app.py

import gradio as gr
import regex as re
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

# Import chatbot logic
from analyzer import analyze_code

# Chatbot system prompt
CHATBOT_SYSTEM_PROMPT = (
    "You are a helpful assistant. Your goal is to help the user describe their ideal open-source repo. "
    "Ask questions to clarify what they want, their use case, preferred language, features, etc. "
    "When the user clicks 'End Chat', analyze the conversation and return about 5 keywords for repo search. "
    "Return only the keywords as a comma-separated list."
)

def read_csv_as_text(csv_filename):
    return pd.read_csv(csv_filename, dtype=str)

def process_repo_input(text):
    if not text:
        return pd.DataFrame(columns=["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
    # Split by newlines and commas, strip whitespace
    repo_ids = [repo.strip() for repo in re.split(r'[\n,]+', text) if repo.strip()]
    # Write to CSV
    csv_filename = "repo_ids.csv"
    with open(csv_filename, mode="w", newline='', encoding="utf-8") as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
        for repo_id in repo_ids:
            writer.writerow([repo_id, "", "", "", ""])
    # Read the CSV into a DataFrame to display
    df = read_csv_as_text(csv_filename)
    return df

# Store the last entered repo ids and the current index in global variables for button access
last_repo_ids = []
current_repo_idx = 0

# Store extracted keywords for the chatbot flow
generated_keywords = []

def process_repo_input_and_store(text):
    global last_repo_ids, current_repo_idx
    if not text:
        last_repo_ids = []
        current_repo_idx = 0
        return pd.DataFrame(columns=["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
    repo_ids = [repo.strip() for repo in re.split(r'[\n,]+', text) if repo.strip()]
    last_repo_ids = repo_ids
    current_repo_idx = 0
    csv_filename = "repo_ids.csv"
    with open(csv_filename, mode="w", newline='', encoding="utf-8") as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
        for repo_id in repo_ids:
            writer.writerow([repo_id, "", "", "", ""])
    df = read_csv_as_text(csv_filename)
    return df

def keyword_search_and_update(keyword):
    global last_repo_ids, current_repo_idx
    if not keyword:
        return pd.DataFrame(columns=["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
    repo_ids = search_top_spaces(keyword, limit=5)
    last_repo_ids = repo_ids
    current_repo_idx = 0
    csv_filename = "repo_ids.csv"
    with open(csv_filename, mode="w", newline='', encoding="utf-8") as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
        for repo_id in repo_ids:
            writer.writerow([repo_id, "", "", "", ""])
    df = read_csv_as_text(csv_filename)
    return df

def show_combined_repo_and_llm():
    global current_repo_idx
    if not last_repo_ids:
        return "No repo ID available. Please submit repo IDs first.", "", pd.DataFrame()
    if current_repo_idx >= len(last_repo_ids):
        return "All repo IDs have been processed.", "", read_csv_as_text("repo_ids.csv")
    repo_id = last_repo_ids[current_repo_idx]
    try:
        download_space_repo(repo_id, local_dir="repo_files")
    except Exception as e:
        return f"Error downloading repo: {e}", "", read_csv_as_text("repo_ids.csv")
    txt_path = combine_repo_files_for_llm()
    try:
        with open(txt_path, "r", encoding="utf-8") as f:
            combined_content = f.read()
    except Exception as e:
        return f"Error reading {txt_path}: {e}", "", read_csv_as_text("repo_ids.csv")
    llm_output = analyze_combined_file(txt_path)
    llm_json = parse_llm_json_response(llm_output)
    # Update CSV for the current repo id
    csv_filename = "repo_ids.csv"
    extraction_status = ""
    strengths = ""
    weaknesses = ""
    try:
        df = read_csv_as_text(csv_filename)
        for col in ["strength", "weaknesses", "speciality", "relevance rating"]:
            df[col] = df[col].astype(str)
        for idx, row in df.iterrows():
            if row["repo id"] == repo_id:
                if isinstance(llm_json, dict) and "error" not in llm_json:
                    extraction_status = "JSON extraction: SUCCESS"
                    strengths = llm_json.get("strength", "")
                    weaknesses = llm_json.get("weaknesses", "")
                    df.at[idx, "strength"] = strengths
                    df.at[idx, "weaknesses"] = weaknesses
                    df.at[idx, "speciality"] = llm_json.get("speciality", "")
                    df.at[idx, "relevance rating"] = llm_json.get("relevance rating", "")
                else:
                    extraction_status = f"JSON extraction: FAILED\nRaw: {llm_json.get('raw', '') if isinstance(llm_json, dict) else llm_json}"
                break
        df.to_csv(csv_filename, index=False)
    except Exception as e:
        df = read_csv_as_text(csv_filename)
        extraction_status = f"CSV update error: {e}"
    # Move to next repo for next click
    current_repo_idx += 1
    summary = f"{extraction_status}\n\nStrengths:\n{strengths}\n\nWeaknesses:\n{weaknesses}"
    return combined_content, summary, df

def go_to_analysis():
    return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)

def go_to_input():
    return gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)

def go_to_chatbot():
    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)

def go_to_start():
    return gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), gr.update(visible=False)

def go_to_results():
    return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)

repo_id_input = gr.Textbox(label="Enter repo IDs (comma or newline separated)", lines=5, placeholder="repo1, repo2\nrepo3")
df_output = gr.Dataframe(headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"],
    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
    if not keywords:
        return pd.DataFrame(columns=["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
    # Use the keywords to search for repos
    repo_ids = search_top_spaces(keywords, limit=5)
    last_repo_ids = repo_ids
    current_repo_idx = 0
    csv_filename = "repo_ids.csv"
    with open(csv_filename, mode="w", newline='', encoding="utf-8") as csvfile:
        writer = csv.writer(csvfile)
        writer.writerow(["repo id", "strength", "weaknesses", "speciality", "relevance rating"])
        for repo_id in repo_ids:
            writer.writerow([repo_id, "", "", "", ""])
    df = read_csv_as_text(csv_filename)
    return df

with gr.Blocks() as demo:
    page_state = gr.State(0)

