sync with remote

README.md

@@ -1,8 +1,8 @@
 ---
-title: Vqa Supervised Machine Learning
+title: VQA on Supervised Machine Learning
 emoji: 💻
 colorFrom: purple
-colorTo: purple
+colorTo: red
 sdk: streamlit
 sdk_version: 1.42.0
 app_file: app.py

app.py

@@ -0,0 +1,338 @@
+import streamlit as st
+import os
+import google.generativeai as genai
+from huggingface_hub import hf_hub_download
+import base64
+
+MODEL_ID = "gemini-2.0-flash-exp"  # Keep the model ID as is
+try:
+    api_key = os.getenv("GEMINI_API_KEY")
+    model_id = MODEL_ID
+    genai.configure(api_key=api_key)
+except Exception as e:
+    st.error(f"Error: {e}")
+    st.stop
+
+model = genai.GenerativeModel(MODEL_ID)
+chat = model.start_chat()
+
+def download_pdf():
+    """
+    Downloads the PDF file from the Hugging Face Hub using the correct repo path and filename.
+    """
+    try:
+        hf_token = os.getenv("HF_TOKEN")
+        repo_id = "louiecerv/vqa_machine_learning_dataset"  # Corrected dataset repo path
+        filename = "Supervised_Learning_Alogirthms.pdf"
+        filepath = hf_hub_download(repo_id=repo_id, filename=filename, token=hf_token, repo_type="dataset")
+        return filepath
+    except Exception as e:
+        st.error(f"Failed to download PDF from Hugging Face Hub: {e}")
+        st.stop()  # Stop if the download fails
+
+# Initialize conversation history in Streamlit session state
+if "conversation_history" not in st.session_state:
+    st.session_state.conversation_history = []
+if "uploaded_file_part" not in st.session_state:  # Store the file *part*
+    st.session_state.uploaded_file_part = None
+if "uploaded_pdf_path" not in st.session_state:
+    st.session_state.uploaded_pdf_path = download_pdf() 
+
+def multimodal_prompt(pdf_path, text_prompt):
+    """
+    Sends a multimodal prompt to Gemini, handling file uploads efficiently.
+    Args:
+        pdf_path: The path to the PDF file.
+        text_prompt: The text prompt for the model.
+    Returns:
+        The model's response as a string, or an error message.
+    """
+    try:
+        if st.session_state.uploaded_file_part is None:  # First time, upload
+            pdf_part = genai.upload_file(pdf_path, mime_type="application/pdf")
+            st.session_state.uploaded_file_part = pdf_part
+            prompt = [text_prompt, pdf_part] # First turn includes the actual file
+        else: # Subsequent turns, reference the file
+            
+            prompt = [text_prompt, st.session_state.uploaded_file_part] # Subsequent turns include the file reference
+
+        response = chat.send_message(prompt)
+
+        # Update conversation history
+        st.session_state.conversation_history.append({"role": "user", "content": text_prompt, "has_pdf": True})
+        st.session_state.conversation_history.append({"role": "assistant", "content": response.text})
+        return response.text
+
+    except Exception as e:
+        return f"An error occurred: {e}"
+
+def display_download_button(file_path, file_name):
+    try:
+        with open(file_path, "rb") as f:
+            file_bytes = f.read()
+        b64 = base64.b64encode(file_bytes).decode()
+        href = f'<a href="data:application/pdf;base64,{b64}" download="{file_name}">Download the source document (PDF)</a>'
+        st.markdown(href, unsafe_allow_html=True)
+    except FileNotFoundError:
+        st.error("File not found for download.")
+    except Exception as e:
+        st.error(f"Error during download: {e}")
+
+# Define the ML Models
+roles = ["Linear Regression", "Logistic Regression", 
+         "Naive Bayes", "Support Vector Machine", "Decision Tree", 
+         "Random Forest", "K-Nearest Neighbor", "Gradient Boosting Manchines", "Neural Network"]
+
+# --- Sidebar ---
+st.sidebar.title("🤖 Visual Q and A")
+selected_model = st.sidebar.selectbox("Select the ML Model", roles)
+
+# --- Main Page ---
+st.title("📚 VQA on the Supervised Machine Learning Algorithms")
+about = """
+
+**How to use this App**
+This app leverages Gemini 2.0 to provide insights on the provided document.
