updated with api key

chains.py

@@ -1,98 +1,98 @@
-import os
-from langchain_groq import ChatGroq
-from langchain_core.prompts import PromptTemplate
-from langchain_core.output_parsers import JsonOutputParser
-from langchain_core.exceptions import OutputParserException
-from dotenv import load_dotenv
-
-import streamlit as st
-
-GROQ_API_KEY = st.secrets["default"]["GROQ_API_KEY"]
-
-# using this we can have a file called .env in your root folder where you can keep your API key. 
-# load_dotenv()   # This will find the .env file and it will set the things in that file as your environment variable
-
-# print(os.getenv("GROQ_API_KEY"))        # just for testing
-
-class Chain:
-    def __init__(self):
-        self.llm = ChatGroq(temperature=0, groq_api_key=GROQ_API_KEY, model_name="llama-3.1-70b-versatile")
-        # self.llm = ChatGroq(temperature=0, groq_api_key=os.getenv("GROQ_API_KEY"), model_name="llama-3.1-70b-versatile")
-        
-
-    # function for extracting the job description and then passing it to a json parser to convert it to json
-    def extract_jobs(self, cleaned_text):
-        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 | self.llm
-        res = chain_extract.invoke(input={"page_data": cleaned_text})
-        try:
-            json_parser = JsonOutputParser()
-            res = json_parser.parse(res.content)
-            # Check if the result is a list and extract the first dictionary
-            # if isinstance(json_res, list):
-            #     json_res = json_res[0]
-            
-        except OutputParserException:
-            raise OutputParserException("Context too big. Unable to parse jobs.")
-        return res if isinstance(res, list) else [res]
-
-    
-    def summarize_pdf(self, pdf_data):
-        prompt_extract = PromptTemplate.from_template(
-        """
-        ### PDF DATA OBTAINED FROM RESUME:
-        {pdf_data}
-        ### INSTRUCTION:
-        The data is from the resume of a person.
-        Your job is to extract all the details of this person and summarize it in 200 words, which includes name, education, experience, projects, skills.
-        ### (NO PREAMBLE):    
-        """
-        )
-        chain_extract = prompt_extract | self.llm    # this will form a langchain chain ie you are getting a prompt and passing it to LLM 
-        res2 = chain_extract.invoke(input={'pdf_data':pdf_data})
-        # print(res.content)
-        summary = res2.content
-        return summary
-        
-    def write_mail(self, job_description, summary):
-        prompt_email = PromptTemplate.from_template(
-        """
-        ### JOB DESCRIPTION:
-        This is a job description
-        
-        {job_description}
-
-        ### INSTRUCTION:
-        These are the person's details.
-        {summary}
-        Consider yourself as this person. 
-        
-        Introduce yourself in an engaging way from above with your name from the above details and your current designation.  
-        
-        Try to find some things in the job description which are similar with your details. Mention those things which are similar. 
-        Do not mention anything which is not present in the details.
-        
-        Your job is to write a cold email of about 250 words to the hiring manager regarding the job mentioned above describing the capability of you 
-        in fulfilling their needs. The cold email must be engaging to read. 
-        End the email with Name and Current place where your are working or studying. 
-        Do not provide a preamble.
-        ### EMAIL (NO PREAMBLE):
-
-        """
-        )
-        chain_email = prompt_email | self.llm
-        res = chain_email.invoke({"job_description": str(job_description), "summary": summary})
-        return res.content
-
-# if __name__ == "__main__":
+import os
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import JsonOutputParser
+from langchain_core.exceptions import OutputParserException
+from dotenv import load_dotenv
+
+import streamlit as st
+
+# GROQ_API_KEY = st.secrets["default"]["GROQ_API_KEY"]
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
+# using this we can have a file called .env in your root folder where you can keep your API key. 
+# load_dotenv()   # This will find the .env file and it will set the things in that file as your environment variable
+
+# print(os.getenv("GROQ_API_KEY"))        # just for testing
+
+class Chain:
+    def __init__(self):
+        self.llm = ChatGroq(temperature=0, groq_api_key=GROQ_API_KEY, model_name="llama-3.1-70b-versatile")
+        # self.llm = ChatGroq(temperature=0, groq_api_key=os.getenv("GROQ_API_KEY"), model_name="llama-3.1-70b-versatile")
+        
+
+    # function for extracting the job description and then passing it to a json parser to convert it to json
+    def extract_jobs(self, cleaned_text):
+        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 | self.llm
+        res = chain_extract.invoke(input={"page_data": cleaned_text})
+        try:
+            json_parser = JsonOutputParser()
+            res = json_parser.parse(res.content)
+            # Check if the result is a list and extract the first dictionary
+            # if isinstance(json_res, list):
+            #     json_res = json_res[0]
+            
+        except OutputParserException:
+            raise OutputParserException("Context too big. Unable to parse jobs.")
+        return res if isinstance(res, list) else [res]
+
+    
+    def summarize_pdf(self, pdf_data):
+        prompt_extract = PromptTemplate.from_template(
+        """
+        ### PDF DATA OBTAINED FROM RESUME:
+        {pdf_data}
+        ### INSTRUCTION:
+        The data is from the resume of a person.
+        Your job is to extract all the details of this person and summarize it in 200 words, which includes name, education, experience, projects, skills.
+        ### (NO PREAMBLE):    
+        """
+        )
+        chain_extract = prompt_extract | self.llm    # this will form a langchain chain ie you are getting a prompt and passing it to LLM 
+        res2 = chain_extract.invoke(input={'pdf_data':pdf_data})
+        # print(res.content)
+        summary = res2.content
+        return summary
+        
+    def write_mail(self, job_description, summary):
+        prompt_email = PromptTemplate.from_template(
+        """
+        ### JOB DESCRIPTION:
+        This is a job description
+        
+        {job_description}
+
+        ### INSTRUCTION:
+        These are the person's details.
+        {summary}
+        Consider yourself as this person. 
+        
+        Introduce yourself in an engaging way from above with your name from the above details and your current designation.  
+        
+        Try to find some things in the job description which are similar with your details. Mention those things which are similar. 
+        Do not mention anything which is not present in the details.
+        
+        Your job is to write a cold email of about 250 words to the hiring manager regarding the job mentioned above describing the capability of you 
+        in fulfilling their needs. The cold email must be engaging to read. 
+        End the email with Name and Current place where your are working or studying. 
+        Do not provide a preamble.
+        ### EMAIL (NO PREAMBLE):
+
+        """
+        )
+        chain_email = prompt_email | self.llm
+        res = chain_email.invoke({"job_description": str(job_description), "summary": summary})
+        return res.content
+
+# if __name__ == "__main__":
 #     print(os.getenv("GROQ_API_KEY"))