Create app.py

app.py

@@ -0,0 +1,50 @@
+import streamlit as st
+
+
+import torch
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+
+
+quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+
+
+model_name = "masakhane/zephyr-7b-gemma-sft-african-alpaca"
+
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, quantization_config=quantization_config)
+
+
+pipe = pipeline("text-generation", model=model,tokenizer=tokenizer, torch_dtype=torch.bfloat16, device_map="auto")
+
+
+
+if 'messages' not in st.session_state:
+    st.session_state.messages = [
+    {
+        "role": "system",
+        "content": "You are a friendly chatbot who answewrs question in given language",
+    },
+]
+
+def ask_model(question):
+    # Placeholder for model interaction logic
+    # You would replace this with actual code to query the model
+    st.session_state.messages.append({"role": "user", "content": f"{question}"})
+
+    prompt = pipe.tokenizer.apply_chat_template(st.session_state.messages, tokenize=False, add_generation_prompt=True)
+    outputs = pipe(prompt, max_new_tokens=1000, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
+    print(outputs[0]["generated_text"].split("<|assistant|>")[-1])
+
+    st.session_state.messages.append({"role": "assistant", "content": f"{outputs[0]['generated_text'].split('<|assistant|>')[-1]}"})
+    return st.session_state.messages
+
+st.title('LLM Interaction Interface')
+
+user_input = st.text_input("Ask a question:")
+
+if user_input:
+    # This function is supposed to send the question to the LLM and get the response
+    response = ask_model(user_input)
+    st.text_area("Response:", value=response[-1]['content'], height=300, max_chars=None, help=None)
+    st.json({'value':response},expanded=False)