+Select a question from the dropdown menu or enter your own question to get 
+Gemini's generated response based on the provided document.
+"""
+
+with st.expander("How to use this App"):
+    st.markdown(about)
+
+# --- Q and A Tab ---
+st.header("Questions and Answers")
+
+# Generate 5 questions based on the selected model
+if selected_model == "Linear Regression":
+    questions = [
+    "What is the fundamental concept of Linear Regression and how does it connect to the goal of predicting a target variable?",
+    "Explain the different components of the Linear Regression equation: y, x1, x2,..., xn, β0, β1, β2,..., βn, and e.",
+    "How does the Ordinary Least Squares (OLS) method help in finding the best-fit line in Linear Regression?",
+    "What is the role of the error term (e) in Linear Regression and how does it affect the accuracy of predictions?",
+    "What are the primary advantages of using Linear Regression, especially in terms of simplicity and efficiency?",
+    "In what situations would the assumption of a linear relationship in Linear Regression become a major drawback?",
+    "How do outliers impact a Linear Regression model and what methods can be used to reduce their influence?",
+    "Explain the concept of multicollinearity in the context of Linear Regression and its potential effects on model performance.",
+    "Can you provide real-world examples where Linear Regression is a suitable and effective solution?",
+    "How can Linear Regression be used to predict a continuous value, such as the price of a house or stock?",
+    "What are the limitations of Linear Regression when dealing with non-linear or categorical data?",
+    "How does Linear Regression differ from Logistic Regression, especially in terms of the types of problems they address?",
+    "What are the relative strengths and weaknesses of Linear Regression compared to more complex models like Support Vector Machines (SVM) or Decision Trees?",
+    "How can Linear Regression be adapted or modified to handle non-linear relationships between variables?",
+    "What techniques can be used for feature selection or dimensionality reduction to enhance the performance of Linear Regression?",
+    "How can regularization methods like Ridge or Lasso regression prevent overfitting in Linear Regression models?",
+    "What are the consequences of heteroscedasticity in Linear Regression and how can it be dealt with?",
+    "What metrics are commonly used to evaluate the performance of a Linear Regression model, such as R-squared or mean squared error?",
+    "How can the coefficients (β1, β2,..., βn) in a Linear Regression model be interpreted to understand the effect of each independent variable on the target variable?",
+    "Can you explain the limitations of Linear Regression when dealing with high-dimensional data or datasets with a large number of features?"
+    ]
+if selected_model == "Logistic Regression":
+    questions =[
+    "What is the fundamental concept of Logistic Regression and how does it apply to binary classification problems?",
+    "Explain the role of the sigmoid function in Logistic Regression and how it helps predict probabilities.",
+    "How does Logistic Regression determine the threshold for classifying an instance into one of the two classes?",
+    "What are the main advantages of using Logistic Regression, particularly its interpretability and ability to provide probability estimates?",
+    "In what situations would the inability of Logistic Regression to handle non-linear relationships effectively become a significant limitation?",
+    "How does Logistic Regression handle irrelevant features in the dataset and how does it affect the model's performance?",
+    "Can you provide real-world examples where Logistic Regression is commonly used for classification tasks?",
+    "How does Logistic Regression compare to Linear Regression in terms of their applications and the types of problems they address?",
+    "What are the limitations of Logistic Regression when dealing with multi-class classification problems?",
+    "How can Logistic Regression be extended or modified to handle non-linear decision boundaries?",
+    "What techniques can be used to address overfitting in Logistic Regression models?",
+    "What metrics are commonly used to evaluate the performance of a Logistic Regression model, such as accuracy, precision, recall, and F1-score?",
+    "How can the coefficients in a Logistic Regression model be interpreted to understand the influence of each independent variable on the predicted probabilities?",
+    "What are the implications of imbalanced datasets in Logistic Regression and how can they be addressed?",
+    "How does Logistic Regression handle categorical features and how are they incorporated into the model?",
+    "What are the advantages and disadvantages of using Logistic Regression compared to other classification models like Support Vector Machines or Decision Trees?",
+    "How can regularization techniques be applied in Logistic Regression to improve model generalization?",
+    "What are the challenges in interpreting the results of Logistic Regression when dealing with high-dimensional data?",
+    "How can Logistic Regression be used in applications like spam detection, fraud detection, or medical diagnosis?",
+    "What are the ethical considerations when using Logistic Regression in sensitive domains like healthcare or criminal justice?"
