Delete app_R2.py

app_R2.py

@@ -1,448 +0,0 @@
-# -*- coding: utf-8 -*-
-"""app
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1pwwcBb5Zlw1DA3u5K8W8mjrwBTBWXc1L
-"""
-
-import gradio as gr
-import numpy as np
-from transformers import pipeline
-import os
-import time
-import groq
-import uuid  # For generating unique filenames
-
-# Updated imports to address LangChain deprecation warnings:
-from langchain_groq import ChatGroq
-from langchain.schema import HumanMessage
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.docstore.document import Document
-
-# Importing chardet (make sure to add chardet to your requirements.txt)
-import chardet
-
-import fitz  # PyMuPDF for PDFs
-import docx  # python-docx for Word files
-import gtts  # Google Text-to-Speech library
-from pptx import Presentation  # python-pptx for PowerPoint files
-import re
-
-# Initialize Whisper model for speech-to-text
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
-
-# Set API Key (Ensure it's stored securely in an environment variable)
-groq.api_key = os.getenv("GROQ_API_KEY")  # Replace with a valid API key
-
-#___________________________________
-
-# Authenticate with Hugging Face API using the token
-#hf_token = os.getenv("HF_TOKEN")  # Replace with the environment variable containing your Hugging Face token
-#login(token=hf_token)
-
-# Load the LLaVA model
-#model_id = "liuhaotian/LLaVA-7B"  # You can change the model ID based on what is available
-#processor = AutoProcessor.from_pretrained(model_id)
-#model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16).cuda()
-
-# Load and preprocess an image
-#image = Image.open("your_image.jpg")  # Replace with the path to your image
-#inputs = processor(text="Describe this image", images=image, return_tensors="pt").to("cuda")
-
-# Generate output from LLaVA model
-#output = model.generate(**inputs)
-
-# Decode and print the output
-#print(processor.decode(output[0]))
-
-#___________________________________
-
-# Initialize Chat Model
-chat_model = ChatGroq(model_name="llama-3.3-70b-versatile", api_key=groq.api_key) #DeepSeek-R1-Distill-Llama-70b , llama-3.3-70b-versatile , deepseek-r1-distill-qwen-32b
-
-# Initialize Embeddings and chromaDB
-
-embedding_model = HuggingFaceEmbeddings()
-vectorstore = Chroma(embedding_function=embedding_model)
-
-
-# -*- coding: utf-8 -*-
-"""app
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1jdKA4WQJcLb0_aQ3vtGVM46B1wriSsDv
-"""
-
-import gradio as gr
-import numpy as np
-from transformers import pipeline
-import os
-import time
-import groq
-import uuid  # For generating unique filenames
-
-# Updated imports to address LangChain deprecation warnings:
-from langchain_groq import ChatGroq
-from langchain.schema import HumanMessage
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain_community.vectorstores import Chroma
-from langchain_community.embeddings import HuggingFaceEmbeddings
-from langchain.docstore.document import Document
-
-# Importing chardet (make sure to add chardet to your requirements.txt)
-import chardet
-
-import fitz  # PyMuPDF for PDFs
-import docx  # python-docx for Word files
-import gtts  # Google Text-to-Speech library
-from pptx import Presentation  # python-pptx for PowerPoint files
-import re
-
-
-# Initialize Whisper model for speech-to-text
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base.en")
-
-# Set API Key (Ensure it's stored securely in an environment variable)
-groq.api_key = os.getenv("GROQ_API_KEY")
-
-# Initialize Chat Model
-chat_model = ChatGroq(model_name="llama-3.3-70b-versatile", api_key=groq.api_key) #DeepSeek-R1-Distill-Llama-70b    |   deepseek-r1-distill-qwen-32b
-
-# Initialize Embeddings and chromaDB
-os.makedirs("chroma_db", exist_ok=True)
-embedding_model = HuggingFaceEmbeddings()
-#new
-vectorstore = Chroma(
-    embedding_function=embedding_model,
-    persist_directory="chroma_db"  # Set a valid folder name or path
-)
-vectorstore.persist()
-#end New
-
-# Short-term memory for the LLM
-chat_memory = []
-
-# Prompt for quiz generation with added remark
-quiz_prompt = """
-You are an AI assistant specialized in education and assessment creation. Given an uploaded document or text, generate a quiz with a mix of multiple-choice questions (MCQs) and fill-in-the-blank questions. The quiz should be directly based on the key concepts, facts, and details from the provided material.
