Create app.py

app.py

@@ -0,0 +1,29 @@
+pip install streamlit transformers
+import streamlit as st
+from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
+
+# Load the pipeline
+model_name = "Orenguteng/Llama-3.1-8B-Lexi-Uncensored-V2"
+pipe = pipeline("text-generation", model=model_name)
+
+# Optionally load the tokenizer and model directly (not used directly in this example)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def generate_response(prompt):
+    """Generate a response from the model given a prompt."""
+    response = pipe(prompt, max_length=100, num_return_sequences=1)
+    return response[0]['generated_text']
+
+# Streamlit Interface
+st.title("AI Chatbot using Hugging Face")
+st.markdown("This app uses the Llama-3.1-8B-Lexi-Uncensored-V2 model to generate responses.")
+
+user_input = st.text_input("Enter your message:", placeholder="Type something here...")
+
+if st.button("Generate Response"):
+    if user_input:
+        response = generate_response(user_input)
+        st.text_area("Response:", value=response, height=200)
+    else:
+        st.warning("Please enter a message before clicking the button.")