+    ]
+if selected_model == "Naive Bayes":
+    questions = [
+    "Explain the underlying principle of Naïve Bayes and its connection to Bayes' Theorem.",
+    "What is the assumption of independence among predictors in Naïve Bayes, and how does it affect the model's performance?",
+    "Describe how Naïve Bayes calculates the probability of a class C given features X, using the formula and explaining each term.",
+    "What are the key advantages of using Naïve Bayes, such as speed, scalability, and robustness to irrelevant features?",
+    "In what situations would the assumption of feature independence in Naïve Bayes become a significant limitation?",
+    "How does Naïve Bayes handle continuous features and incorporate them into the model?",
+    "Provide real-world examples where Naïve Bayes is commonly used for classification tasks.",
+    "Compare Naïve Bayes to other classification models like Logistic Regression or Support Vector Machines, considering accuracy and efficiency.",
+    "What are the different variations of Naïve Bayes, like Gaussian Naïve Bayes or Multinomial Naïve Bayes, and how do their applications differ?",
+    "Explain how Naïve Bayes can be used in text classification, spam filtering, or sentiment analysis.",
+    "What techniques can improve the performance of Naïve Bayes when dealing with correlated features?",
+    "How does Naïve Bayes handle missing values in the dataset?",
+    "What are the limitations of Naïve Bayes when dealing with high-dimensional data or datasets with many features?",
+    "How can Naïve Bayes be combined with other machine learning techniques, such as ensemble methods?",
+    "What ethical considerations arise when using Naïve Bayes in sensitive areas like healthcare or finance?",
+    "How does Naïve Bayes handle imbalanced datasets, and how can this issue be addressed?",
+    "What challenges are associated with interpreting the results of Naïve Bayes, especially with many features?",
+    "How can Naïve Bayes be used in real-time applications that require quick predictions?",
+    "Compare the advantages and disadvantages of using Naïve Bayes versus deep learning models for classification tasks.",
+    "How can Naïve Bayes be adapted or modified to handle both continuous and categorical features together?"
+]
+if selected_model == "Support Vector Machine":
+    questions = [
+    "What is the core concept behind Support Vector Machines (SVM) and how does it relate to finding the optimal hyperplane for classification?",
+    "Explain the concept of a margin in SVM and its importance in achieving good generalization performance.",
+    "How does SVM handle non-linearly separable data using the Kernel Trick?",
+    "What are the different types of kernel functions used in SVM, such as linear, polynomial, and radial basis function (RBF) kernels?",
+    "What are the main advantages of using SVM, particularly its effectiveness in high-dimensional spaces and ability to handle non-linear classification?",
+    "What are the limitations of SVM, such as its computational cost for large datasets and difficulty in tuning hyperparameters?",
+    "How does SVM compare to other classification models like Logistic Regression or Decision Trees in terms of accuracy and complexity?",
+    "How can SVM be used for both classification and regression tasks?",
+    "What are the challenges in choosing the appropriate kernel function for an SVM model?",
+    "How does SVM handle outliers in the dataset and how do they affect the model's performance?",
+    "What are the different optimization algorithms used to train SVM models, such as quadratic programming or gradient descent?",
+    "How can SVM be used in applications like image recognition, text classification, or bioinformatics?",
+    "What are the implications of imbalanced datasets in SVM and how can they be addressed?",
+    "How does SVM handle categorical features and how are they incorporated into the model?",
+    "What are the advantages and disadvantages of using SVM compared to deep learning models for classification tasks?",
+    "How can SVM be used in conjunction with other machine learning techniques, such as ensemble methods or feature selection?",
+    "What are the ethical considerations when using SVM in sensitive domains like healthcare or finance?",
+    "How can SVM be adapted or modified to handle online learning scenarios where data arrives sequentially?",
+    "What are the challenges in interpreting the results of SVM, particularly when using non-linear kernel functions?",
+    "How can SVM be used in real-time applications where quick predictions are required?"
