diff --git a/evo_prophet/functions/grade_info.py b/evo_prophet/functions/grade_info.py
deleted file mode 100644
index 717a3988..00000000
--- a/evo_prophet/functions/grade_info.py
+++ /dev/null
@@ -1,119 +0,0 @@
-from langchain_openai import ChatOpenAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema.output_parser import StrOutputParser
-from langchain.output_parsers import PydanticOutputParser
-# from langchain_core.pydantic_v1 import BaseModel
-
-# class InfoScores(BaseModel):
-#     credibility: int
-#     bias: int
-#     accuracy: int
-#     currency: int
-#     relevance: int
-#     significance: int
-    
-grading_planning_prompt_template = """
-You are a professional information evaluator. Your goal is to 
-evaluate research for making an informed prediction to answer the question: {question}.
-
-Limit yourself to the following criteria:
-
-* Credibility: Assess the source's credibility based on the author's credentials, logical arguments, and documentation backed by scholarly literature​​.
-* Bias: Examine if the source has a hidden agenda or dismisses other viewpoints​​.
-* Accuracy: Check for supporting documentation for presented facts and contradictions with other reliable sources​​.
-* Currency: Ensure the information is current and up-to-date, particularly important for rapidly evolving topics​​.
-* Relevance: The information should be relevant to the specific topic of interest​​.
-* Significance: Evaluate if the information is significant and not trivial or too general​
-
-Limit yourself to the criteria above. Do NOT add any new criteria or subcriteria.
-
-How would you approach grading the information for each of these criteria?
-Let's think about it step by step.
-"""
-
-
-grading_prompt_template = """
-You are a professional information evaluator. Your goal is to 
-evaluate research for making an informed prediction to answer the question: {question}.
-
-Here is the plan you came up with to evaluate the information:
-
-{plan}
-
-Limit yourself to the criteria in the plan. Do NOT add any new criteria or subcriteria.
-
-Now you will be given a piece of information to evaluate, according to the plan.
-Provide a single numeric score for each criteria, from 0 to 100, where 0 is the worst and 100 is the best.
-Provide a step by step justification for each score.
-
-The information's format or writing style should not affect your evaluation. Neither should the information's length.
-
-Information:
-
-{information}
-"""
-
-grading_format_prompt_template = """
-You will be given an evaluation of information you graded. It contains scores for different
-evaluation criteria. Extract the score for each criteria and return them in the following
-format:
-
-{{ "one_of_the_criteria": xx, ... }}
-
-Where xx is a number. Only respond with a single JSON. The keys should be lowercase and snake_case.
-
-{report}
-"""
-
-def grade_info(question: str, information: str) -> str:
-    """
-    The grading function grades information using criteria from https://guides.lib.unc.edu/evaluating-info/evaluate,
-    ignoring `usability` and `intended audience`.
-
-    Upon receiving a question like `"Will Twitter implement a new misinformation policy before the 2024 elections?"`
-    along with complimentary information, it will:
-
-    1. Create an information evaluation plan
-    2. Evaluate the information according to the plan
-    3. Extract the scores from the evaluation
-    """
-
-    grading_prompt = ChatPromptTemplate.from_template(template=grading_prompt_template)
-    planning_prompt = ChatPromptTemplate.from_template(template=grading_planning_prompt_template)
-    formatting_prompt = ChatPromptTemplate.from_template(template=grading_format_prompt_template)
-    
-    llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
-    
-    planning_chain = (
-        planning_prompt |
-        llm |
-        StrOutputParser()
-    )
-    
-    grading_chain = (
-        grading_prompt |
-        llm |
-        StrOutputParser()
-    )
-    
-    formatting_chain = (
-        formatting_prompt |
-        llm |
-        StrOutputParser()
-    )
-    
-    plan = planning_chain.invoke({
-        "question": question
-    })
-    
-    report = grading_chain.invoke({
-        "question": question,
-        "plan": plan,
-        "information": information
-    })
-    
-    grades: str = formatting_chain.invoke({
-        "report": report
-    })
-    
-    return grades
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