Create app.py

app.py

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+import streamlit as st
+from streamlit_chat import message
+import time
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# Set page configuration
+st.set_page_config(
+    page_title="ChatGPT-Style Chatbot",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS for styling
+st.markdown("""
+    <style>
+        .stApp {
+            background-image: linear-gradient(to right, #f5f7fa, #c3cfe2);
+        }
+        .sidebar .sidebar-content {
+            background-image: linear-gradient(to bottom, #667eea, #764ba2);
+            color: white;
+        }
+        .stTextInput>div>div>input {
+            border-radius: 20px;
+            padding: 10px 15px;
+        }
+        .stButton>button {
+            border-radius: 20px;
+            padding: 10px 25px;
+            background-image: linear-gradient(to right, #667eea, #764ba2);
+            color: white;
+            border: none;
+        }
+        .stButton>button:hover {
+            background-image: linear-gradient(to right, #764ba2, #667eea);
+        }
+        .chat-container {
+            background-color: rgba(255, 255, 255, 0.9);
+            border-radius: 15px;
+            padding: 20px;
+            box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        }
+        .title {
+            color: #4a4a4a;
+            text-align: center;
+            margin-bottom: 30px;
+        }
+    </style>
+""", unsafe_allow_html=True)
+
+# Sidebar
+with st.sidebar:
+    st.title("🤖 Chatbot Settings")
+    st.markdown("""
+    ### About
+    This is a ChatGPT-style chatbot powered by a fine-tuned LLM.
+    """)
+    
+    # Model selection
+    model_name = st.selectbox(
+        "Choose a model",
+        ["gpt2", "microsoft/DialoGPT-medium", "facebook/blenderbot-400M-distill"],
+        index=1
+    )
+    
+    # Advanced settings
+    with st.expander("Advanced Settings"):
+        max_length = st.slider("Max response length", 50, 500, 100)
+        temperature = st.slider("Temperature", 0.1, 1.0, 0.7)
+        top_p = st.slider("Top-p", 0.1, 1.0, 0.9)
+    
+    st.markdown("---")
+    st.markdown("Built with ❤️ using [Streamlit](https://streamlit.io/) and [Hugging Face](https://huggingface.co/)")
+
+# Initialize chat history
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = []
+
+if 'past' not in st.session_state:
+    st.session_state['past'] = []
+
+if 'model' not in st.session_state:
+    st.session_state['model'] = None
+
+if 'tokenizer' not in st.session_state:
+    st.session_state['tokenizer'] = None
+
+# Load model
+@st.cache_resource
+def load_model(model_name):
+    try:
+        tokenizer = AutoTokenizer.from_pretrained(model_name)
+        model = AutoModelForCausalLM.from_pretrained(model_name)
+        return model, tokenizer
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None, None
+
+# Generate response
+def generate_response(prompt):
+    if st.session_state['model'] is None or st.session_state['tokenizer'] is None:
+        return "Model not loaded. Please try again."
+    
+    try:
+        # Create conversation history context
+        history = "\n".join([f"User: {p}\nBot: {g}" for p, g in zip(st.session_state['past'], st.session_state['generated'])])
+        full_prompt = f"{history}\nUser: {prompt}\nBot:"
+        
+        # Generate response
+        inputs = st.session_state['tokenizer'].encode(full_prompt, return_tensors="pt")
+        outputs = st.session_state['model'].generate(
+            inputs,
+            max_length=max_length + len(inputs[0]),
+            temperature=temperature,
+            top_p=top_p,
+            pad_token_id=st.session_state['tokenizer'].eos_token_id
+        )
+        response = st.session_state['tokenizer'].decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract only the new response
+        return response.split("Bot:")[-1].strip()
+    except Exception as e:
+        return f"Error generating response: {e}"
+
+# Main app
+st.title("💬 ChatGPT-Style Chatbot")
+st.markdown("""
+    <div class='title'>
+        Experience a conversation with our fine-tuned LLM chatbot
+    </div>
+""", unsafe_allow_html=True)
+
+# Container for chat
+chat_container = st.container()
+
+# Load model button
+if st.button("Load Model"):
+    with st.spinner(f"Loading {model_name}..."):
+        st.session_state['model'], st.session_state['tokenizer'] = load_model(model_name)
+    st.success(f"Model {model_name} loaded successfully!")
+
+# Display chat
+with chat_container:
+    if st.session_state['generated']:
+        for i in range(len(st.session_state['generated'])):
+            message(st.session_state['past'][i], is_user=True, key=str(i) + '_user', avatar_style="identicon")
+            message(st.session_state['generated'][i], key=str(i), avatar_style="bottts")
+
+# User input
+with st.form(key='chat_form', clear_on_submit=True):
+    user_input = st.text_input("You:", key='input', placeholder="Type your message here...")
+    submit_button = st.form_submit_button(label='Send')
+
+if submit_button and user_input:
+    if st.session_state['model'] is None or st.session_state['tokenizer'] is None:
+        st.warning("Please load the model first!")
+    else:
+        # Add user message to chat history
+        st.session_state['past'].append(user_input)
+        
+        # Generate response
+        with st.spinner("Thinking..."):
+            response = generate_response(user_input)
+        
+        # Add bot response to chat history
+        st.session_state['generated'].append(response)
+        
+        # Rerun to update the chat display
+        st.experimental_rerun()