-Generate 20 Questions.
-Remove all unnecessary formatting generated by the LLM, including <think> tags, asterisks, markdown formatting, and any bold or italic text, as well as **, ###, ##, and # tags.
-For each question:
-- Provide 4 answer choices (for MCQs), with only one correct answer.
-- Ensure fill-in-the-blank questions focus on key terms, phrases, or concepts from the document.
-- Include an answer key for all questions.
-- Ensure questions vary in difficulty and encourage comprehension rather than memorization.
-- Additionally, implement an instant feedback mechanism:
-    - When a user selects an answer, indicate whether it is correct or incorrect.
-    - If incorrect, provide a brief explanation from the document to guide learning.
-    - Ensure responses are concise and educational to enhance understanding.
-Output Example:
-1. Fill in the blank: The LLM Agent framework has a central decision-making unit called the _______________________.
-
-Answer: Agent Core
-
-Feedback: The Agent Core is the central component of the LLM Agent framework, responsible for managing goals, tool instructions, planning modules, memory integration, and agent persona.
-
-2. What is the main limitation of LLM-based applications?
-a) Limited token capacity
-b) Lack of domain expertise
-c) Prone to hallucination
-d) All of the above
-
-Answer: d) All of the above
-
-Feedback: LLM-based applications have several limitations, including limited token capacity, lack of domain expertise, and being prone to hallucination, among others.
-
-3. Given the following info, what is the value of P(jam|Rain)?
-P(no Rain) = 0.8;
-P(no Jam) = 0.2;
-P(Rain|Jam) = 0.1
-
-a) 0.016
-b) 0.025
-c) 0.1
-d) 0.4
-
-Answer: d) 0.4
-
-Feedback: This question tests understanding of Bayes’ Theorem by requiring the calculation of conditional probability using the given values.
-"""
-
-# Function to clean AI response by removing unwanted formatting
-def clean_response(response):
-    """Removes <think> tags, asterisks, and markdown formatting."""
-    cleaned_text = re.sub(r"<think>.*?</think>", "", response, flags=re.DOTALL)
-    cleaned_text = re.sub(r"(\*\*|\*|\[|\])", "", cleaned_text)
-    cleaned_text = re.sub(r"^##+\s*", "", cleaned_text, flags=re.MULTILINE)
-    cleaned_text = re.sub(r"\\", "", cleaned_text)
-    cleaned_text = re.sub(r"---", "", cleaned_text)
-    return cleaned_text.strip()
-
-# Function to generate quiz based on content
-def generate_quiz(content):
-    prompt = f"{quiz_prompt}\n\nDocument content:\n{content}"
-    response = chat_model([HumanMessage(content=prompt)])
-    cleaned_response = clean_response(response.content)
-    return cleaned_response
-
-# Function to retrieve relevant documents from vectorstore based on user query
-def retrieve_documents(query):
-    results = vectorstore.similarity_search(query, k=3)
-    return [doc.page_content for doc in results]
-
-# Function to handle chatbot interactions with short-term memory
-def chat_with_groq(user_input):
-    try:
-        # Retrieve relevant documents for additional context
-        relevant_docs = retrieve_documents(user_input)
-        context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found."
-
-        # Construct proper prompting with conversation history
-        system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely."
-        conversation_history = "\n".join(chat_memory[-10:])  # Keep the last 10 exchanges
-        prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}"
-
-        # Call the chat model
-        response = chat_model([HumanMessage(content=prompt)])
-
-        # Clean response to remove any unwanted formatting
-        cleaned_response_text = clean_response(response.content)
-
-        # Append conversation history
-        chat_memory.append(f"User: {user_input}")
-        chat_memory.append(f"AI: {cleaned_response_text}")
-
-        # Convert response to speech
-        audio_file = speech_playback(cleaned_response_text)
-
-        # Ensure the return format is a list of tuples
-        return [(user_input, cleaned_response_text)], audio_file
-    except Exception as e:
-        return [("Error", str(e))], None
-
-
-# Function to play response as speech using gTTS
-def speech_playback(text):
-    try:
-        # Generate a unique filename for each audio file
-        unique_id = str(uuid.uuid4())
-        audio_file = f"output_audio_{unique_id}.mp3"
-
-        # Convert text to speech
-        tts = gtts.gTTS(text, lang='en')
-        tts.save(audio_file)
-
-        # Return the path to the audio file
-        return audio_file
-    except Exception as e:
-        print(f"Error in speech_playback: {e}")
-        return None
-
-# Function to detect encoding safely
-def detect_encoding(file_path):
-    try:
-        with open(file_path, "rb") as f:
-            raw_data = f.read(4096)
-            detected = chardet.detect(raw_data)
-            encoding = detected["encoding"]
-        return encoding if encoding else "utf-8"
-    except Exception:
-        return "utf-8"
-
-# Function to extract text from PDF
-def extract_text_from_pdf(pdf_path):
-    try:
-        doc = fitz.open(pdf_path)
-        text = "\n".join([page.get_text("text") for page in doc])
-        return text if text.strip() else "No extractable text found."
