app.py

import os
import base64
import re
import requests
import pytz
import json
from io import BytesIO
from datetime import datetime
import gradio as gr
import openai
import fitz  # pymupdf
from bs4 import BeautifulSoup
from moviepy.video.io.VideoFileClip import VideoFileClip

# 🔐 CONFIG
KEY_FILE = "openai_api_key.txt"
MODEL = "gpt-4o-2024-05-13"

# 🍿 Default key load
if os.path.exists(KEY_FILE):
    with open(KEY_FILE, 'r') as f:
        DEFAULT_KEY = f.read().strip()
else:
    DEFAULT_KEY = ''

# 🔧 HELPERS
def save_api_key(api_key):
    with open(KEY_FILE, 'w') as f:
        f.write(api_key.strip())
    return "🔑 Key saved!"

# 🗒️ Chat

def chat_with_openai(api_key, user_message, history):
    openai.api_key = api_key.strip()
    messages = []
    for u, a in history:
        messages.append({"role": "user", "content": u})
        messages.append({"role": "assistant", "content": a})
    messages.append({"role": "user", "content": user_message})
    resp = openai.ChatCompletion.create(model=MODEL, messages=messages)
    answer = resp.choices[0].message.content
    history.append((user_message, answer))
    return history

# 🖼️ Image analysis
def image_to_base64(file):
    return base64.b64encode(file.read()).decode()

def analyze_image(api_key, file, prompt):
    data_uri = f"data:image/png;base64,{image_to_base64(file)}"
    openai.api_key = api_key.strip()
    resp = openai.ChatCompletion.create(
        model=MODEL,
        messages=[
            {"role": "system", "content": "You are a helpful assistant that responds in Markdown."},
            {"role": "user", "content": [
                    {"type":"text","text":prompt},
                    {"type":"image_url","image_url":{"url":data_uri}}
            ]}
        ]
    )
    return resp.choices[0].message.content

# 🎤 Audio transcription + chat
def transcribe_audio_file(api_key, file):
    openai.api_key = api_key.strip()
    resp = openai.Audio.transcriptions.create(model="whisper-1", file=file)
    return resp.text

# 🎥 Video summarize
def summarize_video(api_key, file, prompt, seconds=2):
    # save tmp
    with open("tmp_vid.mp4", 'wb') as f: f.write(file.read())
    clip = VideoFileClip("tmp_vid.mp4")
    frames = []
    step = int(clip.fps * seconds)
    for t in range(0, int(clip.duration), seconds):
        frame = clip.get_frame(t)
        buf = BytesIO()
        from PIL import Image
        Image.fromarray(frame).save(buf, format='JPEG')
        frames.append(base64.b64encode(buf.getvalue()).decode())
    transcript = transcribe_audio_file(api_key, open("tmp_vid.mp4", 'rb'))
    openai.api_key = api_key.strip()
    messages = [{"role":"system","content":"You are a helpful assistant."},
                {"role":"user","content": prompt}]
    for f64 in frames:
        messages.append({"role":"user","content": {"type":"image_url","image_url":{"url":f"data:image/jpeg;base64,{f64}"}}})
    messages.append({"role":"user","content": f"Transcript: {transcript}"})
    resp = openai.ChatCompletion.create(model=MODEL, messages=messages)
    return resp.choices[0].message.content

# 📄 PDF->Markdown
def pdf_to_markdown(path):
    doc = fitz.open(path)
    md = ''
    for page in doc:
        md += page.get_text('markdown') + '\n'
    return md

# 🔍 ArXiv RAG
from gradio_client import Client
def arxiv_search(query):
    client = Client("awacke1/Arxiv-Paper-Search-And-QA-RAG-Pattern")
    refs = client.predict(query, 10, "Semantic Search - up to 10 Mar 2024", "mistralai/Mixtral-8x7B-Instruct-v0.1", api_name="/update_with_rag_md")
    ans = client.predict(query, "mistralai/Mixtral-8x7B-Instruct-v0.1", True, api_name="/ask_llm")
    return refs + "\n" + ans

# 🔈 TTS
from gtts import gTTS
def tts_bytes(text):
    buf = BytesIO()
    gTTS(text=text, lang='en').write_to_fp(buf)
    buf.seek(0)
    return buf.read()

