dotenv import load_dotenv
+import os
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Initialize the Cohere client with the API key from the environment variable
+cohere_api_key = os.getenv('COHERE_API_KEY')
+cohere_client = cohere.Client(cohere_api_key)
+
+# Function to store the initial context (resume and job description)
+def store_context(resume_text, job_description):
+    context = f"Resume:\n{resume_text}\n\nJob Description:\n{job_description}\n\n"
+    return context
+
+
+# Function to answer questions based on the stored context and a new question
+def answer_questions(context, question):
+    prompt = (
+        f"{context}"
+        f"Question:\n"
+        f"{question}\n\n"
+        "Answer:"
+    )
+    response = cohere_client.generate(
+        model='command-xlarge-nightly',
+        prompt=prompt,
+        max_tokens=500  # Adjust max_tokens to a higher value as needed
+    )
+    return response.generations[0].text.strip()

src/main.py

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+import streamlit as st
+from src.get_response import store_context, answer_questions
+
+def initialize_session_state():
+    if "context" not in st.session_state:
+        st.session_state.context = None
+    if "qa_pairs" not in st.session_state:
+        st.session_state.qa_pairs = []
+
+def input_resume_and_job_description():
+    st.subheader("Input Resume and Job Description")
+    resume_input = st.text_area("Enter your resume text:", height=200)
+    job_description_input = st.text_area("Enter the job description text:", height=200)
+
+    if st.button("Submit Resume and Job Description"):
+        if resume_input.strip() and job_description_input.strip():
+            st.session_state.context = store_context(resume_input, job_description_input)
+            st.session_state.qa_pairs = []  # Clear previous Q&A pairs when new context is submitted
+            st.success("Context stored successfully. You can now ask questions.")
+            st.experimental_rerun()
+        else:
+            st.warning("Please enter both the resume and the job description text.")
+
+def input_question():
+    st.subheader("Ask Questions")
+    question_input = st.text_input("Enter a question based on the resume and job description:")
+    
+    if st.button("Answer Question"):
+        if st.session_state.context and question_input.strip():
+            answer = answer_questions(st.session_state.context, question_input)
+            st.session_state.qa_pairs.insert(0, (question_input, answer))  # Insert at the beginning to keep the latest at the top
+            st.experimental_rerun()
+        else:
+            st.warning("Please enter a question.")
+
+def display_qa_pairs():
+    if st.session_state.qa_pairs:
+        st.subheader("Questions and Answers:")
+        for i, (question, answer) in enumerate(st.session_state.qa_pairs):
+            st.write(f"**Q{i+1}:** {question}")
+            st.write(f"**A{i+1}:** {answer}")
+
+def clear_context_and_qa():
+    if st.button("Clear Context and Q&A"):
+        st.session_state.context = None
+        st.session_state.qa_pairs = []
+        st.success("Context and Q&A cleared. Please enter new resume and job description.")
+        st.experimental_rerun()
+
+def run_app():
+    st.title("Resume and Job Description Analyzer")
+    st.write("This app uses Cohere's NLP capabilities to answer questions based on your resume and job description.")
+
+    initialize_session_state()
+
+    if st.session_state.context is None:
+        input_resume_and_job_description()
+    else:
+        st.success("Context is already stored. You can ask questions based on it.")
+        input_question()
+        display_qa_pairs()
+        clear_context_and_qa()
+
+    st.write("Powered by Streamlit and Cohere")