    # --- Start Page: Option Selection ---
    with gr.Column(visible=True) as start_page:
        gr.Markdown("## Welcome! How would you like to proceed?")
        option_a_btn = gr.Button("A) I know which repos I want to search and research about")
        option_b_btn = gr.Button("B) I don't know exactly what I want (Chatbot)")

    # --- Page 1: Input ---
    with gr.Column(visible=False) as input_page:
        gr.Markdown("## Enter Keyword or Repo IDs")
        keyword_input = gr.Textbox(label="Enter keyword to search repos", lines=1, placeholder="e.g. audio")
        keyword_btn = gr.Button("Search and Update Repo List")
        repo_id_box = repo_id_input.render()
        df_box = df_output.render()
        submit_btn = gr.Button("Submit Repo IDs")
        next_btn = gr.Button("Next: Go to Analysis")
        back_to_start_btn = gr.Button("Back to Start")

    # --- Page 2: Analysis ---
    with gr.Column(visible=False) as analysis_page:
        gr.Markdown("## Combine and Display Repo Files")
        combine_btn = gr.Button("Download, Combine & Show .py/.md Files from Next Repo and Analyze")
        combined_txt = gr.Textbox(label="Combined Repo Files", lines=20)
        llm_output_txt = gr.Textbox(label="LLM Analysis Output", lines=10)
        df_display = gr.Dataframe(
            headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"],
            datatype=["str", "str", "str", "str", "str", "str"]
        )
        back_btn = gr.Button("Back to Input")
        back_to_start_btn2 = gr.Button("Back to Start")

    # --- Page 3: Chatbot ---
    with gr.Column(visible=False) as chatbot_page:
        gr.Markdown("## Repo Recommendation Chatbot")
        chatbot = gr.Chatbot()
        state = gr.State([])
        user_input = gr.Textbox(label="Your message", placeholder="Describe your ideal repo or answer the assistant's questions...")
        send_btn = gr.Button("Send")
        end_btn = gr.Button("End Chat and Extract Keywords")
        keywords_output = gr.Textbox(label="Extracted Keywords for Repo Search", interactive=False)
        go_to_results_btn = gr.Button("Find Repos with These Keywords")
        back_to_start_btn3 = gr.Button("Back to Start")

    # --- Page 4: Results after Chatbot ---
    with gr.Column(visible=False) as results_page:
        gr.Markdown("## Repo Results Based on Your Conversation")
        results_df = gr.Dataframe(
            headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"],
            datatype=["str", "str", "str", "str", "str", "str"]
        )
        back_to_start_btn4 = gr.Button("Back to Start")

    # Navigation logic
    option_a_btn.click(go_to_input, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])
    option_b_btn.click(go_to_chatbot, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])
    next_btn.click(go_to_analysis, inputs=None, outputs=[input_page, analysis_page, chatbot_page, results_page])
    back_btn.click(go_to_input, inputs=None, outputs=[input_page, analysis_page, chatbot_page, results_page])
    back_to_start_btn.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])
    back_to_start_btn2.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])
    back_to_start_btn3.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])
    back_to_start_btn4.click(go_to_start, inputs=None, outputs=[start_page, input_page, chatbot_page, results_page])

    # Keyword and repo input logic
    keyword_btn.click(keyword_search_and_update, inputs=keyword_input, outputs=df_box)
    submit_btn.click(process_repo_input_and_store, inputs=repo_id_box, outputs=df_box)

    # Analysis logic
    combine_btn.click(show_combined_repo_and_llm, inputs=None, outputs=[combined_txt, llm_output_txt, df_display])

    # Chatbot logic
    def user_send(user_message, history):
        assistant_reply = chat_with_user(user_message, history)
        history = history + [[user_message, assistant_reply]]
        return history, history, ""

    def end_chat(history):
        keywords = extract_keywords_from_conversation(history)
        global generated_keywords
        generated_keywords.clear()
        generated_keywords.extend([k.strip() for k in keywords.split(",") if k.strip()])
        return keywords

    def go_to_results_from_chatbot(keywords):
        # Use the keywords to search and update the CSV, then display the DataFrame
        df = use_keywords_to_search_and_update_csv(keywords)
        return gr.update(visible=False), gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), df

    send_btn.click(user_send, inputs=[user_input, state], outputs=[chatbot, state, user_input])
    end_btn.click(end_chat, inputs=state, outputs=keywords_output)
    go_to_results_btn.click(go_to_results_from_chatbot, inputs=keywords_output, outputs=[chatbot_page, input_page, analysis_page, results_page, results_df])

demo.launch()