Update app.py

app.py

@@ -4,7 +4,7 @@ import csv
 import pandas as pd
 from analyzer import combine_repo_files_for_llm, analyze_combined_file, parse_llm_json_response
 from hf_utils import download_space_repo, search_top_spaces
-
+from chatbot_page import chat_with_user, extract_keywords_from_conversation
 # Import chatbot logic
 from analyzer import analyze_code
 
@@ -146,35 +146,8 @@ df_output = gr.Dataframe(headers=["repo id", "strength", "weaknesses", "speciali
     datatype=["str", "str", "str", "str", "str", "str"]
 )
 
-def chat_with_user(user_message, history):
-    from openai import OpenAI
-    client = OpenAI()
-    messages = [
-        {"role": "system", "content": CHATBOT_SYSTEM_PROMPT}
-    ]
-    for msg in history:
-        messages.append({"role": "user", "content": msg[0]})
-        if msg[1]:
-            messages.append({"role": "assistant", "content": msg[1]})
-    messages.append({"role": "user", "content": user_message})
-    response = client.chat.completions.create(
-        model="gpt-4o-mini",
-        messages=messages,
-        max_tokens=256,
-        temperature=0.7
-    )
-    assistant_reply = response.choices[0].message.content
-    return assistant_reply
-
-def extract_keywords_from_conversation(history):
-    conversation = "\n".join([f"User: {msg[0]}\nAssistant: {msg[1]}" for msg in history if msg[1]])
-    prompt = (
-        "Given the following conversation between a user and an assistant about finding an ideal open-source repo, "
-        "extract about 5 keywords that would be most useful for searching Hugging Face repos to find the most relevant results for the user. "
-        "Return only the keywords as a comma-separated list.\n\nConversation:\n" + conversation
-    )
-    keywords = analyze_code(prompt)
-    return keywords
+
+
 
 def use_keywords_to_search_and_update_csv(keywords):
     global last_repo_ids, current_repo_idx