+]
+if selected_model == "Decision Tree":
+    questions = [
+    "What is the fundamental concept of a Decision Tree and how does it use a tree-like structure for classification or regression?",
+    "Explain the process of constructing a Decision Tree, including how the data is recursively split based on features.",
+    "What are the different splitting criteria used in Decision Trees, such as information gain, Gini impurity, or entropy?",
+    "What are the advantages of using Decision Trees, such as their interpretability and ability to handle both numerical and categorical data?",
+    "What are the disadvantages of Decision Trees, such as their tendency to overfit the training data?",
+    "How can overfitting be prevented in Decision Trees using techniques like pruning or setting limits on the tree depth?",
+    "How can Decision Trees be used for both classification and regression tasks?",
+    "How do you interpret the results of a Decision Tree and understand the decision rules it has learned?",
+    "What are the limitations of Decision Trees when dealing with complex relationships or high-dimensional data?",
+    "How does a Decision Tree compare to other models like Linear Regression or Support Vector Machines in terms of interpretability and accuracy?",
+    "What are the different ensemble methods that utilize Decision Trees, such as Random Forest or Gradient Boosting Machines?",
+    "How can Decision Trees be used in applications like customer churn prediction, medical diagnosis, or fraud detection?",
+    "How can Decision Trees handle missing values in the dataset?",
+    "What are the ethical considerations when using Decision Trees in sensitive domains like healthcare or education?",
+    "How can Decision Trees be used in conjunction with other machine learning techniques, such as feature selection or data visualization?",
+    "What are the challenges in interpreting the results of Decision Trees when dealing with a large number of features or complex branching structures?",
+    "How can Decision Trees be adapted or modified to handle imbalanced datasets?",
+    "What are the advantages and disadvantages of using Decision Trees compared to deep learning models for classification or regression tasks?",
+    "How can Decision Trees be used in real-time applications where quick predictions are required?",
+    "How can Decision Trees be used to identify important features or variables that contribute most to the prediction task?"
+    ]
+if selected_model == "Random Forest":
+    questions = [
+    "What is the fundamental concept of a Random Forest and how does it differ from a single Decision Tree?",
+    "Explain the process of constructing a Random Forest, including the steps involved in creating multiple Decision Trees and combining their predictions.",
+    "How does the Random Forest algorithm utilize bagging (bootstrap aggregating) to enhance the diversity and robustness of the model?",
+    "What are the key parameters that influence the performance of a Random Forest, such as the number of trees, tree depth, and number of features considered at each split?",
+    "How does Random Forest achieve better generalization performance and reduce overfitting compared to a single Decision Tree?",
+    "Explain the role of feature randomness in Random Forest and how it helps to decorrelate the trees and improve model accuracy.",
+    "What are the main advantages of using Random Forest, such as its ability to handle high-dimensional data, missing values, and irrelevant features?",
+    "What are the limitations of Random Forest, such as its computational cost and potential lack of interpretability compared to a single Decision Tree?",
+    "How does Random Forest compare to other ensemble methods like Gradient Boosting Machines in terms of accuracy, complexity, and interpretability?",
+    "How can Random Forest be used for both classification and regression tasks, and what are the differences in its implementation for each type of problem?",
+    "How do you interpret the results of a Random Forest, including understanding feature importance and visualizing the decision boundaries?",
+    "What metrics are commonly used to evaluate the performance of a Random Forest model, such as accuracy, precision, recall, F1-score, and AUC?",
+    "How can Random Forest be used in real-world applications, such as image classification, object detection, fraud detection, and bioinformatics?",
+    "How does Random Forest handle imbalanced datasets and what techniques can be used to address this issue?",
+    "What are the ethical considerations when using Random Forest in sensitive domains, such as healthcare or finance?",
+    "How can Random Forest be integrated with other machine learning techniques, such as feature selection, dimensionality reduction, or hyperparameter optimization?",
+    "What are the challenges in deploying and maintaining a Random Forest model in a production environment, particularly in terms of scalability and resource utilization?",
+    "How can Random Forest be adapted or modified to handle online learning scenarios where data arrives sequentially?",
+    "What are the ongoing research directions and advancements in the field of Random Forest, such as exploring new tree diversity techniques or incorporating explainability features?",
+    "What are the advantages and disadvantages of using Random Forest compared to deep learning models for various machine learning tasks?"
