app.py

import os
import gradio as gr
import json, re
import openai
from datetime import datetime
import pdfplumber, docx, pandas as pd
from PIL import Image

openai.api_key = os.environ.get("GROQ_API_KEY")
openai.api_base = "https://api.groq.com/openai/v1"

# Chat with Groq
def chat_with_groq(message, state):
    if state is None:
        state = {"oai_history": [], "chatbot_ui": []}

    oai_history = state["oai_history"]
    chatbot_ui = state["chatbot_ui"]

    if not oai_history:
        oai_history.append({"role": "system", "content": "You are Neobot 🤖 – helpful and concise."})

    oai_history.append({"role": "user", "content": message})
    try:
        response = openai.ChatCompletion.create(
            model="llama3-70b-8192",
            messages=oai_history
        )
        reply = response["choices"][0]["message"]["content"]
    except Exception as e:
        reply = f"❌ Error: {e}"

    oai_history.append({"role": "assistant", "content": reply})
    chatbot_ui.append({"role": "user", "content": message})
    chatbot_ui.append({"role": "assistant", "content": reply})

    return "", chatbot_ui, {"oai_history": oai_history, "chatbot_ui": chatbot_ui}

# Save chat
def save_session(state):
    if not state or not state.get("oai_history"):
        return "❌ No chat to save"

    oai_history = state["oai_history"]
    prompt = next((m["content"] for m in oai_history if m["role"] == "user"), "chat")
    title = re.sub(r"[^\w\s]", "", prompt).strip()
    title = " ".join(title.split()[:6]) or "Chat"
    timestamp = datetime.now().strftime("%b %d %Y %H-%M")
    filename = f"{title} - {timestamp}.json"
    with open(filename, "w", encoding="utf-8") as f:
        json.dump(oai_history, f, indent=2, ensure_ascii=False)
    return f"✅ Chat saved as: {filename[:-5]}"

def list_saved_files():
    return sorted([f[:-5] for f in os.listdir() if f.endswith(".json")])

def load_chat(name):
    filename = f"{name}.json"
    try:
        with open(filename, "r", encoding="utf-8") as f:
            oai_history = json.load(f)
        chatbot_ui = []
        for m in oai_history:
            if m["role"] in ["user", "assistant"]:
                chatbot_ui.append({"role": m["role"], "content": m["content"]})
        return chatbot_ui, {"oai_history": oai_history, "chatbot_ui": chatbot_ui}, f"✅ Loaded {name}"
    except Exception as e:
        return [], {"oai_history": [], "chatbot_ui": []}, f"❌ Could not load {name}: {e}"

# Transcription
def transcribe_audio(file):
    if not file: return ""
    audio_file = open(file, "rb")
    transcript = openai.Audio.transcribe("whisper-1", audio_file)
    return transcript["text"]

# File processor
def process_file(file):
    if file is None:
        return ""
    filename = file.name
    ext = filename.split(".")[-1].lower()
    try:
        if ext == "pdf":
            with pdfplumber.open(file.name) as pdf:
                text = "\n".join([p.extract_text() for p in pdf.pages if p.extract_text()])
        elif ext == "docx":
            doc = docx.Document(file.name)
            text = "\n".join([p.text for p in doc.paragraphs])
        elif ext in ["csv", "xlsx"]:
            df = pd.read_csv(file.name) if ext == "csv" else pd.read_excel(file.name)
            text = df.to_string()
        elif ext in ["png", "jpg", "jpeg"]:
            Image.open(file.name)  # just validate
            text = "Image uploaded. Please describe what you want to know."
        else:
            text = file.read().decode("utf-8")

        if len(text) > 8000:
            text = text[:8000]

        response = openai.ChatCompletion.create(
            model="llama3-70b-8192",
            messages=[
                {"role": "system", "content": "Summarize or explain the content uploaded."},
                {"role": "user", "content": text}
            ]
        )
        return response["choices"][0]["message"]["content"]
    except Exception as e:
        return f"❌ Error processing file: {e}"

# UI
with gr.Blocks(css="""
body { background: #fff; color: #000; }
#title { font-size: 2rem; text-align: center; margin-top: 1rem; }
.gr-chatbot { height: 70vh !important; }
textarea, input[type='text'] { background: #f0f0f0; border-radius: 8px; }
.gr-button { border-radius: 8px; background: #000; color: white; }
""") as demo:

    state = gr.State({"oai_history": [], "chatbot_ui": []})

    gr.Markdown("# 🤖 Neobot - Always Listening", elem_id="title")

    chatbot = gr.Chatbot(type="messages")

    with gr.Row():
        chat_input = gr.Textbox(placeholder="Type here or use mic...", scale=6, show_label=False)
        send_btn = gr.Button("🚀", scale=1)

    with gr.Row():
        mic_audio = gr.Audio(type="filepath", label="🎙️ Record Voice")
        mic_audio.change(transcribe_audio, [mic_audio], [chat_input])

    with gr.Row():
        file_upload = gr.File(label="📎 Upload file", file_types=[".pdf", ".docx", ".txt", ".csv", ".xlsx", ".jpg", ".png"])
        process_btn = gr.Button("🔍 Analyze File")

    with gr.Row():
        new_btn = gr.Button("🆕 New Chat")
        save_btn = gr.Button("💾 Save Chat")
        dropdown = gr.Dropdown(label="📂 Saved Chats", choices=list_saved_files(), interactive=True)
        load_btn = gr.Button("📥 Load Chat")

    status = gr.Markdown()

    send_btn.click(chat_with_groq, [chat_input, state], [chat_input, chatbot, state])
    chat_input.submit(chat_with_groq, [chat_input, state], [chat_input, chatbot, state])

    process_btn.click(process_file, [file_upload], [chat_input])

    new_btn.click(lambda: ("", [], {"oai_history": [], "chatbot_ui": []}), [], [chat_input, chatbot, state])
    save_btn.click(save_session, [state], [status])
    save_btn.click(lambda: gr.update(choices=list_saved_files()), [], [dropdown])
    load_btn.click(load_chat, [dropdown], [chatbot, state, status])

demo.launch()