formatted the response and added readme.md steps

README.md

@@ -1 +1,37 @@
 # genai-interview-assistant
+# Interview Assistant
+
+This application uses Cohere's NLP capabilities to simulate an interviewee responding to interview questions. Users can input their resume and a job description, and then ask questions to get detailed and structured answers based on the provided context.
+
+## Features
+
+- Input resume and job description
+- Ask questions based on the provided context
+- Get detailed and structured answers generated by Cohere
+- View all questions and answers in a scrollable format
+- Clear context and Q&A to start a new session
+- Follow the me on GitHub and LinkedIn
+
+## Setup
+
+1. Clone the repository:
+   ```bash
+   git clone https://github.com/rrrreddy/interview-assistant.git
+   cd interview-assistant
+    ```
+2. Install the required packages:
+```bash
+pip install -r requirements.txt
+```
+3. Create a .env file in the root directory with your Cohere API key:
+```
+COHERE_API_KEY=your-cohere-api-key
+```
+4. Run the application:
+
+```
+streamlit run app.py
+```
+
+
+

src/get_response.py

@@ -11,21 +11,40 @@ 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
+    return f"Resume:\n{resume_text}\n\nJob Description:\n{job_description}\n\n"
 
-
-# Function to answer questions based on the stored context and a new question
+# Enhanced function to answer questions based on the stored context and a new question
 def answer_questions(context, question):
     prompt = (
+        f"You are an interviewee being asked questions based on your resume and a job description. "
+        f"Provide a detailed and structured response to the following question.\n\n"
         f"{context}"
         f"Question:\n"
         f"{question}\n\n"
-        "Answer:"
+        "Answer as if you are the interviewee:"
     )
     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()
+    
+    # Format the response in a structured way
+    detailed_response = response.generations[0].text.strip()
+    formatted_response = format_response(detailed_response)
+    return formatted_response
+
+def format_response(detailed_response):
+    # Here we add basic formatting, such as headings and bold points
+    formatted_response = ""
+    lines = detailed_response.split('\n')
+    
+    for line in lines:
+        if line.strip().endswith(':'):
+            # Heading
+            formatted_response += f"### {line.strip()}\n"
+        elif line.strip():
+            # Regular bullet points
+            formatted_response += f"- **{line.strip()}**\n"
+    
+    return formatted_response

src/main.py

@@ -1,10 +1,6 @@
 import streamlit as st
 from src.get_response import store_context, answer_questions
 
-@st.cache_data
-def get_context(resume_text, job_description):
-    return store_context(resume_text, job_description)
-
 def initialize_session_state():
     if "context" not in st.session_state:
         st.session_state.context = None
@@ -18,7 +14,7 @@ def input_resume_and_job_description():
 
     if st.button("Submit Resume and Job Description"):
         if resume_input.strip() and job_description_input.strip():
-            st.session_state.context = get_context(resume_input, job_description_input)
+            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.rerun()
@@ -42,7 +38,7 @@ def display_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}")
+            st.markdown(answer, unsafe_allow_html=True)
 
 def clear_context_and_qa():
     if st.button("Clear Context and Q&A"):
@@ -51,9 +47,16 @@ def clear_context_and_qa():
         st.success("Context and Q&A cleared. Please enter new resume and job description.")
         st.rerun()
 
+def add_social_links():
+    st.sidebar.title("Follow Me")
+    st.sidebar.write("[GitHub](https://github.com/rrrreddy)")
+    st.sidebar.write("[LinkedIn](https://www.linkedin.com/in/raghu-konda/)")
+
 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.")
+    st.title("Interview Assistant")
+    st.write("This app uses Cohere's NLP capabilities to simulate an interviewee and answer questions based on your resume and job description.")
+
+    add_social_links()
 
     initialize_session_state()
 
@@ -65,4 +68,4 @@ def run_app():
         display_qa_pairs()
         clear_context_and_qa()
 
-    st.write("Designed by Raghu")
+    st.write("Designed by Raghu!")