Initial commit: Math Problem Solver application

.gitignore

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+# Environment variables
+.env
+.env.local
+
+# Virtual Environment
+venv/
+env/
+ENV/
+
+# Python cache files
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Streamlit specific
+.streamlit/secrets.toml
+
+# Logs
+*.log
+logs/
+
+# IDE specific files
+.idea/
+.vscode/
+*.swp
+*.swo
+.DS_Store

app.py

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+# Math Problem Solver 
+# A Streamlit application that solves math problems and searches for information using Google Gemma 2 model through Groq API
+
+import streamlit as st
+import os
+from langchain_groq import ChatGroq
+from langchain.agents import Tool, initialize_agent
+from langchain.agents.agent_types import AgentType
+from langchain.prompts import PromptTemplate
+from langchain_community.utilities import WikipediaAPIWrapper, ArxivAPIWrapper, DuckDuckGoSearchRun
+from langchain_community.tools import WikipediaQueryRun, ArxivQueryRun
+from langchain.callbacks import StreamlitCallbackHandler
+from langchain.chains import LLMMathChain, LLMChain
+from langchain.schema import SystemMessage
+from dotenv import load_dotenv
+
+# Load environment variables from .env file
+load_dotenv()
+
+# Page configuration
+st.set_page_config(
+    page_title="Math Problem Solver",
+    page_icon="🧮",
+    layout="wide"
+)
+
+# App title and description
+st.title("🧮 Math Problem Solver")
+st.markdown("""
+This application solves mathematical problems and provides step-by-step explanations using Groq's AI models.
+It can handle various math topics including:
+- Arithmetic calculations
+- Algebra
+- Geometry
+- Statistics
+- Word problems
+- And more!
+""")
+
+# Sidebar configuration
+st.sidebar.title("⚙️ Configuration")
+st.sidebar.markdown("---")
+
+# API key input - use environment variable if available
+default_api_key = os.getenv("GROQ_API_KEY", "")
+groq_api_key = st.sidebar.text_input(
+    label="Groq API Key", 
+    value=default_api_key,
+    type="password",
+    help="Get your API key from https://console.groq.com/keys"
+)
+
+# Model selection
+model_options = {
+    "gemma2-9b-it": "Gemma 2 9B (Fast)",
+    "llama3-8b-8192": "Llama 3 8B (Balanced)",
+    "llama3-70b-8192": "Llama 3 70B (Powerful)",
+    "mixtral-8x7b-32768": "Mixtral 8x7B (Comprehensive)"
+}
+selected_model = st.sidebar.selectbox(
+    "Select AI model:", 
+    list(model_options.keys()), 
+    format_func=lambda x: model_options[x],
+    index=0
+)
+
+# Advanced options
+with st.sidebar.expander("Advanced Options"):
+    temperature = st.slider("Temperature", min_value=0.0, max_value=1.0, value=0.2, step=0.1,
+                           help="Lower values make responses more deterministic, higher values more creative")
+    show_reasoning = st.checkbox("Show AI reasoning steps", value=True,
+                                help="Display the AI's step-by-step reasoning process")
+
+# Initialize LLM if API key is provided
+if not groq_api_key:
+    st.info("Please add your Groq API key in the sidebar to continue")
+    st.sidebar.info("Don't have an API key? Sign up at [console.groq.com](https://console.groq.com)")
+    
+    # Example problem display
+    st.markdown("### Example Problem")
+    st.markdown("""
+    ```
+    I have 5 bananas and 7 grapes. I eat 2 bananas and give away 3 grapes. 
+    Then I buy a dozen apples and 2 packs of blueberries. 
+    Each pack of blueberries contains 25 berries. 
+    How many total pieces of fruit do I have at the end?
+    ```
+    """)
+    st.stop()
+
+# Initialize LLM
+try:
+    llm = ChatGroq(
+        model=selected_model, 
+        groq_api_key=groq_api_key,
+        temperature=temperature,
+        streaming=True
+    )
+except Exception as e:
+    st.error(f"Error initializing the AI model: {str(e)}")
+    st.stop()
+
+# Initialize tools
+def setup_tools():
+    """Initialize and return tools for the agent"""
+    
+    # Wikipedia tool
+    wiki_wrapper = WikipediaAPIWrapper(top_k_results=2)
+    wiki_tool = WikipediaQueryRun(api_wrapper=wiki_wrapper)
+    
+    # ArXiv tool for academic papers
+    arxiv_wrapper = ArxivAPIWrapper(top_k_results=1)
+    arxiv_tool = ArxivQueryRun(api_wrapper=arxiv_wrapper)
+    
+    # DuckDuckGo search tool
+    search_tool = DuckDuckGoSearchRun()
+    
+    # Math calculation tool
+    llm_math_chain = LLMMathChain.from_llm(llm=llm)
+    calculator = Tool(
+        func=llm_math_chain.run,
+        name="Calculator",
+        description="A tool for performing mathematical calculations. Input only mathematical expressions."
+    )
+    
+    # Reasoning tool for detailed explanations
+    prompt = """
+    You're an expert mathematics teacher. Solve the following problem step by step:
+    
