Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,40 @@
-import altair as alt
-import numpy as np
-import pandas as pd
+from transformers import AutoTokenizer, AutoModelForCausalLM
 import streamlit as st
-
-"""
-# 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])),
-    ))
+import torch
+
+st.title("Tokenizer Test Space")
+
+model_id = "google/gemma-2b-it" # Test with the official model first
+# model_id = "Rahul-8799/project_manager_gemma3" # If the official model works, try yours
+
+try:
+    st.write(f"Attempting to load tokenizer for {model_id}...")
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    st.success("Tokenizer loaded successfully!")
+    st.write("Tokenizer details:", tokenizer)
+except Exception as e:
+    st.error(f"Error loading tokenizer: {e}")
+    st.exception(e) # Show full traceback
+
+try:
+    st.write(f"Attempting to load model for {model_id}...")
+    # Assuming you want 4-bit quantization for Gemma
+    from transformers import BitsAndBytesConfig
+    quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_quant_type="nf4",
+        bnb_4bit_compute_dtype=torch.bfloat16,
+        bnb_4bit_use_double_quant=False,
+    )
+    model = AutoModelForCausalLM.from_pretrained(
+        model_id,
+        quantization_config=quantization_config,
+        low_cpu_mem_usage=True,
+        torch_dtype=torch.bfloat16,
+        trust_remote_code=True
+    )
+    st.success("Model loaded successfully!")
+    st.write("Model details:", model)
+except Exception as e:
+    st.error(f"Error loading model: {e}")
+    st.exception(e)