Upload app.py

app.py

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+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)