-    except Exception as e:
-        return f"Error extracting text from PDF: {str(e)}"
-
-# Function to extract text from Word files (.docx)
-def extract_text_from_docx(docx_path):
-    try:
-        doc = docx.Document(docx_path)
-        text = "\n".join([para.text for para in doc.paragraphs])
-        return text if text.strip() else "No extractable text found."
-    except Exception as e:
-        return f"Error extracting text from Word document: {str(e)}"
-
-# Function to extract text from PowerPoint files (.pptx)
-def extract_text_from_pptx(pptx_path):
-    try:
-        presentation = Presentation(pptx_path)
-        text = ""
-        for slide in presentation.slides:
-            for shape in slide.shapes:
-                if hasattr(shape, "text"):
-                    text += shape.text + "\n"
-        return text if text.strip() else "No extractable text found."
-    except Exception as e:
-        return f"Error extracting text from PowerPoint: {str(e)}"
-
-# Function to process documents safely
-def process_document(file):
-    try:
-        file_extension = os.path.splitext(file.name)[-1].lower()
-        if file_extension in [".png", ".jpg", ".jpeg"]:
-            return "Error: Images cannot be processed for text extraction."
-        if file_extension == ".pdf":
-            content = extract_text_from_pdf(file.name)
-        elif file_extension == ".docx":
-            content = extract_text_from_docx(file.name)
-        elif file_extension == ".pptx":
-            content = extract_text_from_pptx(file.name)
-        else:
-            encoding = detect_encoding(file.name)
-            with open(file.name, "r", encoding=encoding, errors="replace") as f:
-                content = f.read()
-        text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
-        documents = [Document(page_content=chunk) for chunk in text_splitter.split_text(content)]
-        vectorstore.add_documents(documents)
-        vectorstore.persist()  # <-- Persist changes after adding documents
-
-        quiz = generate_quiz(content)
-        return f"Document processed successfully (File Type: {file_extension}). Quiz generated:\n{quiz}"
-    except Exception as e:
-        return f"Error processing document: {str(e)}"
-
-# Function to handle speech-to-text conversion
-def transcribe_audio(audio):
-    sr, y = audio
-    if y.ndim > 1:
-        y = y.mean(axis=1)
-    y = y.astype(np.float32)
-    y /= np.max(np.abs(y))
-    return transcriber({"sampling_rate": sr, "raw": y})["text"]
-
-# Modify chat_with_groq function to return audio file for playback
-def chat_with_groq(user_input):
-    try:
-        # Retrieve relevant documents for additional context
-        relevant_docs = retrieve_documents(user_input)
-        context = "\n".join(relevant_docs) if relevant_docs else "No relevant documents found."
-
-        # Construct proper prompting with conversation history
-        system_prompt = "You are a helpful AI assistant. Answer questions accurately and concisely."
-        conversation_history = "\n".join(chat_memory[-10:])  # Keep the last 10 exchanges
-        prompt = f"{system_prompt}\n\nConversation History:\n{conversation_history}\n\nUser Input: {user_input}\n\nContext:\n{context}"
-
-        # Call the chat model
-        response = chat_model([HumanMessage(content=prompt)])
-
-        # Clean response to remove any unwanted formatting
-        cleaned_response_text = clean_response(response.content)
-
-        # Append conversation history
-        chat_memory.append(f"User: {user_input}")
-        chat_memory.append(f"AI: {cleaned_response_text}")
-
-        # Convert response to speech
-        audio_file = speech_playback(cleaned_response_text)
-
-        # Return both chat response and audio file path
-        return [(user_input, cleaned_response_text)], audio_file  # Return as a tuple
-    except Exception as e:
-        return [("Error", str(e))], None
-
-#__________________________________________________________________________________________________________________________
-
-def tutor_ai_chatbot():
-    """Main Gradio interface for the Tutor AI Chatbot."""
