Create app.py

app.py

@@ -0,0 +1,107 @@
+import gradio as gr
+import os
+import fitz  # PyMuPDF
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_groq import ChatGroq
+from langchain_core.prompts import ChatPromptTemplate
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    document = fitz.open(pdf_path)
+    text = ""
+    for page_num in range(len(document)):
+        page = document.load_page(page_num)
+        text += page.get_text("text")
+    return text
+
+# Function to split text into chunks
+def split_text_into_chunks(text, chunk_size=1000, chunk_overlap=200):
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=chunk_overlap,
+        length_function=len,
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+# Function to create vector store
+def create_vector_store(chunks):
+    embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
+    vector_store = FAISS.from_texts(chunks, embeddings)
+    return vector_store
+
+# Function to perform RAG
+def perform_rag(vector_store, query):
+    retriever = vector_store.as_retriever()
+    docs = retriever.get_relevant_documents(query)
+    context = " ".join([doc.page_content for doc in docs])
+    return context
+
+# Function to parse output
+def parse_output(output):
+    # Directly access the content of the AIMessage object
+    output_content = output.content
+    start_index = output_content.find('content="') + len('content="')
+    end_index = output_content.rfind('"')
+    parsed_content = output_content[start_index:end_index]
+    parsed_content = parsed_content.replace('\\n', '\n')
+    return parsed_content
+
+# Create Gradio interface function with topic as input
+def generate_cover_letter(pdf_path, job_role, company_name, company_context, groq_api_key):
+    # Set the API key as an environment variable
+    os.environ["GROQ_API_KEY"] = groq_api_key
+
+    # Extract text from PDF
+    text = extract_text_from_pdf(pdf_path)
+    # Split text into chunks
+    chunks = split_text_into_chunks(text)
+    # Create vector store
+    vector_store = create_vector_store(chunks)
+    # Perform RAG
+    candidate_profile = perform_rag(vector_store, job_role)
+
+    # Load the Groq model
+    chat = ChatGroq(
+        temperature=0.5,
+        model="llama3-70b-8192", #define model available on Groq
+        api_key=os.getenv("GROQ_API_KEY")
+    )
+
+    # Define the prompt
+    system = "You are expert career coach and consultant. You will generate well made and proper cover letetr based on user profile and user input."
+    human = """
+    So, I am applying for {job_role} at {company_name}
+    =================
+    {company_context}
+    =================
+    {candidate_profile}
+    =================
+    From the company profile and my profile, please create a cover letter for the {job_role} position. Ensure that it is well-crafted and engaging for recruiters and hiring managers. Also, verify that my recent work experience and academic background align with the role I am applying for.
+    """
+    prompt = ChatPromptTemplate.from_messages([("system", system), ("human", human)])
+
+    # Define LangChain
+    chain = prompt | chat
+    output = chain.invoke({"job_role": job_role, "company_name": company_name, "company_context": company_context, "candidate_profile": candidate_profile})
+    # Parse output
+    parsed_output = parse_output(output)
+    
+    return parsed_output
+
+# Create Gradio interface
+gr.Interface(
+    fn=generate_cover_letter,
+    inputs=[
+        gr.File(label="Upload ATS Resume (PDF)", file_types=[".pdf"]),
+        gr.Textbox(label="Job Role", placeholder="Ex: Data Scientist, Fullstack Developer, etc."),
+        gr.Textbox(label="Company Name", placeholder="Enter a company name you applying"),
+        gr.Textbox(label="Company Context", placeholder="Enter a brief description of the company"),
+        gr.Textbox(label="Groq API Key", type="password", placeholder="Enter your Groq API Key")
+    ],
+    outputs=gr.Textbox(label="Generated Cover Letter", show_copy_button=True),
+    title="Cover Letter Generator",
+    description="Generate a cover letter based on your job role, company, context, and profile."
+).launch()