changed to groq

app.py

@@ -15,6 +15,7 @@ import queue
 from langchain_openai import ChatOpenAI
 from typing import List, Dict, Optional
 from pydantic import BaseModel, Field, confloat
+from langchain_groq import ChatGroq  # Import Groq LLM
 
 class SkillScore(BaseModel):
     skill_name: str = Field(description="Name of the skill being scored")
@@ -231,6 +232,8 @@ class CompanyResearch(BaseModel):
     interview_questions: List[str] = Field(
         description="Strategic questions to ask during the interview"
     )
+llm = ChatGroq(model="groq/llama3-70b-8192", groq_api_key=groq_api_key)
+
 resume_analyzer = Agent(
                         role= "Resume Optimization Expert",
                             goal= "Analyze resumes and provide structured optimization suggestions",
@@ -240,7 +243,7 @@ resume_analyzer = Agent(
                                 providing actionable suggestions for improvement. Your recommendations always
                                 focus on both human readability and ATS compatibility.""",
             verbose=True,
-            # llm=llm,
+            llm=llm,
             # knowledge_sources=[pdf_source],
         )
 job_analyzer = Agent(
@@ -253,7 +256,7 @@ job_analyzer = Agent(
                 and soft skills requirements, and can evaluate experience levels accurately.""",
             verbose=True,
             tools=[ScrapeWebsiteTool()],
-            # llm=LLM(llm),
+            llm=llm,
             # knowledge_sources=[pdf_source],
 )
 company_researcher = Agent(
@@ -266,7 +269,7 @@ company_researcher = Agent(
                 candidates for interviews. """,
             tools=[SerperDevTool()],
             verbose=True,
-            # llm=LLM(llm),
+            llm=llm,
             # knowledge_sources=[pdf_source],
 
         )
@@ -279,7 +282,7 @@ resume_writer = Agent (
                     beautifully formatted, ATS-friendly documents that maintain professionalism
                     while showcasing candidate strengths effectively.""",
                 verbose=True,
-            # llm=LLM(llm),
+                llm=llm,
         )
 report_generator = Agent(
                 role= "Career Report Generator and Markdown Specialist",
@@ -291,7 +294,7 @@ report_generator = Agent(
                     into clear, actionable insights with proper markdown formatting, emojis, and
                     visual elements that make information both appealing and easily digestible.""",
                 verbose=True,
-            # llm=LLM(llm),
+                llm=llm,
         )
 
 analyze_job_task = Task(