Update app.py

app.py

@@ -1,66 +1,350 @@
-# We'll generate a sample Streamlit app for LLM fine-tuning and deployment simulation.
-# Since we can't actually fine-tune large models in this script due to constraints, 
-# we'll simulate the UI and interaction as if the model was already fine-tuned.
-
-import os
-
-# Create a simple streamlit app template
-streamlit_app_code = """
 import streamlit as st
-from transformers import pipeline
+from streamlit_option_menu import option_menu
+import pandas as pd
+import numpy as np
+import time
+from PIL import Image
+import os
+import json
+import tempfile
+from huggingface_hub import notebook_login, HfApi, Repository
 
-st.set_page_config(page_title="LLM Fine-Tuned Chatbot", page_icon="🧠", layout="wide")
+# Set page config
+st.set_page_config(
+    page_title="LLM Fine-Tuning & Deployment",
+    page_icon=":robot:",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
 
-# Custom CSS styling
-st.markdown(\"""
+# Custom CSS for styling
+st.markdown("""
     <style>
-        .main {
-            background-color: #f4f4f9;
-        }
-        .stTextInput>div>div>input {
-            border-radius: 10px;
+        .stApp {
+            background-color: #f5f5f5;
         }
         .stButton>button {
             background-color: #4CAF50;
             color: white;
+            border-radius: 5px;
+            padding: 0.5rem 1rem;
+            border: none;
+        }
+        .stTextInput>div>div>input {
+            border-radius: 5px;
+            padding: 0.5rem;
+        }
+        .stSelectbox>div>div>select {
+            border-radius: 5px;
+            padding: 0.5rem;
+        }
+        .css-1aumxhk {
+            background-color: #ffffff;
             border-radius: 10px;
-            height: 3em;
-            width: 100%;
+            padding: 2rem;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+        .header {
+            color: #4CAF50;
+            font-size: 2.5rem;
+            font-weight: bold;
+            margin-bottom: 1rem;
+        }
+        .subheader {
+            color: #333333;
+            font-size: 1.5rem;
+            margin-bottom: 1rem;
         }
     </style>
-\""", unsafe_allow_html=True)
+""", unsafe_allow_html=True)
 
-st.title("🧠 Fine-Tuned LLM Chatbot")
-st.subheader("Chat with your own fine-tuned LLM model")
+# App header
+col1, col2 = st.columns([1, 3])
+with col1:
+    st.image("https://huggingface.co/front/assets/huggingface_logo-noborder.svg", width=100)
+with col2:
+    st.markdown('<div class="header">LLM Fine-Tuning & Deployment</div>', unsafe_allow_html=True)
+    st.markdown("Fine-tune and deploy your large language models with ease")
 
-# Sidebar Info
-st.sidebar.title("Model Info")
-st.sidebar.info("This chatbot uses a fine-tuned LLM (simulated via Hugging Face pipeline)")
+# Navigation menu
+with st.sidebar:
+    selected = option_menu(
+        menu_title="Main Menu",
+        options=["Home", "Data Preparation", "Model Selection", "Fine-Tuning", "Evaluation", "Deployment", "About"],
+        icons=["house", "file-earmark-text", "cpu", "gear", "graph-up", "cloud-upload", "info-circle"],
+        menu_icon="cast",
+        default_index=0,
+    )
 
-# Load pipeline (simulation for actual fine-tuned model)
-@st.cache_resource
-def load_pipeline():
-    # Replace with your fine-tuned model, e.g., "your-username/your-fine-tuned-model"
-    return pipeline("text-generation", model="gpt2")
+# Home Page
+if selected == "Home":
+    st.markdown('<div class="subheader">Welcome to LLM Fine-Tuning & Deployment</div>', unsafe_allow_html=True)
+    st.markdown("""
+        This application guides you through the process of fine-tuning large language models (LLMs) 
+        and deploying them to Hugging Face Hub.
+        
+        **Key Features:**
+        - Prepare your dataset for fine-tuning
+        - Select from popular base models
+        - Configure fine-tuning parameters
+        - Evaluate model performance
+        - Deploy to Hugging Face Hub
+        
+        Get started by selecting a step from the sidebar menu.
+    """)
+    
+    st.image("https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/hf-libraries.png", 
+             caption="Hugging Face Ecosystem", use_column_width=True)
 
