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README.md

@@ -1,13 +1,13 @@
 ---
-title: Chat Prompt Enhancer AI2
+title: Chat Prompt Enhancer AI
 emoji: 💬
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
-sdk_version: 4.36.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
-license: llama3.1
+license: apache-2.0
 ---
 
 An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -4,6 +4,7 @@ from huggingface_hub import InferenceClient
 """
 For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
 """
+# client = InferenceClient("meta-llama/Meta-Llama-3.1-8B-Instruct")
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
 
@@ -45,8 +46,8 @@ For information on how to customize the ChatInterface, peruse the gradio docs: h
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
+        gr.Textbox(value="Act as a Prompt Enhancer AI that takes user-input prompts and transforms them into more engaging, detailed, and thought-provoking questions. Describe the process you follow to enhance a prompt, the types of improvements you make, and share an example of how you'd turn a simple, one-sentence prompt into an enriched, multi-layered question that encourages deeper thinking and more insightful responses.", label="System message"),
+        gr.Slider(minimum=1, maximum=2048, value=1536, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
@@ -60,4 +61,46 @@ demo = gr.ChatInterface(
 
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()
+#####################################
+# import gradio as gr
+
+# gr.load("models/meta-llama/Meta-Llama-3.1-70B-Instruct").launch()
+########################################
+# from openai import OpenAI
+# import streamlit as st
+# import os
+# import sys
+# from dotenv import load_dotenv, dotenv_values
+# load_dotenv()
+
+# st.title("ChatGPT-like clone")
+
+# client = OpenAI(api_key=os.environ.get["OPENAI_API_KEY"])
+
+# if "openai_model" not in st.session_state:
+#     st.session_state["openai_model"] = "gpt-3.5-turbo"
+
+# if "messages" not in st.session_state:
+#     st.session_state.messages = []
+
+# for message in st.session_state.messages:
+#     with st.chat_message(message["role"]):
+#         st.markdown(message["content"])
+
+# if prompt := st.chat_input("What is up?"):
+#     st.session_state.messages.append({"role": "user", "content": prompt})
+#     with st.chat_message("user"):
+#         st.markdown(prompt)
+
+#     with st.chat_message("assistant"):
+#         stream = client.chat.completions.create(
+#             model=st.session_state["openai_model"],
+#             messages=[
+#                 {"role": m["role"], "content": m["content"]}
+#                 for m in st.session_state.messages
+#             ],
+#             stream=True,
+#         )
+#         response = st.write_stream(stream)
+#     st.session_state.messages.append({"role": "assistant", "content": response})

gitattributes.txt

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requirements.txt

@@ -1 +1,8 @@
-huggingface_hub==0.22.2
+huggingface_hub
+transformers
+streamlit
+openai
+requests
+python-dotenv
+langchain
+langchain-community