Update app.py

app.py

@@ -6,14 +6,6 @@ from Data import markdown_to_csv
 from langchain_core.prompts import ChatPromptTemplate
 from langchain_openai import ChatOpenAI
 
-# Load OpenRouter API Key
-openrouter_api_key = "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"
-
-model = ChatOpenAI(
-    openai_api_key=openrouter_api_key,
-    model="meta-llama/llama-4-maverick:free",
-    base_url="https://openrouter.ai/api/v1"
-)
 
 st.title("AI Web Scraper")
 
@@ -72,25 +64,3 @@ if "all_dom_content" in st.session_state:
             else:
                 st.write("No tables to merge.")
 
-def merge_tables_with_llm(tables, parse_description):
-    """Merges a list of Pandas DataFrames into a single Markdown table using LLM."""
-    from langchain_core.prompts import ChatPromptTemplate
-    from langchain_openai import ChatOpenAI
-    # Convert DataFrames to Markdown strings
-    table_strings = [table.to_markdown(index=False) for table in tables]
-
-    # Create a prompt for the LLM
-    merge_prompt = (
-        "You are tasked with merging the following Markdown tables into a single, comprehensive Markdown table.\n"
-        "The tables contain information related to: {parse_description}.\n"
-        "Combine the tables, ensuring that the merged table is well-formatted and contains all relevant information.\n"
-        "If there are duplicate columns, rename them to be unique. If there are missing values, fill them with 'N/A'.\n"
-        "Ensure the final output is a single valid Markdown table.\n\n"
-        "Here are the tables:\n\n" + "\n\n".join(table_strings) +
-        "\n\nReturn the merged table in Markdown format:"
-    )
-
-    # Invoke the LLM
-    response = model.invoke({"dom_content": "", "parse_description": merge_prompt})
-    return response.content
-