# UI CONFIG
ui_config = {
    "chat": {"label":"💬 ChatGPT-4o", "placeholder":"Say something..."},
    "image_prompt": {"label":"🖼️ Image Prompt", "default":"Describe this image..."},
    "audio_prompt": {"label":"🎤 Audio Prompt", "default":"Transcribe and summarize..."},
    "video_prompt": {"label":"🎥 Video Prompt", "default":"Summarize this video..."},
    "pdf_prompt": {"label":"📄 PDF Prompt", "default":"Convert PDF to markdown..."},
    "arxiv_prompt": {"label":"🔍 Arxiv Query", "default":"Search papers..."}
}

with gr.Blocks(title="🔬🧠 ScienceBrain.Gradio") as demo:
    gr.Markdown("# 🔬🧠 ScienceBrain Gradio
Enter API key below."
    )
    with gr.Row():
        api_key = gr.Textbox(label="🔑 OpenAI Key", value=DEFAULT_KEY, type="password")
        save_btn = gr.Button("💾 Save Key")
        status = gr.Textbox(label="Status", interactive=False)
    save_btn.click(save_api_key, inputs=api_key, outputs=status)

    # Tabs for each modality
    with gr.Tab("💬 Chat"):
        chatbot = gr.Chatbot(label=ui_config['chat']['label'], value=[])
        msg = gr.Textbox(label=ui_config['chat']['label'], placeholder=ui_config['chat']['placeholder'])
        msg.submit(chat_with_openai, inputs=[api_key, msg, chatbot], outputs=chatbot)

    with gr.Tab("🖼️ Image"):
        img_in = gr.File(file_types=['png','jpg','jpeg'])
        img_prompt = gr.Textbox(label=ui_config['image_prompt']['label'], value=ui_config['image_prompt']['default'])
        img_btn = gr.Button("🔍 Analyze Image")
        img_out = gr.Markdown()
        img_btn.click(analyze_image, inputs=[api_key, img_in, img_prompt], outputs=img_out)

    with gr.Tab("🎤 Audio"):
        aud_in = gr.File(file_types=['wav','mp3'])
        aud_btn = gr.Button("🔊 Transcribe + Chat")
        aud_out = gr.Markdown()
        def audio_pipeline(key, f):
            text = transcribe_audio_file(key, f)
            reply = chat_with_openai(key, text, [])[-1][1]
            return f"**Transcript:** {text}\n\n**Reply:** {reply}"
        aud_btn.click(audio_pipeline, inputs=[api_key, aud_in], outputs=aud_out)

    with gr.Tab("🎥 Video"):
        vid_in = gr.File(file_types=['mp4'])
        vid_prompt = gr.Textbox(label=ui_config['video_prompt']['label'], value=ui_config['video_prompt']['default'])
        vid_btn = gr.Button("🎞️ Summarize Video")
        vid_out = gr.Markdown()
        vid_btn.click(summarize_video, inputs=[api_key, vid_in, vid_prompt], outputs=vid_out)

    with gr.Tab("📄 PDF"):
        pdf_in = gr.File(file_types=['pdf'])
        pdf_btn = gr.Button("📝 Convert PDF")
        pdf_out = gr.Markdown()
        pdf_btn.click(lambda f: pdf_to_markdown(f.name), inputs=[pdf_in], outputs=pdf_out)

    with gr.Tab("🔍 ArXiv"):
        arxiv_in = gr.Textbox(label=ui_config['arxiv_prompt']['label'], value=ui_config['arxiv_prompt']['default'])
        arxiv_btn = gr.Button("🔎 Search ArXiv")
        arxiv_out = gr.Markdown()
        arxiv_btn.click(arxiv_search, inputs=[arxiv_in], outputs=arxiv_out)

    with gr.Tab("⚙️ Quick Tests"):
        tests = [
            ("📝 Text","What is 2+2?"),
            ("🖼️ Image","Analyze image https://via.placeholder.com/150.png"),
            ("🎤 Audio","Transcribe https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg10.wav"),
            ("🎥 Video","Summarize video https://sample-videos.com/video123/mp4/240/big_buck_bunny_240p_1mb.mp4"),
            ("🖼️+📝 Img+Txt","Given image https://via.placeholder.com/150.png list 3 facts."),
            ("🎤+📝 Aud+Txt","Given audio https://www2.cs.uic.edu/~i101/SoundFiles/gettysburg10.wav summarize."),
            ("🎥+📝 Vid+Txt","Given video https://sample-videos.com/video123/mp4/240/big_buck_bunny_240p_1mb.mp4 transcript+summary.")
        ]
        for idx, (e,p) in enumerate(tests,1):
            btn = gr.Button(f"{idx}. {e} Test")
            btn.click(chat_with_openai, inputs=[api_key, gr.State(p), gr.State([])], outputs=chatbot)


if __name__ == "__main__":
    demo.launch()