app.py

import os
import time
from typing import List, Tuple, Optional
from pytube import YouTube
from moviepy.editor import *
import speech_recognition as sr
# import stanza
import pandas as pd
import numpy as np
import google.generativeai as genai
from tqdm.auto import tqdm
import time

import google.generativeai as genai
import gradio as gr
from PIL import Image

print("google-generativeai:", genai.__version__)

GOOGLE_API_KEY = os.environ.get("GOOGLE_API_KEY")

TITLE = """<h1 align="center">تجربة جزئية إقتراح الآيات من ضياء</h1>"""
SUBTITLE = """<h2 align="center">Play with Gemini Pro and Gemini Pro Vision API 🖇️</h2>"""
DUPLICATE = """
<div style="text-align: center; display: flex; justify-content: center; align-items: center;">
    <a href="https://huggingface.co/spaces/SkalskiP/ChatGemini?duplicate=true">
        <img src="https://bit.ly/3gLdBN6" alt="Duplicate Space" style="margin-right: 10px;">
    </a>
    <span>Duplicate the Space and run securely with your 
        <a href="https://makersuite.google.com/app/apikey">GOOGLE API KEY</a>.
    </span>
</div>
"""


IMAGE_WIDTH = 512


safety_settings = [
    {
        "category": "HARM_CATEGORY_DANGEROUS",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HARASSMENT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "threshold": "BLOCK_NONE",
    },
    {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "threshold": "BLOCK_NONE",
    },]


def extract_text_from(vid_link):
  video_url = vid_link
  yt = YouTube(video_url)
  text = ""
  audio_stream = yt.streams.get_audio_only()
  audio_stream.download(filename='tmp.mp4')
  audio_clip = AudioFileClip('tmp.mp4')
  audio_clip.write_audiofile('tmp.wav')
  r = sr.Recognizer()
  with sr.AudioFile('tmp.wav') as source:
    audio_data = r.record(source)
    try:
        text = r.recognize_google(audio_data,  language='ar')
    except sr.UnknownValueError:
        print("Google Speech Recognition could not understand audio")
    except sr.RequestError as e:
        print("Could not request results from Google Speech Recognition service; {0}".format(e))
    return text
      

def preprocess_stop_sequences(stop_sequences: str) -> Optional[List[str]]:
    if not stop_sequences:
        return None
    return [sequence.strip() for sequence in stop_sequences.split(",")]


def preprocess_image(image: Image.Image) -> Optional[Image.Image]:
    image_height = int(image.height * IMAGE_WIDTH / image.width)
    return image.resize((IMAGE_WIDTH, image_height))


def user(text_prompt: str, chatbot: List[Tuple[str, str]]):
    return "", chatbot + [[text_prompt, None]]
#https://www.youtube.com/watch?v=5Abk7EU5EJI

def bot(
    google_key: str,
    image_prompt: Optional[Image.Image],
    temperature: float,
    max_output_tokens: int,
    stop_sequences: str,
    top_k: int,
    top_p: float,
    chatbot: List[Tuple[str, str]]
):
    google_key = google_key if google_key else GOOGLE_API_KEY
    if not google_key:
        raise ValueError(
            "GOOGLE_API_KEY is not set. "
            "Please follow the instructions in the README to set it up.")

    #text_prompt = chatbot[-1][0]
    txt_in = chatbot[-1][0]
    # if "youtube" in txt_in:
    #     text_prompt = extract_text_from(txt_in)
    # else:
    #     text_prompt = txt_in

    genai.configure(api_key=google_key)

    generation_config = genai.types.GenerationConfig(
        temperature=0.7,
        max_output_tokens=2048,
        stop_sequences=preprocess_stop_sequences(stop_sequences=stop_sequences),
        top_k=40,
        top_p=0.95)
    
    if "youtube" in txt_in:
        text_prompt = extract_text_from(txt_in)
        prompt= "استخرج كلمات مفتاحية من النص التالي: "+text_prompt
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config,safety_settings=safety_settings)
        response.resolve()
        time.sleep(0.1)

        out1 = response.text
        
        model2 = genai.GenerativeModel('gemini-pro')

        prompt = "أذكر لي آية من القران الكريم تتحدث عن أحد هذه المواضيع او اكثر: "+ out1 + " واشرح الآيه وفسرها باللغة العربية."
        response2 = model2.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config, safety_settings=safety_settings)
        response2.resolve()
        
    elif image_prompt is None:
        model = genai.GenerativeModel('gemini-pro')

        prompt= "استخرج كلمات مفتاحية من النص التالي: "+txt_in
        model = genai.GenerativeModel('gemini-pro')
        response = model.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config,safety_settings=safety_settings)
        response.resolve()
        time.sleep(0.1)

        out1 = response.text
        
        model2 = genai.GenerativeModel('gemini-pro')

        prompt = "أذكر لي آية من القران الكريم تتحدث عن أحد هذه المواضيع او اكثر: "+ out1 + " واشرح الآيه وفسرها باللغة العربية."
        response2 = model2.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config, safety_settings=safety_settings)
        response2.resolve()
        
