draft

app.py

@@ -1,106 +1,39 @@
-import gradio as gr
-import os 
-import json 
-import requests
-
-#Streaming endpoint 
-API_URL = "https://api.openai.com/v1/chat/completions" #os.getenv("API_URL") + "/generate_stream"
-
-#Testing with my Open AI Key 
-OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
-
-def predict(inputs, top_p, temperature, chat_counter, chatbot=[], history=[]):  
+import asyncio
+import datetime
 
-    payload = {
-    "model": "gpt-4",
-    "messages": [{"role": "user", "content": f"{inputs}"}],
-    "temperature" : 1.0,
-    "top_p":1.0,
-    "n" : 1,
-    "stream": True,
-    "presence_penalty":0,
-    "frequency_penalty":0,
-    }
-
-    headers = {
-    "Content-Type": "application/json",
-    "Authorization": f"Bearer {OPENAI_API_KEY}"
-    }
+import gradio as gr
 
-    print(f"chat_counter - {chat_counter}")
-    if chat_counter != 0 :
-        messages=[]
-        for data in chatbot:
-          temp1 = {}
-          temp1["role"] = "user" 
-          temp1["content"] = data[0] 
-          temp2 = {}
-          temp2["role"] = "assistant" 
-          temp2["content"] = data[1]
-          messages.append(temp1)
-          messages.append(temp2)
-        temp3 = {}
-        temp3["role"] = "user" 
-        temp3["content"] = inputs
-        messages.append(temp3)
-        #messages
-        payload = {
-        "model": "gpt-4",
-        "messages": messages, #[{"role": "user", "content": f"{inputs}"}],
-        "temperature" : temperature, #1.0,
-        "top_p": top_p, #1.0,
-        "n" : 1,
-        "stream": True,
-        "presence_penalty":0,
-        "frequency_penalty":0,
-        }
+import koil
 
-    chat_counter+=1
+from lm.lm.openai import openai
+from lm.log.arweaveditems import arweaveditems
 
-    history.append(inputs)
-    print(f"payload is - {payload}")
-    # make a POST request to the API endpoint using the requests.post method, passing in stream=True
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    print(f"response code - {response}")
-    token_counter = 0 
-    partial_words = "" 
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY") 
 
-    counter=0
-    for chunk in response.iter_lines():
-        #Skipping first chunk
-        if counter == 0:
-          counter+=1
-          continue
-        #counter+=1
-        # check whether each line is non-empty
-        if chunk.decode() :
-          chunk = chunk.decode()
-          # decode each line as response data is in bytes
-          if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-              #if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
-              #  break
-              partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
-              if token_counter == 0:
-                history.append(" " + partial_words)
-              else:
-                history[-1] = partial_words
-              chat = [(history[i], history[i + 1]) for i in range(0, len(history) - 1, 2) ]  # convert to tuples of list
-              token_counter+=1
-              yield chat, history, chat_counter, response  # resembles {chatbot: chat, state: history}  
-                   
+@koil.unkoil
+async def predict(input):
+    timestamp = datetime.datetime.now().isoformat()
+    try:
+        api = openai(api_key = OPENAI_API_KEY)
+    except:
+        api = openai(api_key = OPENAI_API_KEY, model = 'gpt-4')
+    async with api as api, arweaveditems() as log:
+        response = await api(input)
+        addr = await log(
+                timestamp = timestamp,
+                **api.metadata,
+                input = input,
+                output = response
+        )
+    print(addr)
+    return [addr, response]
 
 def reset_textbox():
     return gr.update(value='')
 
-title = """<h1 align="center">🔥GPT4 with ChatCompletions API +🚀Gradio-Streaming</h1>"""
-description = """Language models can be conditioned to act like dialogue agents through a conversational prompt that typically takes the form:
-```
-User: <utterance>
-Assistant: <utterance>
-User: <utterance>
-Assistant: <utterance>
-...
-```
+title = """<h1 align="center">🔥GPT4 +🚀Arweave</h1>"""
+description = """Provides GPT4 completions logged to arweave.
+
 In this app, you can explore the outputs of a gpt-4 LLM.
 """
 
@@ -110,13 +43,11 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                 #chatbot {height: 520px; overflow: auto;}""",
               theme=theme) as demo:
     gr.HTML(title)
-    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you full access to GPT4 API (4096 token limit). 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
-    gr.HTML('''<center><a href="https://huggingface.co/spaces/ysharma/ChatGPT4?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
+    gr.HTML("""<h3 align="center">🔥This Huggingface Gradio Demo provides you access to GPT4 API. 🎉🥳🎉You don't need any OPENAI API key🙌</h1>""")
+    gr.HTML('''<center>Duplicate the space to provide a different api key, or donate your key to others in the community tab.</center>''')
     with gr.Column(elem_id = "col_container"):
-        #GPT4 API Key is provided by Huggingface 
-        #openai_api_key = gr.Textbox(type='password', label="Enter only your GPT4 OpenAI API key here")
         chatbot = gr.Chatbot(elem_id='chatbot') #c
-        inputs = gr.Textbox(placeholder= "Hi there!", label= "Type an input and press Enter") #t
+        inputs = gr.Textbox(label= "Type an input and press Enter") #t
         state = gr.State([]) #s
         with gr.Row():
             with gr.Column(scale=7):
@@ -125,15 +56,17 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
                 server_status_code = gr.Textbox(label="Status code from OpenAI server", )
     
         #inputs, top_p, temperature, top_k, repetition_penalty
-        with gr.Accordion("Parameters", open=False):
-            top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
-            temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
+        #with gr.Accordion("Parameters", open=False):
+            #top_p = gr.Slider( minimum=-0, maximum=1.0, value=1.0, step=0.05, interactive=True, label="Top-p (nucleus sampling)",)
+            #temperature = gr.Slider( minimum=-0, maximum=5.0, value=1.0, step=0.1, interactive=True, label="Temperature",)
             #top_k = gr.Slider( minimum=1, maximum=50, value=4, step=1, interactive=True, label="Top-k",)
             #repetition_penalty = gr.Slider( minimum=0.1, maximum=3.0, value=1.03, step=0.01, interactive=True, label="Repetition Penalty", )
-            chat_counter = gr.Number(value=0, visible=False, precision=0)
+            #chat_counter = gr.Number(value=0, visible=False, precision=0)
 
-    inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
-    b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
+    #inputs.submit( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
+    inputs.submit(predict, [inputs], [chatbot])
+    #b1.click( predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code],)  #openai_api_key
+    b1.click(predict, [inputs], [chatbot])
     b1.click(reset_textbox, [], [inputs])
     inputs.submit(reset_textbox, [], [inputs])
                     

requirements.txt

@@ -0,0 +1,2 @@
+koil
+lm[openai,arweave] @ git+https://codeberg.org/xloem/lm.git