+    ]
+if selected_model == "K-Nearest Neighbor":
+    questions = [
+    "What is the core concept behind the K-Nearest Neighbors (KNN) algorithm and how does it use similarity metrics for classification or regression?",
+    "Explain the process of classifying a new instance using KNN, including how the algorithm calculates distances to the K nearest neighbors.",
+    "What are the different distance metrics used in KNN, such as Euclidean distance, Manhattan distance, or cosine similarity?",
+    "How does the choice of K value affect the model's performance and generalization ability in KNN?",
+    "What are the main advantages of using KNN, such as its simplicity, flexibility, and ability to handle non-linear decision boundaries?",
+    "What are the limitations of KNN, such as its computational cost for large datasets, sensitivity to irrelevant features, and lack of interpretability?",
+    "How does KNN compare to other classification models like Logistic Regression or Support Vector Machines in terms of accuracy and efficiency?",
+    "How can KNN be used for both classification and regression tasks, and what are the differences in its implementation for each type of problem?",
+    "What are the challenges in choosing an appropriate distance metric for KNN, especially when dealing with high-dimensional data or mixed data types?",
+    "How does KNN handle categorical features and how are they incorporated into the model?",
+    "What are the different strategies for handling missing values in the dataset when using KNN?",
+    "How can KNN be used in applications like recommendation systems, anomaly detection, or clustering?",
+    "What are the implications of imbalanced datasets in KNN and how can they be addressed?",
+    "What are the ethical considerations when using KNN in sensitive domains like healthcare or finance?",
+    "How can KNN be integrated with other machine learning techniques, such as feature selection, dimensionality reduction, or ensemble methods?",
+    "What are the challenges in interpreting the results of KNN, particularly when dealing with a large number of features or instances?",
+    "How can KNN be adapted or modified to handle online learning scenarios where data arrives sequentially?",
+    "What are the advantages and disadvantages of using KNN compared to deep learning models for classification or regression tasks?",
+    "How can KNN be used in real-time applications where quick predictions are required?",
+    "What are the ongoing research directions and advancements in the field of KNN, such as exploring new distance metrics or improving the algorithm's scalability and efficiency?"
+    ]   
+if selected_model == "Gradient Boosting Manchines":
+    questions = [
+    "What is the core concept behind Gradient Boosting Machines (GBM) and how does it use an ensemble of weak learners to create a strong predictive model?",
+    "Explain the process of training a Gradient Boosting Machine, including how the algorithm sequentially adds trees to correct errors made by the previous trees.",
+    "How does GBM handle the issue of overfitting by using techniques like shrinkage (learning rate) and tree pruning?",
+    "What are the different loss functions used in GBM, such as mean squared error, log loss, or hinge loss, and how do they affect the model's performance?",
+    "What are the main advantages of using GBM, such as its high predictive accuracy, robustness to outliers, and ability to capture complex relationships in the data?",
+    "What are the limitations of GBM, such as its computational cost, sensitivity to hyperparameters, and potential overfitting on noisy data?",
+    "How does GBM compare to other ensemble methods like Random Forest or AdaBoost in terms of accuracy, complexity, and interpretability?",
+    "How can GBM be used for both classification and regression tasks, and what are the differences in its implementation for each type of problem?",
+    "What are the challenges in tuning hyperparameters for GBM, such as the number of trees, tree depth, learning rate, and subsampling rate?",
+    "How does GBM handle missing values in the dataset and what techniques can be used to impute or ignore them during training?",
+    "What are the different feature importance metrics used in GBM, such as gain, cover, or frequency, and how do they help interpret the model's predictions?",
+    "What are the different regularization techniques that can be applied to GBM, such as L1 or L2 regularization, to prevent overfitting?",
+    "How can GBM be used in real-world applications like click-through rate prediction, customer churn analysis, or fraud detection?",
+    "What are the implications of imbalanced datasets in GBM and how can they be addressed using techniques like class weights or resampling?",
+    "What are the ethical considerations when using GBM in sensitive domains like healthcare or finance?",
+    "How can GBM be integrated with other machine learning techniques, such as feature selection, dimensionality reduction, or hyperparameter optimization?",
+    "What are the challenges in interpreting the results of GBM, particularly when dealing with a large number of features or complex interactions?",
+    "How can GBM be adapted or modified to handle online learning scenarios where data arrives sequentially?",
+    "What are the ongoing research directions and advancements in the field of GBM, such as exploring new loss functions or improving the algorithm's scalability and efficiency?",
+    "What are the advantages and disadvantages of using GBM compared to deep learning models for various machine learning tasks?"
