app.py

import json
import os
import re
import csv
import tempfile
from rapidfuzz import fuzz
import datetime
import gradio as gr

# -----------------------------
# Config / data loading
# -----------------------------
DATA_PATH = "quotes.json"

def load_quotes():
    if os.path.exists(DATA_PATH):
        try:
            with open(DATA_PATH, "r", encoding="utf-8") as f:
                data = json.load(f)
            if isinstance(data, dict):
                print(f"Loaded dataset from {DATA_PATH} with {len(data)} categories.")
                return data
        except Exception as e:
            print(f"Failed to load {DATA_PATH}: {e}")
    print("No dataset file found. Upload one via the UI.")
    return {}

QUOTES = load_quotes()

# -----------------------------
# Text helpers
# -----------------------------
STOPWORDS = {
    "the","a","an","and","or","but","if","then","so","than","to","of","in","on","at","for",
    "is","are","was","were","be","being","been","it","that","this","these","those","with",
    "as","by","from","about","into","over","after","before","up","down","out"
}

POS_HINTS = {"good","great","love","like","enjoy","awesome","amazing","nice","positive","best","fantastic","excellent"}
NEG_HINTS = {"bad","hate","dislike","worst","awful","terrible","negative","poor","meh","gross","unsafe","hard","difficult"}

punct_re = re.compile(r"[{}]".format(re.escape("""!"#$%&'()*+,-./:;<=>?@[\]^_`{|}~""")))

def normalize(text: str) -> str:
    return punct_re.sub(" ", (text or "").lower())

def tokenize(text: str):
    return [t for t in normalize(text).split() if t and t not in STOPWORDS]

def infer_sentiment(user_text: str) -> str:
    tl = normalize(user_text)
    has_pos = any(w in tl for w in POS_HINTS)
    has_neg = any(w in tl for w in NEG_HINTS)
    if has_pos and not has_neg:
        return "positive"
    if has_neg and not has_pos:
        return "negative"
    return "positive"

# -----------------------------
# Retrieval
# -----------------------------
def best_match_quote(user_text: str) -> str:
    best_score = 0
    best_quote = None
    for cat, quotes_list in QUOTES.items():
        for quote_entry in quotes_list:
            quote = quote_entry.get("quote", "")
            if not quote.strip():
                continue
            score = fuzz.partial_ratio(user_text.lower(), quote.lower())
            if score > best_score:
                best_score = score
                best_quote = quote
    if best_score < 30 or best_quote is None:
        return f"No data about '{user_text}'"
    return best_quote

# -----------------------------
# Gradio callbacks
# -----------------------------
def respond(message, history, category):
    if not QUOTES:
        bot = "No dataset loaded. Please upload a JSON file first."
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": bot})
        return "", history

    if not category:
        bot = "Please select a category."
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": bot})
        return "", history

    quote = best_match_quote(message)

    # 3-fold response
    summary = quote.split(". ")[0] + "." if "." in quote else quote
    detail = quote
    unknown = ""
    if "No data about" in quote:
        unknown = quote

    bot_text = f"Summary:\n{summary}\n\nWhat real people say:\n{detail}"
    if unknown:
        bot_text += f"\n\n{unknown}"

    history.append({"role": "user", "content": message})
    history.append({"role": "assistant", "content": bot_text})
    return "", history

def clear_chat():
    return None

def upload_json(filepath):
    global QUOTES, DATA_PATH
    try:
        with open(filepath, "r", encoding="utf-8") as f:
            data = json.load(f)
        if not isinstance(data, dict):
            return gr.update(value="Upload failed: JSON root must be an object."), gr.update(choices=[])
        QUOTES = data
        DATA_PATH = os.path.basename(filepath)
        cats = sorted(list(QUOTES.keys()))
        status = f"Loaded {len(cats)} categories from {DATA_PATH}."
        return status, gr.update(choices=cats, value=(cats[0] if cats else None))
    except Exception as e:
        return f"Error loading file: {e}", gr.update(choices=[])

def download_current_json():
    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".json")
    with open(tmp_file.name, "w", encoding="utf-8") as f:
        json.dump(QUOTES, f, indent=2, ensure_ascii=False)
    return tmp_file.name

def download_conversation_csv(history):
    tmp_file = tempfile.NamedTemporaryFile(delete=False, suffix=".csv")
    with open(tmp_file.name, "w", newline="", encoding="utf-8") as f:
        writer = csv.writer(f)
        writer.writerow(["role", "message"])
        for msg in history:
            writer.writerow([msg.get("role"), msg.get("content")])
    return tmp_file.name

# -----------------------------
# UI
# -----------------------------
with gr.Blocks() as demo:
    gr.Markdown("## 🎓 College Life Chatbot — Category-Aware, Fuzzy Matching")

    initial_categories = sorted(list(QUOTES.keys()))

    with gr.Row():
        category = gr.Dropdown(
            label="Category",
            choices=initial_categories,
            value=(initial_categories[0] if initial_categories else None)
        )

    chatbot = gr.Chatbot(label="Conversation", height=360, type="messages")
    msg = gr.Textbox(label="Your message", placeholder="Ask something like: 'Is food good in college?'", autofocus=True)
    send = gr.Button("Send")
    clear = gr.Button("Clear")

    with gr.Row():
        upload_btn = gr.File(label="Upload dataset (.json)", file_types=[".json"], type="filepath")
        upload_status = gr.Textbox(label="Upload status", interactive=False)

    # New download system
    with gr.Row():
        download_json_btn = gr.Button("Download current dataset (.json)")
        download_csv_btn = gr.Button("Export conversation to CSV")
        download_json_file = gr.File(label="JSON download")
        download_csv_file = gr.File(label="CSV download")

    # Events
    msg.submit(respond, [msg, chatbot, category], [msg, chatbot])
    send.click(respond, [msg, chatbot, category], [msg, chatbot])
    clear.click(clear_chat, None, chatbot, queue=False)
    upload_btn.upload(upload_json, upload_btn, [upload_status, category])

    # Fixed download events using Button -> File
    download_json_btn.click(fn=download_current_json, inputs=None, outputs=download_json_file)
    download_csv_btn.click(fn=download_conversation_csv, inputs=chatbot, outputs=download_csv_file)

# -----------------------------
# Startup log
# -----------------------------
print(f"===== Application Startup at {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')} =====")
if QUOTES:
    for cat, entries in QUOTES.items():
        print(f" - {cat}: {len(entries)} entries")

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860)