-generator = load_pipeline()
+# Data Preparation Page
+elif selected == "Data Preparation":
+    st.markdown('<div class="subheader">Data Preparation</div>', unsafe_allow_html=True)
+    
+    tab1, tab2, tab3 = st.tabs(["Upload Data", "Preview Data", "Data Statistics"])
+    
+    with tab1:
+        st.markdown("### Upload Your Dataset")
+        data_file = st.file_uploader("Choose a file (CSV, JSON, or TXT)", type=["csv", "json", "txt"])
+        
+        if data_file is not None:
+            file_details = {"FileName": data_file.name, "FileType": data_file.type, "FileSize": data_file.size}
+            st.success("File uploaded successfully!")
+            st.json(file_details)
+            
+            # Save uploaded file to temporary location
+            temp_dir = tempfile.mkdtemp()
+            path = os.path.join(temp_dir, data_file.name)
+            with open(path, "wb") as f:
+                f.write(data_file.getbuffer())
+            
+            st.session_state['data_path'] = path
+            st.session_state['data_type'] = data_file.type
+    
+    with tab2:
+        if 'data_path' in st.session_state:
+            st.markdown("### Data Preview")
+            
+            if st.session_state['data_type'] == "text/csv":
+                df = pd.read_csv(st.session_state['data_path'])
+                st.dataframe(df.head())
+            elif st.session_state['data_type'] == "application/json":
+                with open(st.session_state['data_path']) as f:
+                    data = json.load(f)
+                st.json(data)
+            else:
+                with open(st.session_state['data_path']) as f:
+                    data = f.read()
+                st.text_area("Text Content", data, height=200)
+        else:
+            st.warning("Please upload a file first.")
+    
+    with tab3:
+        if 'data_path' in st.session_state:
+            st.markdown("### Data Statistics")
+            
+            if st.session_state['data_type'] == "text/csv":
+                df = pd.read_csv(st.session_state['data_path'])
+                
+                col1, col2, col3 = st.columns(3)
+                col1.metric("Total Samples", len(df))
+                col2.metric("Columns", len(df.columns))
+                col3.metric("Missing Values", df.isnull().sum().sum())
+                
+                st.markdown("**Column Types**")
+                st.table(df.dtypes.reset_index().rename(columns={"index": "Column", 0: "Type"}))
+            else:
+                st.info("Detailed statistics available for CSV files only.")
 
-# User input
-user_input = st.text_input("Enter your prompt here")
+# Model Selection Page
+elif selected == "Model Selection":
+    st.markdown('<div class="subheader">Model Selection</div>', unsafe_allow_html=True)
+    
+    model_options = {
+        "GPT-like": ["gpt2", "gpt2-medium", "gpt2-large", "gpt2-xl"],
+        "BERT-like": ["bert-base-uncased", "bert-large-uncased"],
+        "RoBERTa": ["roberta-base", "roberta-large"],
+        "T5": ["t5-small", "t5-base", "t5-large"],
+        "Custom": ["Enter custom model name"]
+    }
+    
+    model_family = st.selectbox("Select Model Family", list(model_options.keys()))
+    
+    if model_family == "Custom":
+        model_name = st.text_input("Enter Hugging Face Model ID")
+    else:
+        model_name = st.selectbox("Select Model", model_options[model_family])
+    
+    st.markdown("### Model Information")
+    
+    if model_name:
+        st.info(f"You've selected: **{model_name}**")
+        
+        # Display model card
+        st.markdown(f"View model card on [Hugging Face Hub](https://huggingface.co/{model_name})")
+        
+        # Show estimated resource requirements
+        st.markdown("**Estimated Resource Requirements**")
+        
+        if "gpt2" in model_name or "large" in model_name:
+            st.warning("This model requires significant GPU memory (8GB+ recommended)")
+        else:
+            st.success("This model can run on modest hardware (4GB GPU memory sufficient for fine-tuning)")
+        
+        st.session_state['selected_model'] = model_name
+
+# Fine-Tuning Page
+elif selected == "Fine-Tuning":
+    st.markdown('<div class="subheader">Fine-Tuning Configuration</div>', unsafe_allow_html=True)
+    
+    if 'selected_model' not in st.session_state:
+        st.warning("Please select a model first from the Model Selection page.")
+        st.stop()
+    
+    st.info(f"Fine-tuning model: **{st.session_state['selected_model']}**")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("### Training Parameters")
+        epochs = st.slider("Number of Epochs", 1, 20, 3)
+        batch_size = st.selectbox("Batch Size", [4, 8, 16, 32, 64], index=2)
+        learning_rate = st.selectbox("Learning Rate", [1e-5, 3e-5, 5e-5, 1e-4], index=1)
+        
+    with col2:
+        st.markdown("### Advanced Options")
+        warmup_steps = st.number_input("Warmup Steps", 0, 1000, 100)
+        weight_decay = st.slider("Weight Decay", 0.0, 0.1, 0.01)
+        fp16 = st.checkbox("Use Mixed Precision (FP16)", value=True)
+    
+    st.markdown("### Start Fine-Tuning")
+    
+    if st.button("Begin Fine-Tuning Process"):
+        if 'data_path' not in st.session_state:
+            st.error("Please upload your dataset first.")
+        else:
+            with st.spinner("Setting up fine-tuning environment..."):
+                time.sleep(2)
+                
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+            
+            for i in range(1, 101):
+                progress_bar.progress(i)
+                status_text.text(f"Training progress: {i}%")
+                time.sleep(0.05)
+                
+            st.success("Fine-tuning completed successfully!")
+            st.balloons()
+            
+            st.session_state['fine_tuned'] = True
+            st.session_state['model_path'] = f"./models/{st.session_state['selected_model']}-fine-tuned"
 
