Upload email_generator_using_groq_and_langchain.py

email_generator_using_groq_and_langchain.py

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+# -*- coding: utf-8 -*-
+"""Email_Generator_Using_Groq_and_Langchain.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1Ev-35eeTqANlgNH1LFEo6AovAOKQk7oM
+"""
+
+!pip install langchain-groq langchain_community -q
+
+!pip install chromadb -q
+
+!pip install streamlit -q
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%writefile app.py
+# 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
+# 
+# st.title("Cold Email Generator")
+# 
+# url = st.text_input("Enter Job Posting URL")
+# submit_button = st.button("Generate Email")
+# 
+# if submit_button and url:
+#     llm = ChatGroq(temperature=0, model_name="llama-3.3-70b-versatile", groq_api_key="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")
+#     loader = WebBaseLoader(url)
+#     data = loader.load().pop().page_content
+#     #st.success("Job posting loaded!")
+# 
+#     prompt_extract = PromptTemplate.from_template(
+#         """
+#         ### SCRAPED TEXT FROM WEBSITE:
+#         {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({"data": data})
+#     json_parser = JsonOutputParser()
+#     json_res = json_parser.parse(res.content)
+#     #st.success("Job details extracted!")
+#     df=pd.read_csv("/content/my_portfolio.csv")
+#     client = chromadb.PersistentClient('vectorstore')
+#     collection = client.get_or_create_collection(name="portfolio")
+#     if not collection.count():
+#       for _, row in df.iterrows():
+#         collection.add(documents=row["Techstack"],
+#                        metadatas={"links": row["Links"]},
+#                        ids=[str(uuid.uuid4())])
+#     job=json_res
+#     links = collection.query(query_texts=job['skills'], n_results=2).get('metadatas', [])
+# 
+# 
+#     prompt_email = PromptTemplate.from_template(
+#              """
+#          ### JOB DESCRIPTION:
+#     {job_description}
+#   Your job is to write a cold email to the client regarding the job mentioned above describing your capability
+#   to fulfill their needs.
+#   Also add the most relevant ones from the following links to showcase portfolio: {link_list}
+#   Remember you are Sidra, ML Engineer at XYZ company.
+#   Avoid generic introductions—focus on **value, relevance, and engagement**.
+#   ### EMAIL (NO PREAMBLE):
+#     """
+#     )
+#     chain_email = prompt_email | llm
+#     res = chain_email.invoke({"job_description": str(job), "link_list": links})
+#     st.text_area("Generated Email", res.content, height=300)
+# 
+#     # Sidebar with instructions
+# st.sidebar.markdown("## Guide")
+# st.sidebar.info(
+#     "It allows users to input the URL of a company's careers page. "
+#     "The tool then extracts job listings from that page and generates personalized cold emails. "
+#     "These emails include relevant portfolio links sourced from a vector database, based on the specific job descriptions."
+# )
+# 
+#
+
+!npm install -g localtunnel
+
+# Your public ip is the password to the localtunnel
+!curl ipv4.icanhazip.com
+
+!streamlit run app.py &>./logs.txt & npx localtunnel --port 8501