Adding all to hf branch

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from llama_cpp import Llama
+from huggingface_hub import hf_hub_download
+import io
+import os
+
+# Page configuration
+st.set_page_config(
+    page_title="📊 LLM Data Analyzer",
+    page_icon="📊",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for better UI
+st.markdown("""
+    <style>
+    .main {
+        padding: 0rem 1rem;
+    }
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 2px;
+    }
+    </style>
+    """, unsafe_allow_html=True)
+
+# Title and description
+st.title("📊 LLM Data Analyzer")
+st.markdown("""
+    Analyze your CSV/Excel files and chat with an AI assistant powered by Llama 2.
+    This app runs on **free Hugging Face CPU** - response time ~5-10 seconds per query.
+    """)
+
+# Cache model loading to avoid reloading
+@st.cache_resource
+def load_llm_model():
+    """Load Llama 2 model from Hugging Face Hub"""
+    st.info("📥 Downloading model (first time only, ~4GB)... This may take 2-3 minutes.")
+    
+    try:
+        model_path = hf_hub_download(
+            repo_id="TheBloke/Llama-2-7B-Chat-GGUF",
+            filename="llama-2-7b-chat.Q4_K_M.gguf"
+        )
+        
+        llm = Llama(
+            model_path=model_path,
+            n_ctx=2048,
+            n_threads=4,
+            n_gpu_layers=0,  # CPU only (free tier)
+            verbose=False
+        )
+        return llm
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# Load model
+llm = load_llm_model()
+
+if llm is None:
+    st.error("Failed to load model. Please refresh the page.")
+    st.stop()
+
+st.success("✅ Model loaded successfully!")
+
+# Create tabs
+tab1, tab2, tab3 = st.tabs(["📤 Upload & Analyze", "💬 Chat", "📊 About"])
+
+# ============================================================================
+# TAB 1: Upload & Analyze
+# ============================================================================
+with tab1:
+    st.header("📤 Upload and Analyze Data")
+    
+    uploaded_file = st.file_uploader(
+        "Upload a CSV or Excel file",
+        type=["csv", "xlsx", "xls"],
+        help="Supported formats: CSV, Excel"
+    )
+    
+    if uploaded_file is not None:
+        st.success(f"✅ File uploaded: {uploaded_file.name}")
+        
+        # Read the file
+        try:
+            if uploaded_file.name.endswith('.csv'):
+                df = pd.read_csv(uploaded_file)
+            else:
+                df = pd.read_excel(uploaded_file)
+            
+            # Display data preview
+            st.subheader("📋 Data Preview")
+            st.dataframe(df.head(10), use_container_width=True)
+            
+            # Display statistics
+            st.subheader("📊 Data Statistics")
+            col1, col2, col3 = st.columns(3)
+            
+            with col1:
+                st.metric("Rows", df.shape[0])
+            with col2:
+                st.metric("Columns", df.shape[1])
+            with col3:
+                st.metric("Memory", f"{df.memory_usage(deep=True).sum() / 1024:.2f} KB")
+            
+            # Detailed statistics
+            st.write(df.describe().T)
+            
+            # Ask AI about the data
+            st.subheader("❓ Ask AI About Your Data")
+            question = st.text_input(
+                "What would you like to know about this data?",
+                placeholder="e.g., What is the average value in column X?"
+            )
+            
+            if question:
+                with st.spinner("🤔 AI is analyzing your data..."):
+                    # Create prompt
+                    data_summary = df.describe().to_string()
+                    prompt = f"""You are a data analyst expert. You have the following data summary:
+
+{data_summary}
+
+Column names: {', '.join(df.columns.tolist())}
+
+User's question: {question}
+
+Please provide a clear, concise analysis based on the data summary. Focus on actionable insights."""
+                    
+                    # Generate response
+                    response = llm(
+                        prompt,
+                        max_tokens=300,
+                        stop=["\n\nUser:", "Question:"],