-    with gr.Blocks() as app:
-        gr.Markdown("# 📚 AI Tutor - We.(POC)")
-        gr.Markdown("An interactive Personal AI Tutor chatbot to help with your learning needs.")
-
-        # Chatbot Tab
-        with gr.Tab("AI Chatbot"):
-            with gr.Row():
-                with gr.Column(scale=3):
-                    chatbot = gr.Chatbot(height=500)  # Chatbot display area
-                    #chatbot = gr.Chatbot(height=500, type="messages")
-                    with gr.Row():
-                        msg = gr.Textbox(label="Ask a question", placeholder="Type your question here...")
-                        submit = gr.Button("Send")
-
-                    #with gr.Row():
-                    with gr.Column(scale=1):
-                        audio_input = gr.Audio(type="numpy", label="Record or Upload Audio")   # Audio input for speech-to-text
-
-
-                with gr.Column(scale=1):
-                   audio_playback = gr.Audio(label="Audio Response", type="filepath")
-
-            # Clear chat history button
-            clear_btn = gr.Button("Clear Chat")
-
-            # Handle chat interaction
-            submit.click(
-                chat_with_groq,
-                inputs=[msg],
-                outputs=[chatbot, audio_playback]
-            )
-
-            # Clear chat history function
-            def clear_chat_history():
-                return None, None
-
-            clear_btn.click(clear_chat_history, inputs=None, outputs=[chatbot, audio_playback])    #,audio_input
-
-            # Also allow Enter key to submit
-            msg.submit(
-                chat_with_groq,
-                inputs=[msg],
-                outputs=[chatbot, audio_playback]
-            )
-
-            # Add some examples of questions students might ask
-            with gr.Accordion("Example Questions", open=False):
-                gr.Examples(
-                    examples=[
-                        "Can you explain the concept of RLHF AI?",
-                        "What are AI transformers?",
-                        "What is MoE AI?",
-                        "What's gate networks AI?",
-                        "I am making a switch, please generating baking recipe?"
-                    ],
-                    inputs=msg
-                )
-
-        # Upload Notes & Generate Quiz Tab
-        with gr.Tab("Upload Notes & Generate Quiz"):
-            with gr.Row():
-                with gr.Column(scale=2):
-                    file_input = gr.File(label="Upload Lecture Notes (PDF, DOCX, PPTX)")
-                    #generate_btn = gr.Button("Generate Quiz")
-                with gr.Column(scale=3):
-                    quiz_output = gr.Textbox(label="Generated Quiz", lines=10)
-
-
-         # Introduction Video
-        with gr.Tab("Introduction Video"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    #with gr.Column(scale=1):  # Adjust scale for equal width
-                    gr.Markdown("### Welcome to the Introduction Video")  # Adding a heading
-                    gr.Markdown("Music from Xu Mengyuan - China-O, musician Xu Mengyuan YUAN! | 徐梦圆 - China-O 音乐人徐梦圆YUAN! ")  # Adding descriptive text
-                    #gr.Video("https://github.com/lesterchia1/AI_tutor/raw/main/We%20not%20me%20video.mp4", label="Introduction Video")
-                    #gr.Video("https://huggingface.co/spaces/Lesterchia174/FPOC2_AI-Tutor_Chatbot/raw/main/We%20not%20me%20video.mp4", label="Introduction Video")
-                    gr.Video("we_not_me_video.mp4", label="Introduction Video")
-
-            # Connect the button to the document processing function
-            audio_input.change(fn=transcribe_audio, inputs=audio_input, outputs=msg)  # transcribe and fill the msg textbox
-            file_input.change(process_document, inputs=file_input, outputs=quiz_output)
-
-
-    # Launch the application
-    app.launch(share=True)  # Set share=True to create a public link
-
-
-# Launch the AI chatbot
-if __name__ == "__main__":
-    tutor_ai_chatbot()