Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,95 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+from transformers import pipeline
+import torch
+
+# Set the title of the Streamlit app
+st.set_page_config(page_title="Hugging Face Chat", page_icon="🤗")
+st.title("🤗 Hugging Face Model Chat")
+
+# Add a sidebar for model selection
+with st.sidebar:
+    st.header("Model Selection")
+    # A dictionary of available models
+    model_options = {
+        "NVIDIA Nemotron 3 8B": "nvidia/nemotron-3-8b-chat-4k-sft",
+        "Meta Llama 3.1 8B": "meta-llama/Llama-3.1-8B-Instruct",
+        "Mistral 7B Instruct": "mistralai/Mistral-7B-Instruct-v0.1",
+        "Gemma 7B It": "google/gemma-7b-it",
+    }
+    selected_model_name = st.selectbox("Choose a model:", list(model_options.keys()))
+    model_id = model_options[selected_model_name]
+
+    st.markdown("---")
+    st.markdown("This app allows you to chat with different open-source Large Language Models from the Hugging Face Hub.")
+    st.markdown("Select a model from the dropdown and start chatting!")
+
+
+# Caching the model loading to improve performance
+@st.cache_resource
+def load_model(model_id):
+    """Loads the selected model and tokenizer from Hugging Face."""
+    try:
+        # Use "text-generation" pipeline for chat models
+        pipe = pipeline(
+            "text-generation",
+            model=model_id,
+            torch_dtype=torch.bfloat16,
+            device_map="auto"
+        )
+        return pipe
+    except Exception as e:
+        st.error(f"Error loading model: {e}")
+        return None
+
+# Load the selected model
+pipe = load_model(model_id)
+
+# Initialize chat history in session state
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+
+# Display prior chat messages
+for message in st.session_state.messages:
+    with st.chat_message(message["role"]):
+        st.markdown(message["content"])
+
+# Get user input
+if prompt := st.chat_input("What would you like to ask?"):
+    # Add user message to chat history
+    st.session_state.messages.append({"role": "user", "content": prompt})
+    # Display user message
+    with st.chat_message("user"):
+        st.markdown(prompt)
+
+    # Generate a response from the model
+    if pipe:
+        with st.chat_message("assistant"):
+            with st.spinner("Thinking..."):
+                # Prepare the prompt for the model
+                # Note: Different models may have different prompt formats.
+                # This is a generic approach.
+                formatted_prompt = f"User: {prompt}\nAssistant:"
+
+                # Generate the response
+                response = pipe(
+                    formatted_prompt,
+                    max_new_tokens=512,
+                    do_sample=True,
+                    temperature=0.7,
+                    top_p=0.95,
+                    top_k=50
+                )
+
+                # Extract the generated text
+                if response and len(response) > 0 and "generated_text" in response[0]:
+                    # The output often includes the prompt, so we clean it up.
+                    assistant_response = response[0]["generated_text"].split("Assistant:")[-1].strip()
+                else:
+                    assistant_response = "Sorry, I couldn't generate a response."
+
+                st.markdown(assistant_response)
+                # Add assistant response to chat history
+                st.session_state.messages.append({"role": "assistant", "content": assistant_response})
+    else:
+        st.error("Model not loaded. Cannot generate a response.")
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))