    else:
        prompt= "اكتب لي وصف عن الصورة المرفقة "
        image_prompt = preprocess_image(image_prompt)
        model = genai.GenerativeModel('gemini-pro-vision')
        response = model.generate_content(
            contents=[prompt, image_prompt],
            stream=True,
            generation_config=generation_config, safety_settings=safety_settings)
        response.resolve()
        time.sleep(0.1)
        out1 = response.text
        
        prompt= "استخرج كلمات مفتاحية من النص التالي: "+out1
        model1 = genai.GenerativeModel('gemini-pro')
        response1 = model1.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config,safety_settings=safety_settings)
        response1.resolve()
        time.sleep(0.1)

        out2 = response1.text
        
        model2 = genai.GenerativeModel('gemini-pro')

        prompt = "أذكر لي آية من القران الكريم تتحدث عن أحد هذه المواضيع او اكثر: "+ out2 + " واشرح الآيه وفسرها باللغة العربية."
        response2 = model2.generate_content(
            prompt,
            stream=True,
            generation_config=generation_config, safety_settings=safety_settings)
        response2.resolve()
        time.sleep(0.1)

        

    # streaming effect
    chatbot[-1][1] = ""
    for chunk in response2:
        for i in range(0, len(chunk.text), 10):
            section = chunk.text[i:i + 10]
            chatbot[-1][1] += section
            time.sleep(0.01)
            yield chatbot


google_key_component = gr.Textbox(
    label="GOOGLE API KEY",
    value="",
    type="password",
    placeholder="...",
    info="You have to provide your own GOOGLE_API_KEY for this app to function properly",
    visible=GOOGLE_API_KEY is None
)

image_prompt_component = gr.Image(type="pil", label="Image", scale=1)
chatbot_component = gr.Chatbot(
    label='Diyaa',
    bubble_full_width=False,
    scale=2,
    rtl=True
)
text_prompt_component = gr.Textbox(
    placeholder="مرحبا!",
    label="ادخل رابط يوتيوب لإستخراج الآيات أو نص\موضوع معين"
)
run_button_component = gr.Button()
temperature_component = gr.Slider(
    minimum=0,
    maximum=1.0,
    value=0.4,
    step=0.05,
    label="Temperature",
    info=(
        "Temperature controls the degree of randomness in token selection. Lower "
        "temperatures are good for prompts that expect a true or correct response, "
        "while higher temperatures can lead to more diverse or unexpected results. "
    ))
max_output_tokens_component = gr.Slider(
    minimum=1,
    maximum=2048,
    value=1024,
    step=1,
    label="Token limit",
    info=(
        "Token limit determines the maximum amount of text output from one prompt. A "
        "token is approximately four characters. The default value is 2048."
    ))
stop_sequences_component = gr.Textbox(
    label="Add stop sequence",
    value="",
    type="text",
    placeholder="STOP, END",
    info=(
        "A stop sequence is a series of characters (including spaces) that stops "
        "response generation if the model encounters it. The sequence is not included "
        "as part of the response. You can add up to five stop sequences."
    ))
top_k_component = gr.Slider(
    minimum=1,
    maximum=40,
    value=32,
    step=1,
    label="Top-K",
    info=(
        "Top-k changes how the model selects tokens for output. A top-k of 1 means the "
        "selected token is the most probable among all tokens in the model’s "
        "vocabulary (also called greedy decoding), while a top-k of 3 means that the "
        "next token is selected from among the 3 most probable tokens (using "
        "temperature)."
    ))
top_p_component = gr.Slider(
    minimum=0,
    maximum=1,
    value=1,
    step=0.01,
    label="Top-P",
    info=(
        "Top-p changes how the model selects tokens for output. Tokens are selected "
        "from most probable to least until the sum of their probabilities equals the "
        "top-p value. For example, if tokens A, B, and C have a probability of .3, .2, "
        "and .1 and the top-p value is .5, then the model will select either A or B as "
        "the next token (using temperature). "
    ))

user_inputs = [
    text_prompt_component,
    chatbot_component
]

bot_inputs = [
    google_key_component,
    image_prompt_component,
    temperature_component,
    max_output_tokens_component,
    stop_sequences_component,
    top_k_component,
    top_p_component,
    chatbot_component
]

with gr.Blocks() as demo:
    gr.HTML(TITLE)
    # gr.HTML(SUBTITLE)
    # gr.HTML(DUPLICATE)
    with gr.Column():
        google_key_component.render()
        with gr.Row():
            image_prompt_component.render()
            chatbot_component.render()
        text_prompt_component.render()
        run_button_component.render()
        # with gr.Accordion("Parameters", open=False):
        #     temperature_component.render()
        #     max_output_tokens_component.render()
        #     stop_sequences_component.render()
        #     with gr.Accordion("Advanced", open=False):
        #         top_k_component.render()
        #         top_p_component.render()

    run_button_component.click(
        fn=user,
        inputs=user_inputs,
        outputs=[text_prompt_component, chatbot_component],
        queue=False
    ).then(
        fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
    )

    text_prompt_component.submit(
        fn=user,
        inputs=user_inputs,
        outputs=[text_prompt_component, chatbot_component],
        queue=False
    ).then(
        fn=bot, inputs=bot_inputs, outputs=[chatbot_component],
    )

demo.queue(max_size=99).launch(debug=False, show_error=True)