Create app.py

app.py

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+import streamlit as st
+from huggingface_hub import InferenceClient
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import os
+
+# Replace 'your_huggingface_token' with your actual Hugging Face access token
+access_token = os.getenv('token')
+
+# Initialize the tokenizer and model with the Hugging Face access token
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", use_auth_token=access_token)
+model = AutoModelForCausalLM.from_pretrained(
+    "google/gemma-2b-it",
+    torch_dtype=torch.bfloat16,
+    use_auth_token=access_token
+)
+model.eval()  # Set the model to evaluation mode
+
+# Initialize the inference client (if needed for other API-based tasks)
+client = InferenceClient(token=access_token)
+
+def conversation_predict(input_text):
+    """Generate a response for single-turn input using the model."""
+    # Tokenize the input text
+    input_ids = tokenizer(input_text, return_tensors="pt").input_ids
+
+    # Generate a response with the model
+    outputs = model.generate(input_ids, max_new_tokens=2048)
+
+    # Decode and return the generated response
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+def respond():
+    """Streamlit app for a multi-turn chat conversation."""
+    st.title("Chat with Gemma")
+
+    system_message = st.text_input("System message", value="You are a friendly Chatbot.")
+    max_tokens = st.slider("Max new tokens", min_value=1, max_value=2048, value=512, step=1)
+    temperature = st.slider("Temperature", min_value=0.1, max_value=4.0, value=0.7, step=0.1)
+    top_p = st.slider("Top-p (nucleus sampling)", min_value=0.1, max_value=1.0, value=0.95, step=0.05)
+
+    message = st.text_input("Your message")
+
+    if message:
+        response = conversation_predict(message)
+        st.write(response)
+
+if __name__ == "__main__":
+    respond()