Update backend/analysis.py

backend/analysis.py

@@ -1,51 +1,51 @@
-import os
-from dotenv import load_dotenv
-from langchain_groq import ChatGroq
-from langchain_core.prompts import PromptTemplate
-
-# Set up Groq API key
-load_dotenv()  # Load environment variables from .env file
-os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")  # Set the Groq API key from environment variables
-
-# Initialize the ChatGroq model with the specified model name
-llm = ChatGroq(model_name="mixtral-8x7b-32768")
-
-def analyze_resume(full_resume, job_description):
-    # Template for analyzing the resume against the job description
-    template = """
-    You are an AI assistant specialized in resume analysis and recruitment. Analyze the given resume and compare it with the job description. 
-    
-    Example Response Structure:
-    
-    **OVERVIEW**:
-    - **Match Percentage**: [Calculate overall match percentage between the resume and job description]
-    - **Matched Skills**: [List the skills in job description that match the resume]
-    - **Unmatched Skills**: [List the skills in the job description that are missing in the resume]
-
-    **DETAILED ANALYSIS**:
-    Provide a detailed analysis about:
-    1. Overall match percentage between the resume and job description
-    2. List of skills from the job description that match the resume
-    3. List of skills from the job description that are missing in the resume
-    
-    **Additional Comments**:
-    Additional comments about the resume and suggestions for the recruiter or HR manager.
-
-    Resume: {resume}
-    Job Description: {job_description}
-
-    Analysis:
-    """
-    prompt = PromptTemplate(  # Create a prompt template with input variables
-        input_variables=["resume", "job_description"],
-        template=template
-    )
-
-    # Create a chain combining the prompt and the language model
-    chain = prompt | llm
-
-    # Invoke the chain with input data
-    response = chain.invoke({"resume": full_resume, "job_description": job_description})
-
-    # Return the content of the response
+import os
+from dotenv import load_dotenv
+from langchain_groq import ChatGroq
+from langchain_core.prompts import PromptTemplate
+
+# Set up Groq API key
+load_dotenv()  # Load environment variables from .env file
+os.environ["GROQ_API_KEY"] = os.getenv("GROQ_API_KEY")  # Set the Groq API key from environment variables
+
+# Initialize the ChatGroq model with the specified model name
+llm = ChatGroq(model_name="mistral-saba-24b")
+
+def analyze_resume(full_resume, job_description):
+    # Template for analyzing the resume against the job description
+    template = """
+    You are an AI assistant specialized in resume analysis and recruitment. Analyze the given resume and compare it with the job description. 
+    
+    Example Response Structure:
+    
+    **OVERVIEW**:
+    - **Match Percentage**: [Calculate overall match percentage between the resume and job description]
+    - **Matched Skills**: [List the skills in job description that match the resume]
+    - **Unmatched Skills**: [List the skills in the job description that are missing in the resume]
+
+    **DETAILED ANALYSIS**:
+    Provide a detailed analysis about:
+    1. Overall match percentage between the resume and job description
+    2. List of skills from the job description that match the resume
+    3. List of skills from the job description that are missing in the resume
+    
+    **Additional Comments**:
+    Additional comments about the resume and suggestions for the recruiter or HR manager.
+
+    Resume: {resume}
+    Job Description: {job_description}
+
+    Analysis:
+    """
+    prompt = PromptTemplate(  # Create a prompt template with input variables
+        input_variables=["resume", "job_description"],
+        template=template
+    )
+
+    # Create a chain combining the prompt and the language model
+    chain = prompt | llm
+
+    # Invoke the chain with input data
+    response = chain.invoke({"resume": full_resume, "job_description": job_description})
+
+    # Return the content of the response
     return response.content