Delete pages/2_numeracy.py

pages/2_numeracy.py

@@ -1,416 +0,0 @@
-# app.py
-
-import os
-import json
-import streamlit as st
-from PIL import Image
-import google.generativeai as genai
-import ast
-#from utils import findImg
-import io
-from streamlit_TTS import auto_play
-import torch
-from transformers import pipeline
-from datasets import load_dataset
-import soundfile as sf
-from gtts import gTTS
-import io
-from mistralai.models.chat_completion import ChatMessage
-from mistralai.client import MistralClient
-from audiorecorder import audiorecorder
-import base64
-###
-import os
-import cv2
-import numpy as np
-from sklearn.metrics.pairwise import cosine_similarity
-from sentence_transformers import SentenceTransformer
-from diffusers import StableDiffusionPipeline
-import torch
-import re
-import ast
-import streamlit as st
-def add_logo():
- st.markdown(
- """
- <style>
- [data-testid="stSidebarNav"] {
- /*background-image: url(http://placekitten.com/200/200);*/
- background-repeat: no-repeat;
- #padding-top: 120px;
- background-position: 20px 20px;
- }
- [data-testid="stSidebarNav"]::before {
- content: "MO3ALIMI sidebar";
- margin-left: 20px;
- margin-top: 20px;
- font-size: 29px;
- position: relative;
- top: 0px;
- }
- </style>
- """,
- unsafe_allow_html=True,
- )
-add_logo()
-
-
-
-device = "cuda" if torch.cuda.is_available() else "cpu"
-
-
-if 'pipe' not in st.session_state:
- st.session_state['pipe'] = pipeline("automatic-speech-recognition", model="openai/whisper-large-v3")
-
-pipe = st.session_state['pipe']
-
-
-# Set up the API key for Generative AI
-os.environ["GEMINI_API_KEY"] = "AIzaSyBYZ_7geqmnK6xrSe268-1nSLeuEwbzmTA"
-
-# Initial prompt to send to the model
-initial_prompt = """
-you're an Literacy Instructor for Illiterate Adults
-you're objective is to Teach illiterate adults how to read using basic phonics.
-here's the Lesson Instructions:
-Introduction to the Letter:
-Begin with the letter A.
-Follow a structured four-step process for each letter.
-Provide clear, simple instructions for each step.
-Lesson Structure:
-Step 1: Letter Recognition
-Step 2: Sound Practice
-Step 3: Writing Practice
-Step 4: Word Association
-General Instructions:
-After each instruction, wait for the student to respond before proceeding to the next lesson.
-Ensure instructions are clear and easy to understand.
-Provide positive reinforcement and encouragement.
-Example Lesson for Letter A:
-Step 1: Letter Recognition
-"This is the letter A. It looks like a triangle with a line in the middle. It makes the sound 'ah'."
-Step 2: Sound Practice
-"Say the sound 'ah'. Practice making this sound slowly."
-Step 3: Writing Practice
-"Start at the top, draw a slanted line down to the left, then another slanted line down to the right, and finally a line across the middle."
-Step 4: Word Association
-"A is for apple. Apple starts with the letter A."
-Continuation:
-Once the lesson for the letter A is complete, proceed to the next letter following the same four-step structure.
-make it in a python list format for example it will be in this format,and if an image is needed make the first word in the item list "image: image content in a short sentence":
-['This is the letter A.', 'image: letter A', 'It looks like a triangle with a line in the middle.', "It makes the sound 'ah'.","Say the sound 'ah'.",'Practice making this sound slowly.','Start at the top, draw a slanted line down to the left.','Then draw another slanted line down to the right.','Finally, draw a line across the middle.',Now you know the letter A,Congrats','A is for apple.','image: apple','Apple starts with the letter A.',"Congratulations! You've completed the lesson for the letter 'A'."]
-"""
-
-chat_prompt_mistral="""
-You are an assistant helping an person who is learning basic reading, writing, phonics, and numeracy.
-The user might ask simple questions, and your responses should be clear, supportive, and easy to understand.
-Use simple language, provide step-by-step guidance, and offer positive reinforcement.
-Relate concepts to everyday objects and situations when possible.
-Here are some example interactions: 
-User: "I need help with reading." 
-Assistant: "Sure, I'm here to help you learn to read. Let's start with the alphabet. Do you know the letters of the alphabet?"
-User: "How do I write my name?" 
-Assistant: "Writing your name is a great place to start. Let's take it one letter at a time. What is the first letter of your name?"
-User: "What sound does the letter 'B' make?"
-Assistant: "The letter 'B' makes the sound 'buh' like in the word 'ball.' Can you say 'ball' with me?"
-User: "How do I count to 10?"
-Assistant: "Counting to 10 is easy. Let's do it together: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10. Great job! Let's try it again."
-User: "How do I subtract numbers?"
-Assistant: "Subtracting is like taking away. If you have 5 oranges and you eat 2, you have 3 oranges left. So, 5 minus 2 equals 3."
