Update app.py

app.py

@@ -1,154 +1,66 @@
+# 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 AutoTokenizer, AutoModelForCausalLM, pipeline
-import torch
+from transformers import pipeline
 
-# ======================
-# 🎨 STYLING & LAYOUT
-# ======================
-st.set_page_config(
-    page_title="LLM Fine-Tuning Studio",
-    page_icon="🧠",
-    layout="wide"
-)
+st.set_page_config(page_title="LLM Fine-Tuned Chatbot", page_icon="🧠", layout="wide")
 
-# Custom CSS
-st.markdown("""
-<style>
-    /* Main container */
-    .main {
-        background: linear-gradient(135deg, #f5f7fa 0%, #c3cfe2 100%);
-    }
-    
-    /* Headers */
-    h1 {
-        color: #2c3e50;
-        text-align: center;
-        font-family: 'Arial', sans-serif;
-        border-bottom: 2px solid #4CAF50;
-        padding-bottom: 10px;
-    }
-    
-    /* Sidebar */
-    [data-testid="stSidebar"] {
-        background: linear-gradient(195deg, #2c3e50 0%, #4CAF50 100%) !important;
-        color: white;
-    }
-    
-    /* Buttons */
-    .stButton>button {
-        background: linear-gradient(to right, #4CAF50, #2E8B57);
-        color: white;
-        border: none;
-        border-radius: 25px;
-        padding: 10px 24px;
-        font-weight: bold;
-    }
-    
-    /* Chat bubbles */
-    .user-message {
-        background: #e3f2fd;
-        border-radius: 15px 15px 0 15px;
-        padding: 12px;
-        margin: 5px 0;
-    }
-    
-    .bot-message {
-        background: #4CAF50;
-        color: white;
-        border-radius: 15px 15px 15px 0;
-        padding: 12px;
-        margin: 5px 0;
-    }
-    
-    /* Input box */
-    .stTextInput>div>div>input {
-        border-radius: 20px !important;
-        padding: 10px 15px !important;
-    }
-</style>
-""", unsafe_allow_html=True)
+# Custom CSS styling
+st.markdown(\"""
+    <style>
+        .main {
+            background-color: #f4f4f9;
+        }
+        .stTextInput>div>div>input {
+            border-radius: 10px;
+        }
+        .stButton>button {
+            background-color: #4CAF50;
+            color: white;
+            border-radius: 10px;
+            height: 3em;
+            width: 100%;
+        }
+    </style>
+\""", unsafe_allow_html=True)
 
-# ======================
-# 🧠 MODEL LOADING
-# ======================
-@st.cache_resource
-def load_model():
-    model_name = "mistralai/Mistral-7B-v0.1"
-    tokenizer = AutoTokenizer.from_pretrained(model_name)
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name,
-        torch_dtype=torch.float16,
-        device_map="auto"
-    )
-    return tokenizer, model
+st.title("🧠 Fine-Tuned LLM Chatbot")
+st.subheader("Chat with your own fine-tuned LLM model")
 
-tokenizer, model = load_model()
+# Sidebar Info
+st.sidebar.title("Model Info")
+st.sidebar.info("This chatbot uses a fine-tuned LLM (simulated via Hugging Face pipeline)")
 
-# ======================
-# 🎛️ SIDEBAR CONTROLS
-# ======================
-with st.sidebar:
-    st.title("⚙️ Fine-Tuning Controls")
-    
-    st.subheader("Model Parameters")
-    temperature = st.slider("Temperature", 0.1, 1.0, 0.7, 0.05)
-    max_length = st.slider("Max Length", 50, 500, 150)
-    
-    st.subheader("Fine-Tuning Options")
-    use_lora = st.checkbox("Use LoRA", True)
-    quantize = st.selectbox("Quantization", ["None", "4-bit", "8-bit"])
-    
-    if st.button("🔄 Apply Changes"):
-        st.toast("Settings updated!", icon="✅")
+# 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")
 
-# ======================
-# 💬 MAIN CHAT INTERFACE
-# ======================
-st.title("🧠 LLM Fine-Tuning Studio")
-st.caption("Fine-tune and deploy state-of-the-art language models")
+generator = load_pipeline()
 
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = [
-        {"role": "assistant", "content": "Hello! I'm your fine-tuned AI assistant. How can I help you today?"}
-    ]
+# User input
+user_input = st.text_input("Enter your prompt here")
 
-# Display chat messages
-for message in st.session_state.messages:
-    if message["role"] == "assistant":
-        with st.chat_message("assistant"):
-            st.markdown(f'<div class="bot-message">{message["content"]}</div>', unsafe_allow_html=True)
+if st.button("Generate Response"):
+    if user_input.strip() != "":
+        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:
-        with st.chat_message("user"):
-            st.markdown(f'<div class="user-message">{message["content"]}</div>', unsafe_allow_html=True)
+        st.warning("Please enter a prompt to generate response.")
+"""
 
-# Chat input
-if prompt := st.chat_input("Type your message..."):
-    st.session_state.messages.append({"role": "user", "content": prompt})
-    
-    with st.chat_message("user"):
-        st.markdown(f'<div class="user-message">{prompt}</div>', unsafe_allow_html=True)
-    
-    # Generate response
-    with st.chat_message("assistant"):
-        with st.spinner("🧠 Thinking..."):
-            inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-            outputs = model.generate(
-                **inputs,
-                max_new_tokens=max_length,
-                temperature=temperature,
-                do_sample=True
-            )
-            response = tokenizer.decode(outputs[0], skip_special_tokens=True)
-            
-            st.markdown(f'<div class="bot-message">{response}</div>', unsafe_allow_html=True)
-            st.session_state.messages.append({"role": "assistant", "content": response})
+# 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)
 
-# ======================
-# 📊 FINE-TUNE STATUS
-# ======================
-st.sidebar.markdown("---")
-st.sidebar.subheader("Training Metrics")
-st.sidebar.metric("Loss", "0.45", delta="-0.02")
-st.sidebar.metric("Accuracy", "87%", delta="+2%")
-st.sidebar.progress(75)
+file_path