add inital app

__init__.py

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+# -*-coding:utf-8 -*-

app.py

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+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"
+
+
+def predict(inputs, top_p, temperature, openai_api_key, chat_counter, chatbot=[],
+            history=[]):  # repetition_penalty, top_k
+
+    payload = {
+        "model": "gpt-3.5-turbo",
+        "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}"
+    }
+
+    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-3.5-turbo",
+            "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,
+        }
+
+    chat_counter += 1
+
+    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)
+    # response = requests.post(API_URL, headers=headers, json=payload, stream=True)
+    token_counter = 0
+    partial_words = ""
+
+    counter = 0
+    for chunk in response.iter_lines():
+        if counter == 0:
+            counter += 1
+            continue
+        counter += 1
+        # check whether each line is non-empty
+        if chunk:
+            # decode each line as response data is in bytes
+            if len(json.loads(chunk.decode()[6:])['choices'][0]["delta"]) == 0:
+                break
+            # print(json.loads(chunk.decode()[6:])['choices'][0]["delta"]["content"])
+            partial_words = partial_words + json.loads(chunk.decode()[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  # resembles {chatbot: chat, state: history}  
+
+
+def reset_textbox():
+    return gr.update(value='')
+
+
+title = """<h1 align="center">🔥Finance ChatBot 🚀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>
+...
+```
+In this app, you can explore the outputs of a gpt-3.5-turbo LLM.
+"""
+
+with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
+                #chatbot {height: 520px; overflow: auto;}""") as demo:
+    gr.HTML(title)
+    gr.HTML(
+        '''<center><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate the Space and run securely with your OpenAI API Key</center>''')
+    with gr.Column(elem_id="col_container"):
+        openai_api_key = gr.Textbox(type='password', label="Enter your 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
+        state = gr.State([])  # s
+        b1 = gr.Button()
+
+        # 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", )
+            # 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)
+
+    inputs.submit(predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state],
+                  [chatbot, state, chat_counter], )
+    b1.click(predict, [inputs, top_p, temperature, openai_api_key, chat_counter, chatbot, state],
+             [chatbot, state, chat_counter], )
+    b1.click(reset_textbox, [], [inputs])
+    inputs.submit(reset_textbox, [], [inputs])
+
+    # gr.Markdown(description)
+    demo.queue().launch(debug=True)