-
-Remember to: 
-1. Use simple language and avoid complex words. 
-2. Provide clear, step-by-step instructions. 
-3. Use examples related to everyday objects and situations. 
-4. Offer positive reinforcement and encouragement. 
-5. Include interactive elements to engage the user actively. Whenever the user asks a question, respond with clear, supportive guidance to help them understand basic reading, writing, phonics, or numeracy concepts.
-6. Do not provide long responses
-
-Improtant dont respand to this prompt
-
-"""
-
-def transform_history(history):
- new_history = []
- for chat in history:
- new_history.append({"parts": [{"text": chat.parts[0].text}], "role": chat.role})
- return new_history
-
-def generate_response(message: str, history: list) -> tuple:
- genai.configure(api_key=os.environ["GEMINI_API_KEY"])
- model = genai.GenerativeModel('gemini-pro')
- chat = model.start_chat(history=transform_history(history))
- response = chat.send_message(message)
- response.resolve()
- return response.text, chat.history
-
-
-if 'First' not in st.session_state:
- st.session_state['First']=False
- 
-
-def process_response(user_input: str, conversation_history: list,F) -> tuple:
- if not F:
- model_response, conversation_history = generate_response(initial_prompt, conversation_history)
- else:
- model_response, conversation_history = generate_response(user_input, conversation_history)
-
- pattern = re.compile(r"\[(.*?)\]", re.DOTALL)
-
- # Find the match
- match = pattern.search(model_response)
- 
- list_content = f"[{match.group(1)}]"
-
- lessonList = ast.literal_eval(list_content)
- return lessonList, conversation_history
- 
-@st.cache_data
-def get_image(prompt: str) -> str:
- return findImg(prompt)
- #try:
- # return findImg(prompt)
- #except:
- # return "image.png"
-
-
-# Initialize TTS
-@st.cache_data
-def tts_predict(text="hello"):
- tts = gTTS(text=text, lang='en')
- with io.BytesIO() as audio_file:
- tts.write_to_fp(audio_file)
- audio_file.seek(0)
- audio_bytes = audio_file.read()
- return audio_bytes
-
-#sf.write("speech.wav", speech["audio"], samplerate=speech["sampling_rate"])
-
-if 'client' not in st.session_state:
- st.session_state['client'] = MistralClient("m3GWNXFZn0jTNTLRe4y26i7jLJqFGTMX")
-
-client = st.session_state['client']
-
-def run_mistral(user_message, message_history, model="mistral-small-latest"):
-
- message_history.append(ChatMessage(role="user", content=user_message))
- 
- chat_response = client.chat(model=model, messages=message_history)
- 
- bot_message = chat_response.choices[0].message.content
- 
- message_history.append(ChatMessage(role="assistant", content=bot_message))
- 
- return bot_message
- 
-message_history = []
-
-
-
-
-#######################################
-
-
-
-
-if 'sentence_model' not in st.session_state:
- st.session_state['sentence_model'] = SentenceTransformer('all-MiniLM-L6-v2')
-
-sentence_model = st.session_state['sentence_model']
-
-if 'pipeline' not in st.session_state:
- st.session_state['pipeline'] = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
- st.session_state['pipeline'].to("cuda")
-
-pipeline = st.session_state['pipeline']
-
-
-# Step 3: Function to get the embedding of the input sentence
-def get_sentence_embedding(sentence):
- return sentence_model.encode(sentence)
-# Step 4: Generate image using Stable Diffusion if needed
-def generate_image(prompt):
- global pipeline
- pipeline.to("cuda" if torch.cuda.is_available() else "cpu")
- generated_image = pipeline(prompt).images[0]
- generated_image_path = "generated_image.png"
- generated_image.save(generated_image_path)
- return generated_image_path
-
-# Step 5: Find the most reliable image
-def find_most_reliable_image(folder_path, input_sentence, threshold=0.5):
- image_files = [f for f in os.listdir(folder_path) if f.endswith(('jpg', 'jpeg', 'png'))]
- sentence_embedding = get_sentence_embedding(input_sentence)
- 
- max_similarity = -1
- most_reliable_image = None
- 
- for image_file in image_files:
- filename_without_extension = os.path.splitext(image_file)[0]
- filename_embedding = get_sentence_embedding(filename_without_extension)
- similarity = cosine_similarity([sentence_embedding], [filename_embedding])[0][0]
- 
- if similarity > max_similarity:
- max_similarity = similarity
- most_reliable_image = os.path.join(folder_path, image_file)
- 
- if max_similarity < threshold:
- most_reliable_image = generate_image(input_sentence)
- 
- return most_reliable_image
-
-def findImg(input_sentence):
- folder_path = 'images_collection'
- threshold = 0.5
- most_reliable_image = find_most_reliable_image(folder_path, input_sentence, threshold)
