Delete app.py

app.py

@@ -1,96 +0,0 @@
-import streamlit as st
-import pandas as pd
-import uuid
-import chromadb
-from langchain_groq import ChatGroq
-from langchain_community.document_loaders import WebBaseLoader
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-
-# Initialize LLM
-llm = ChatGroq(
-    temperature=0,
-    api_key="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",
-    model_name="llama-3.1-70b-versatile"
-)
-
-# Streamlit App
-st.title("Cold Email Generator with LangChain")
-
-# Load CSV
-uploaded_file = st.file_uploader("Upload your portfolio CSV file", type=["csv"])
-if uploaded_file:
-    df = pd.read_csv(uploaded_file)
-    st.write("Portfolio Data:")
-    st.dataframe(df)
-
-    # Initialize ChromaDB
-    client = chromadb.PersistentClient('vectorstore')
-    collection = client.get_or_create_collection(name="my_portfolio.csv")
-
-    if not collection.count():
-        for _, row in df.iterrows():
-            collection.add(documents=row["Techstack"],
-                           metadatas={"links": row["Links"]},
-                           ids=[str(uuid.uuid4())])
-    st.success("Portfolio data added to the vectorstore!")
-
-# Scrape Job Description
-url = st.text_input("Enter the job posting URL:")
-if url:
-    loader = WebBaseLoader(url)
-    page_data = loader.load().pop().page_content
-    st.write("Scraped Job Data:")
-    st.text(page_data)
-
-    # Extract Job Details
-    prompt_extract = PromptTemplate.from_template(
-        """
-        ### SCRAPED TEXT FROM WEBSITE:
-        {page_data}
-        ### INSTRUCTION:
-        The scraped text is from the career's page of a website.
-        Your job is to extract the job postings and return them in JSON format containing the 
-        following keys: `role`, `experience`, `skills` and `description`.
-        Only return the valid JSON.
-        ### VALID JSON (NO PREAMBLE):    
-        """
-    )
-
-    chain_extract = prompt_extract | llm
-    res = chain_extract.invoke(input={'page_data': page_data})
-    json_parser = JsonOutputParser()
-    job = json_parser.parse(res.content)
-    st.write("Extracted Job Details:")
-    st.json(job)
-
-    # Query Portfolio Links
-    links = collection.query(query_texts=job['skills'], n_results=2).get('metadatas', [])
-    st.write("Relevant Portfolio Links:")
-    st.write(links)
-
-    # Generate Cold Email
-    prompt_email = PromptTemplate.from_template(
-        """
-        ### JOB DESCRIPTION:
-        {job_description}
-
-        ### INSTRUCTION:
-        You are Mohan, a business development executive at AtliQ. AtliQ is an AI & Software Consulting company dedicated to facilitating
-        the seamless integration of business processes through automated tools. 
-        Over our experience, we have empowered numerous enterprises with tailored solutions, fostering scalability, 
-        process optimization, cost reduction, and heightened overall efficiency. 
-        Your job is to write a cold email to the client regarding the job mentioned above describing the capability of AtliQ 
-        in fulfilling their needs.
-        Also add the most relevant ones from the following links to showcase Atliq's portfolio: {link_list}
-        Remember you are Mohan, BDE at AtliQ. 
-        Do not provide a preamble.
-        ### EMAIL (NO PREAMBLE):
-
-        """
-    )
-
-    chain_email = prompt_email | llm
-    email_res = chain_email.invoke({"job_description": str(job), "link_list": links})
-    st.write("Generated Cold Email:")
-    st.text(email_res.content)