+    {question}
+    
+    First, identify what information is given and what is being asked.
+    Then, lay out a clear strategy for solving the problem.
+    Show your work carefully, with each step clearly labeled.
+    Provide a final answer with appropriate units if applicable.
+    
+    Your solution:
+    """
+    prompt_template = PromptTemplate(
+        input_variables=["question"],
+        template=prompt
+    )
+    chain = LLMChain(llm=llm, prompt=prompt_template)
+    reasoning_tool = Tool(
+        name="MathReasoning",
+        func=chain.run,
+        description="A tool for solving math problems step-by-step with detailed explanations."
+    )
+    
+    return [wiki_tool, arxiv_tool, search_tool, calculator, reasoning_tool]
+
+# Set up the agent
+def initialize_math_agent(tools):
+    """Initialize the math problem solving agent"""
+    system_message = SystemMessage(content="""
+    You are an expert mathematics tutor and problem solver. Your goal is to:
+    1. Solve mathematical problems accurately
+    2. Provide clear, step-by-step explanations
+    3. Use the appropriate tools for calculations and information gathering
+    4. Organize your answers in a structured format with headings
+    5. Include formulas and equations where relevant
+    
+    For math problems, always show your work and explain your thinking.
+    For information queries, cite your sources where appropriate.
+    """)
+    
+    return initialize_agent(
+        tools=tools,
+        llm=llm,
+        agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,
+        verbose=show_reasoning,
+        handle_parsing_errors=True,
+        system_message=system_message,
+        early_stopping_method="generate"
+    )
+
+# Initialize chat session state
+if "messages" not in st.session_state:
+    st.session_state["messages"] = [
+        {"role": "assistant", "content": "Hi, I'm your Math Problem Solver! Ask me any math question or problem, and I'll solve it step-by-step."}
+    ]
+
+# Display chat history
+for msg in st.session_state.messages:
+    st.chat_message(msg["role"]).write(msg['content'])
+
+# Example problems for the user to try
+example_problems = [
+    "Solve for x: 2x + 5 = 15",
+    "Find the area of a circle with radius 4 cm",
+    "If a train travels at 60 mph and takes 3 hours to reach its destination, how far did it travel?",
+    "I have 5 bananas and 7 grapes. I eat 2 bananas and give away 3 grapes. Then I buy a dozen apples and 2 packs of blueberries. Each pack contains 25 berries. How many total pieces of fruit do I have at the end?"
+]
+
+# User input area with tabs for different input methods
+tab1, tab2 = st.tabs(["Enter Your Question", "Try Examples"])
+
+with tab1:
+    question = st.text_area(
+        "Type your math problem or question:",
+        placeholder="Enter your math problem here...",
+        height=100
+    )
+    solve_button = st.button("Solve Problem", type="primary")
+
+with tab2:
+    st.write("Select an example problem:")
+    for i, example in enumerate(example_problems):
+        if st.button(f"Example {i+1}", key=f"example_{i}"):
+            question = example
+            solve_button = True
+            st.session_state.messages.append({"role": "user", "content": question})
+            st.experimental_rerun()
+
+# Process the request when button is clicked
+if solve_button and question:
+    # Add user message to chat
+    st.session_state.messages.append({"role": "user", "content": question})
+    st.chat_message("user").write(question)
+    
+    # Initialize tools and agent
+    tools = setup_tools()
+    assistant_agent = initialize_math_agent(tools)
+    
+    # Generate response with callback handler for streaming
+    with st.chat_message("assistant"):
+        st_cb = StreamlitCallbackHandler(st.container(), expand_new_thoughts=show_reasoning)
+        try:
+            response = assistant_agent.run(question, callbacks=[st_cb])
+            st.write(response)
+            
+            # Add assistant response to chat
+            st.session_state.messages.append({'role': 'assistant', "content": response})
+        except Exception as e:
+            error_msg = f"Error generating response: {str(e)}"
+            st.error(error_msg)
+            st.session_state.messages.append({'role': 'assistant', "content": error_msg})
+
+# Add footer
+st.sidebar.markdown("---")
+st.sidebar.markdown("### About")
+st.sidebar.info(
+    "This app uses Groq's AI models to solve math problems "
+    "and provide step-by-step explanations. It can handle a wide range "
+    "of mathematical topics from basic arithmetic to advanced calculus."
+)
+st.sidebar.markdown("### Feedback")
+st.sidebar.markdown("[Report an issue](https://github.com/yourusername/math-problem-solver/issues)")