+                        echo=False,
+                        temperature=0.7
+                    )
+                    
+                    answer = response['choices'][0]['text'].strip()
+                    st.success("✅ Analysis Complete")
+                    st.write(answer)
+        
+        except Exception as e:
+            st.error(f"Error reading file: {e}")
+
+# ============================================================================
+# TAB 2: Chat
+# ============================================================================
+with tab2:
+    st.header("💬 Chat with AI Assistant")
+    st.write("Have a conversation with Llama 2. Ask anything!")
+    
+    # Initialize session state for chat history
+    if "messages" not in st.session_state:
+        st.session_state.messages = []
+    
+    # Display chat history
+    for message in st.session_state.messages:
+        with st.chat_message(message["role"]):
+            st.markdown(message["content"])
+    
+    # Chat input
+    user_input = st.chat_input("Type your message here...")
+    
+    if user_input:
+        # Add user message to history
+        st.session_state.messages.append({"role": "user", "content": user_input})
+        
+        # Display user message
+        with st.chat_message("user"):
+            st.markdown(user_input)
+        
+        # Generate AI response
+        with st.chat_message("assistant"):
+            with st.spinner("⏳ Generating response..."):
+                prompt = f"""You are a helpful AI assistant. The user asks: {user_input}
+
+Provide a clear, helpful, and concise response."""
+                
+                response = llm(
+                    prompt,
+                    max_tokens=300,
+                    stop=["\n\nUser:", "User:"],
+                    echo=False,
+                    temperature=0.7
+                )
+                
+                assistant_message = response['choices'][0]['text'].strip()
+                st.markdown(assistant_message)
+                
+                # Add assistant message to history
+                st.session_state.messages.append({
+                    "role": "assistant",
+                    "content": assistant_message
+                })
+
+# ============================================================================
+# TAB 3: About
+# ============================================================================
+with tab3:
+    st.header("ℹ️ About This App")
+    
+    st.markdown("""
+    ### 🎯 What is this?
+    
+    **LLM Data Analyzer** is an AI-powered tool for analyzing data and having conversations with an intelligent assistant.
+    
+    ### 🔧 Technology Stack
+    
+    - **Model:** Llama 2 7B (quantized to 4-bit)
+    - **Framework:** Llama.cpp (CPU inference)
+    - **Frontend:** Streamlit
+    - **Hosting:** Hugging Face Spaces (Free Tier)
+    
+    ### ⚡ Performance
+    
+    - **Speed:** ~5-10 tokens per second (free CPU)
+    - **Context:** 2048 tokens max
+    - **Model Size:** 4GB (quantized)
+    - **Hardware:** Free tier CPU
+    
+    ### 💡 Use Cases
+    
+    1. **Data Analysis**: Upload CSV/Excel and ask questions
+    2. **Chat**: General conversation with AI
+    3. **Learning**: Understand your data better
+    
+    ### 🚀 Faster Version Available
+    
+    For **GPU acceleration** (70+ tokens/sec):
+    - Run locally on Apple Silicon Mac using MLX
+    - Upgrade to Hugging Face PRO tier
+    - Deploy on GPU-enabled cloud servers
+    
+    ### 📝 Tips
+    
+    - Keep questions focused and specific for best results
+    - First request takes longer (model loading)
+    - Data is processed locally, not stored on server
+    
+    ### 🔗 Links
+    
+    - [GitHub Repository](#) - Source code
+    - [Hugging Face Hub](#) - Model info
+    - [Llama.cpp](#) - Inference engine
+    
+    ---
+    
+    **Version:** 1.0 | **Last Updated:** Dec 2025
+    """)

requirements.txt

@@ -0,0 +1,9 @@
+streamlit==1.29.0
+requests==2.31.0
+pandas==2.1.3
+numpy>=1.26.0,<2.0
+openpyxl==3.1.2
+plotly==5.17.0
+python-dotenv==1.0.0
+llama-cpp-python==0.2.35
+huggingface-hub==0.17.3