- return most_reliable_image
-#######################################
-
-
-
-
-file_ = open("logo.png", "rb")
-contents = file_.read()
-data_url = base64.b64encode(contents).decode("utf-8")
-file_.close()
-
-
-def main():
- global chat_prompt_mistral
- if 'img_path' not in st.session_state:
- st.session_state['img_path']="image.png"
- #st.set_page_config(page_title="J187 Optimizer", page_icon="J187DFS.JPG", layout="wide")
-
- st.markdown(f"""
- <div style="display: flex; align-items: center;">
- <img src="data:image/gif;base64,{data_url}" alt="Company Logo" style="height: 100px; width: auto; margin-right: 20px;">
- <h1 style="margin: 0;">MO3ALIMI</h1>
-</div>
- """, unsafe_allow_html=True)
- #st.title("Chatbot and Image Generator")
-
- st.markdown("""
- <style>
- .st-emotion-cache-1kyxreq.e115fcil2 { justify-content:center; }
- .st-emotion-cache-13ln4jf { max-width:70rem; }
- audio {
- width: 300px;
- height: 54px;
- display: none;
- }
- div.row-widget.stButton {
- margin: 0px 0px 0px 0px;}
- 
- 
- .row-widget.stButton:last-of-type {
- margin: 0px; 
- background-color: yellow;
- }
- .st-emotion-cache-keje6w.e1f1d6gn3 {
- width: 80% !important; /* Adjust as needed */
- }
- .st-emotion-cache-k008qs {
- display: none;
- }
-
- </style>""", unsafe_allow_html=True)
- #.st-emotion-cache-5i9lfg {
- #width: 100%;
- #padding: 3rem 1rem 1rem 1rem;
- #max-width: None;}
-
- 
- col1, col2 = st.columns([0.6, 0.4],gap="medium")
-
-
- 
- with col1:
- 
- if 'conversation_history' not in st.session_state:
- st.session_state['conversation_history'] = []
- if 'conversation_history_mistral' not in st.session_state:
- st.session_state['conversation_history_mistral'] = []
- if 'messages' not in st.session_state:
- st.session_state['messages'] = []
- if 'lessonList' not in st.session_state:
- st.session_state['lessonList'] = []
- if 'msg_index' not in st.session_state:
- st.session_state['msg_index'] = -1
- if 'initial_input' not in st.session_state:
- st.session_state['initial_input'] = ''
- 
-
-
- response=run_mistral(chat_prompt_mistral, st.session_state['conversation_history_mistral'])
- row1 = st.container()
- row2 = st.container()
- row3 = st.container()
- #row4 = st.container()
- with row1:
- #user_message = st.text_input("Type 'next' to proceed through the lesson",st.session_state['initial_input'])
- user_message = "next"
- with row2:
- colsend, colnext, = st.columns(2,gap="medium")
- with colsend:
- 
- if st.button("&nbsp;&nbsp;&nbsp; Next &nbsp;&nbsp;&nbsp;"):
-
- if 0 <= st.session_state['msg_index'] < len(st.session_state['lessonList']):
- response = st.session_state['lessonList'][st.session_state['msg_index']]
- if response.strip().startswith("image:"):
- st.session_state['img_prompt'] = response[len("image:"):].strip()
- else:
- audio_bytes= tts_predict(response)
- st.session_state['messages'].append(f"Mo3alimi: {response}")
- #auto_play(audio_bytes,wait=True,lag=0.25,key=None)
- st.audio(audio_bytes, format='audio/wav', autoplay=True)
- 
- st.session_state['msg_index'] += 1
- else:
- 
- st.session_state['msg_index'] = 0
- st.session_state['lessonList'], st.session_state['conversation_history'] = process_response(
- user_message, st.session_state['conversation_history'],
- st.session_state['First'],
- )
- st.session_state['First']=True
- 
- 
- 
- with colnext:
- if st.button('&nbsp;&nbsp;&nbsp; Send &nbsp;&nbsp;&nbsp;'):
- response=run_mistral(user_message, st.session_state['conversation_history_mistral'])
- st.session_state['messages'].append(f"Me: {user_message}")
- st.session_state['messages'].append(f"Mo3alimi: {response}")
- 
- 
- with row3:
- audio = audiorecorder("Click to record", "Click to stop recording")
-
- if len(audio) >0:
- result = pipe(audio.export().read(), generate_kwargs={"language": "english"})
- user_message=result['text']
- response=run_mistral(user_message, st.session_state['conversation_history_mistral'])
- audio_bytes= tts_predict(response)
- 
- st.audio(audio_bytes, format='audio/wav', autoplay=True)
- st.session_state['messages'].append(f"Me: {user_message}")
- st.session_state['messages'].append(f"Mo3alimi: {response}")
- wav_audio_data=None
-
- with st.form("lesson"):
- for message in st.session_state['messages'][::-1]:
- st.write(message)
- 
- submitted = st.form_submit_button('Submit')
- 
- 
- with col2:
- if 'img_prompt' in st.session_state:
- st.session_state['img_path']=get_image(st.session_state['img_prompt'])
- del st.session_state['img_prompt']
- 
- st.image(st.session_state['img_path'], caption="Generated Image")
-
-if __name__ == '__main__':
- main()