Rename app to app.py

app

@@ -1,7 +0,0 @@
-import gradio as gr
-
-def greet(name):
-    return "Hello " + name + "!!"
-
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()

app.py

@@ -0,0 +1,43 @@
+import streamlit as st
+from streamlit_chat import message
+from streamlit_extras.colored_header import colored_header
+from streamlit_extras.add_vertical_space import add_vertical_space
+from hugchat import hugchat
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+
+tokenizer = AutoTokenizer.from_pretrained("Celestinian/PromptGPT")
+model = AutoModelForCausalLM.from_pretrained("Celestinian/PromptGPT")
+
+st.set_page_config(page_title="EinfachChat")
+
+# Sidebar contents
+with st.sidebar:
+    st.title('EinfachChat')
+    max_length = st.slider('Max Length', min_value=10, max_value=100, value=30)
+    do_sample = st.checkbox('Do Sample', value=True)
+    temperature = st.slider('Temperature', min_value=0.1, max_value=1.0, value=0.4)
+    no_repeat_ngram_size = st.slider('No Repeat N-Gram Size', min_value=1, max_value=10, value=1)
+    top_k = st.slider('Top K', min_value=1, max_value=100, value=50)
+    top_p = st.slider('Top P', min_value=0.1, max_value=1.0, value=0.2)
+
+# Rest of your original Streamlit code ...
+
+def generate_text(prompt, max_length, do_sample, temperature, no_repeat_ngram_size, top_k, top_p):
+    formatted_prompt = "\n" + prompt
+    if not ',' in prompt:
+        formatted_prompt += ','
+    prompt = tokenizer(formatted_prompt, return_tensors='pt')
+    prompt = {key: value.to(device) for key, value in prompt.items()}
+    out = model.generate(**prompt, max_length=max_length, do_sample=do_sample, temperature=temperature,
+                          no_repeat_ngram_size=no_repeat_ngram_size, top_k=top_k, top_p=top_p)
+    output = tokenizer.decode(out[0])
+    clean_output = output.replace('\n', '\n')
+    return clean_output
+
+# Inside the conditional display part, replace
+# response = generate_response(user_input)
+# with
+response = generate_text(user_input, max_length, do_sample, temperature, no_repeat_ngram_size, top_k, top_p)