readme.md

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+# 🧮 Math Problem Solver
+
+## Overview
+
+Math Problem Solver is an AI-powered Streamlit application that helps users solve mathematical problems with step-by-step explanations. The application uses Groq's AI models (including Google Gemma 2) to provide detailed solutions to a wide range of mathematical topics.
+
+## Getting Started
+
+### Prerequisites
+
+- Python 3.8+
+- Groq API key (get one at [console.groq.com](https://console.groq.com/keys))
+
+### Installation
+
+1. Clone this repository:
+   ```bash
+   git clone https://github.com/yourusername/math-problem-solver.git
+   cd math-problem-solver
+   ```
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Create a `.env` file in the project root and add your Groq API key:
+   ```
+   GROQ_API_KEY=your_api_key_here
+   ```
+
+4. Run the application:
+   ```bash
+   streamlit run app.py
+   ```
+
+5. Open your browser and navigate to `http://localhost:8501`
+
+## Usage
+
+1. Enter your Groq API key in the sidebar (if not already set in `.env`)
+2. Select your preferred AI model
+3. Type your math problem in the text area or choose from the example problems
+4. Click "Solve Problem" to get a detailed solution
+5. Review the step-by-step explanation
+
+### Example Problems
+
+- Solve for x: 2x + 5 = 15
+- Find the area of a circle with radius 4 cm
+- If a train travels at 60 mph and takes 3 hours to reach its destination, how far did it travel?
+- Word problems involving multiple operations and conversions
+
+## Technical Details
+
+This application uses:
+
+- **Streamlit**: For the web interface
+- **LangChain**: For agent orchestration and tool integration
+- **Groq API**: For AI model inference
+- **Google Gemma 2**: Primary AI model for solving math problems
+- **Wikipedia API**: For foundational knowledge
+- **arXiv API**: For academic mathematical concepts
+- **DuckDuckGo Search**: For web search capabilities
+
+The application uses a zero-shot agent that strategically chooses which tool to use based on the problem type.
+
+## Advanced Configuration
+
+In the "Advanced Options" section of the sidebar, you can:
+
+- Adjust the temperature (creativity) of the AI responses
+- Toggle the visibility of AI reasoning steps
+- Select different models for different types of problems
+
+## Development
+
+### Project Structure
+
+```
+math-problem-solver/
+├── app.py                  # Main application file
+├── .env                    # Environment variables (git-ignored)
+├── requirements.txt        # Dependencies
+├── README.md               # Project documentation
+├── LICENSE                 # MIT License
+├── screenshots/            # App screenshots
+│   └── app_screenshot.png  # Main app screenshot
+└── .gitignore              # Git ignore configuration
+```

requirements.txt

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+# Core dependencies
+streamlit>=1.28.0
+langchain>=0.1.0
+langchain-groq>=0.1.0
+python-dotenv>=1.0.0
+
+# Tool dependencies
+langchain-community>=0.0.10
+wikipedia>=1.4.0
+arxiv>=1.4.7
+duckduckgo-search>=3.9.0
+numpy>=1.24.0
+sympy>=1.12.0
+
+# Utility dependencies
+protobuf>=4.25.1