Upload app (20).py

app (20).py

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+import gradio as gr
+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")
+
+
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+"""
+For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+"""
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        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,
+            maximum=1.0,
+            value=0.95,
+            step=0.05,
+            label="Top-p (nucleus sampling)",
+        ),
+    ],
+)
+
+
+if __name__ == "__main__":
+    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})