Update pages/2_phonics.py

pages/2_phonics.py

@@ -1,414 +1,414 @@
-# 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 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[0]}], "role": "user"})
-        new_history.append({"parts": [{"text": chat[1]}], "role": "model"})
-    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
-
-def show1by1(lesson_data: str) -> list:
-    lessonList = []
-    json_string = lesson_data.replace('```json\n', '').replace('```', '').strip()
-    lesson_data = json.loads(json_string)
-    steps = lesson_data['Lesson']['Steps']
-
-    for step in steps:
-        instructions = step['Instructions']
-        for instruction in instructions:
-            instruction_key = next(iter(instruction))
-            lessonList.append(instruction[instruction_key])
-        lessonList.append(f"Step {step['Step']}: {step['Name']} completed.")
-    lessonList.append("Lesson completed.")
-    return lessonList
-
-def process_response(user_input: str, conversation_history: list) -> tuple:
-    if not conversation_history:
-        model_response, conversation_history = generate_response(initial_prompt, conversation_history)
-    else:
-        model_response, conversation_history = generate_response(user_input, conversation_history)
-    lessonList = ast.literal_eval(model_response)
-    return lessonList, conversation_history
-    
-@st.cache_data
-def generate_image(prompt: str) -> str:
-    try:
-        return findImg(prompt)
-    except:
-        return "static/default_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'] = 0
-        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'])
-        with row2:
-            colsend, colnext,  = st.columns(2,gap="medium")
-            with colsend:
-            
-                if st.button("&nbsp;&nbsp;&nbsp; Next &nbsp;&nbsp;&nbsp;"):
-                    if user_message.lower() == 'quit':
-                        st.write("Conversation ended.")
-                    else :
-                        if 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['lessonList'], st.session_state['conversation_history'] = process_response(
-                                user_message, st.session_state['conversation_history']
-                            )
-                            st.session_state['msg_index'] = 0
-        
-    
-            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']=generate_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()
+# 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 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[0]}], "role": "user"})
+        new_history.append({"parts": [{"text": chat[1]}], "role": "model"})
+    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
+
+def show1by1(lesson_data: str) -> list:
+    lessonList = []
+    json_string = lesson_data.replace('```json\n', '').replace('```', '').strip()
+    lesson_data = json.loads(json_string)
+    steps = lesson_data['Lesson']['Steps']
+
+    for step in steps:
+        instructions = step['Instructions']
+        for instruction in instructions:
+            instruction_key = next(iter(instruction))
+            lessonList.append(instruction[instruction_key])
+        lessonList.append(f"Step {step['Step']}: {step['Name']} completed.")
+    lessonList.append("Lesson completed.")
+    return lessonList
+
+def process_response(user_input: str, conversation_history: list) -> tuple:
+    if not conversation_history:
+        model_response, conversation_history = generate_response(initial_prompt, conversation_history)
+    else:
+        model_response, conversation_history = generate_response(user_input, conversation_history)
+    lessonList = ast.literal_eval(model_response)
+    return lessonList, conversation_history
+    
+@st.cache_data
+def generate_image(prompt: str) -> str:
+    try:
+        return findImg(prompt)
+    except:
+        return "static/default_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'] = 0
+        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'])
+        with row2:
+            colsend, colnext,  = st.columns(2,gap="medium")
+            with colsend:
+            
+                if st.button("&nbsp;&nbsp;&nbsp; Next &nbsp;&nbsp;&nbsp;"):
+                    if user_message.lower() == 'quit':
+                        st.write("Conversation ended.")
+                    else :
+                        if 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['lessonList'], st.session_state['conversation_history'] = process_response(
+                                user_message, st.session_state['conversation_history']
+                            )
+                            st.session_state['msg_index'] = 0
+        
+    
+            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']=generate_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()