-if st.button("Generate Response"):
-    if user_input.strip() != "":
+# Evaluation Page
+elif selected == "Evaluation":
+    st.markdown('<div class="subheader">Model Evaluation</div>', unsafe_allow_html=True)
+    
+    if 'fine_tuned' not in st.session_state:
+        st.warning("Please complete the fine-tuning process first.")
+        st.stop()
+    
+    st.success(f"Evaluating fine-tuned model: **{st.session_state['selected_model']}**")
+    
+    st.markdown("### Evaluation Metrics")
+    
+    # Simulated metrics
+    col1, col2, col3 = st.columns(3)
+    col1.metric("Training Loss", "0.456", "-0.124 from baseline")
+    col2.metric("Validation Loss", "0.512", "-0.098 from baseline")
+    col3.metric("Accuracy", "0.872", "+0.15 from baseline")
+    
+    st.markdown("### Sample Predictions")
+    
+    sample_text = st.text_area("Enter text to test the model", "The movie was...")
+    
+    if st.button("Generate Prediction"):
         with st.spinner("Generating response..."):
-            response = generator(user_input, max_length=100, do_sample=True)[0]['generated_text']
-            st.success("Response:")
-            st.write(response)
-    else:
-        st.warning("Please enter a prompt to generate response.")
-"""
+            time.sleep(2)
+            
+            # Simulate different responses
+            responses = {
+                "positive": "The movie was absolutely fantastic! The acting was superb and the storyline kept me engaged throughout.",
+                "negative": "The movie was terrible. Poor acting and a predictable plot made it a complete waste of time.",
+                "neutral": "The movie was okay. It had some good moments but nothing particularly memorable."
+            }
+            
+            selected_response = np.random.choice(list(responses.values()))
+            
+            st.markdown("**Model Output:**")
+            st.info(selected_response)
+
+# Deployment Page
+elif selected == "Deployment":
+    st.markdown('<div class="subheader">Model Deployment</div>', unsafe_allow_html=True)
+    
+    if 'fine_tuned' not in st.session_state:
+        st.warning("Please complete the fine-tuning process first.")
+        st.stop()
+    
+    st.info(f"Ready to deploy: **{st.session_state['selected_model']}-fine-tuned**")
+    
+    st.markdown("### Hugging Face Hub Deployment")
+    
+    hf_token = st.text_input("Hugging Face Access Token", type="password")
+    repo_name = st.text_input("Repository Name", "my-fine-tuned-model")
+    privacy = st.radio("Repository Visibility", ["Public", "Private"])
+    
+    if st.button("Deploy to Hugging Face Hub"):
+        if not hf_token:
+            st.error("Please provide your Hugging Face access token")
+        else:
+            with st.spinner("Uploading model to Hugging Face Hub..."):
+                time.sleep(3)
+                
+                # In a real app, you would use:
+                # api = HfApi()
+                # api.create_repo(repo_name, private=(privacy == "Private"), token=hf_token)
+                # api.upload_folder(folder_path=st.session_state['model_path'], repo_id=repo_name)
+                
+                st.success(f"Model successfully deployed to Hugging Face Hub!")
+                st.markdown(f"Your model is available at: [https://huggingface.co/{repo_name}](https://huggingface.co/{repo_name})")
+                
+                st.session_state['deployed'] = True
 
-# Save to file for deployment
-file_path = "/mnt/data/llm_finetuned_chatbot.py"
-with open(file_path, "w") as f:
-    f.write(streamlit_app_code)
+# About Page
+elif selected == "About":
+    st.markdown('<div class="subheader">About This App</div>', unsafe_allow_html=True)
+    
+    st.markdown("""
+        **LLM Fine-Tuning & Deployment App**
+        
+        This application provides an intuitive interface for fine-tuning large language models 
+        and deploying them to Hugging Face Hub.
+        
+        **Features:**
+        - Streamlined workflow for LLM fine-tuning
+        - Support for various model architectures
+        - Easy deployment to Hugging Face Hub
+        - Beautiful and responsive UI
+        
+        **Technologies Used:**
+        - Streamlit for the web interface
+        - Hugging Face Transformers for model handling
+        - Hugging Face Hub for model deployment
+        
+        Developed with ❤️ for the AI community.
+    """)
+    
+    st.markdown("---")
+    st.markdown("""
+        **Disclaimer:** This is a demo application. For production use, 
+        please ensure you have proper hardware resources and follow best practices 
+        for model training and deployment.
+    """)
 
-file_path
+# Footer
+st.markdown("---")
+st.markdown("""
+    <div style="text-align: center; color: #666666; font-size: 0.9rem;">
+        LLM Fine-Tuning & Deployment App | Powered by Streamlit and Hugging Face
+    </div>
+""", unsafe_allow_html=True)