+    ]   
+if selected_model == "Neural Network":
+    questions = [
+    "What is the core concept behind Neural Networks and how do they mimic the structure and function of the human brain?",
+    "Explain the architecture of a basic feedforward Neural Network, including input layer, hidden layers, activation functions, and output layer.",
+    "How does a Neural Network learn from data using techniques like backpropagation, gradient descent, and stochastic gradient descent?",
+    "What are the different types of Neural Networks, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or Generative Adversarial Networks (GANs)?",
+    "What are the main advantages of using Neural Networks, such as their ability to learn complex patterns, generalize well to new data, and adapt to different tasks?",
+    "What are the limitations of Neural Networks, such as their computational cost, sensitivity to hyperparameters, and potential overfitting on small datasets?",
+    "How does a Neural Network compare to other machine learning models like Support Vector Machines or Decision Trees in terms of accuracy, complexity, and interpretability?",
+    "How can Neural Networks be used for both classification and regression tasks, and what are the differences in their implementation for each type of problem?",
+    "What are the challenges in designing the architecture of a Neural Network, such as choosing the number of layers, neurons per layer, and activation functions?",
+    "How does a Neural Network handle categorical features and how are they encoded into the model?",
+    "What are the different activation functions used in Neural Networks, such as sigmoid, tanh, ReLU, or softmax, and how do they affect the model's performance?",
+    "What are the different regularization techniques that can be applied to Neural Networks, such as dropout, weight decay, or early stopping, to prevent overfitting?",
+    "How can Neural Networks be used in real-world applications like image recognition, natural language processing, or reinforcement learning?",
+    "What are the implications of imbalanced datasets in Neural Networks and how can they be addressed using techniques like class weights or oversampling?",
+    "What are the ethical considerations when using Neural Networks in sensitive domains like healthcare or finance?",
+    "How can Neural Networks be integrated with other machine learning techniques, such as transfer learning, feature extraction, or hyperparameter optimization?",
+    "What are the challenges in interpreting the results of Neural Networks, particularly when dealing with deep architectures or complex data transformations?",
+    "How can Neural Networks be adapted or modified to handle online learning scenarios where data arrives sequentially?",
+    "What are the ongoing research directions and advancements in the field of Neural Networks, such as exploring new architectures, optimization algorithms, or explainability features?",
+    "What are the advantages and disadvantages of using Neural Networks compared to traditional machine learning models for various tasks?"
+    ]   
+
+# Create a selection box
+selected_question = st.selectbox("Choose a question", questions)
+
+# Display a checkbox
+if st.checkbox('Check this box to ask a question not listed above'):
+    # If the checkbox is checked, display a text box
+    selected_question = st.text_input('Enter a question')
+
+if st.button("Ask AI"):
+    with st.spinner("AI is thinking..."):
+        if st.session_state.uploaded_pdf_path is None:
+            st.session_state.uploaded_pdf_path = download_pdf()
+
+        filepath = st.session_state.uploaded_pdf_path
+        text_prompt = f"Use the provided document focus on rhe topic: {selected_model} to answer the following question: {selected_question}.  Use your own knowledge as well as sources from the web and the provided document. Always cite your sourcss."
+        response = multimodal_prompt(filepath, text_prompt)  # Use the downloaded filepath
+        st.markdown(f"**Question:** {selected_question}")
+        st.markdown(f"**Response:** {response}")
+
+if st.session_state.uploaded_pdf_path:
+    display_download_button(st.session_state.uploaded_pdf_path, "Supervised_Learning_Alogirthms.pdf")
+
+st.markdown("[Visit our Hugging Face Space!](https://huggingface.co/wvsuaidev)")
+st.markdown("© 2025 WVSU AI Dev Team 🤖 ✨")

requirements.txt

@@ -0,0 +1,3 @@
+streamlit
+huggingface_hub
+google-generativeai