Update app.py

app.py

@@ -1,10 +1,11 @@
 import gradio as gr
-import os
-import json
-import requests
+import openai
 
-
-API_URL = "https://api.openai.com/v1/chat/completions"
+# Initialize the OpenAI client with your proxy API
+client = openai.OpenAI(
+    api_key= openai_api_key,
+    base_url="https://api.pumpkinaigc.online/v1"
+)
 
 # Function to handle predictions
 def predict(inputs, top_p, temperature, openai_api_key, system_prompt, chat_counter, chatbot=[], history=[]):  
@@ -32,31 +33,30 @@ def predict(inputs, top_p, temperature, openai_api_key, system_prompt, chat_coun
         "presence_penalty": 0,
         "frequency_penalty": 0,
     }
-    
-    headers = {
-        "Content-Type": "application/json",
-        "Authorization": f"Bearer {openai_api_key}"
-    }
-    
+
+    # Set the chat counter
     chat_counter += 1
     history.append(inputs)
-    
-    # Make a POST request to the API endpoint using the requests.post method
-    response = requests.post(API_URL, headers=headers, json=payload, stream=True)
-    
+
+    # Using the proxy API to get the response
+    response = client.Completions.create(
+        model=payload["model"],
+        messages=payload["messages"],
+        temperature=payload["temperature"],
+        top_p=payload["top_p"],
+        stream=payload["stream"],
+        presence_penalty=payload["presence_penalty"],
+        frequency_penalty=payload["frequency_penalty"]
+    )
+
     token_counter = 0
     partial_words = ""
     
-    counter = 0
-    for chunk in response.iter_lines():
-        if counter == 0:
-            counter += 1
-            continue
-        
-        if chunk.decode():
-            chunk = chunk.decode()
-            if len(chunk) > 12 and "content" in json.loads(chunk[6:])['choices'][0]['delta']:
-                partial_words = partial_words + json.loads(chunk[6:])['choices'][0]["delta"]["content"]
+    for chunk in response:
+        if 'choices' in chunk:
+            delta = chunk['choices'][0]['delta']
+            if 'content' in delta:
+                partial_words += delta['content']
                 if token_counter == 0:
                     history.append(" " + partial_words)
                 else:
@@ -70,9 +70,9 @@ def reset_textbox():
     return gr.update(value='')
 
 # UI Components
-title = """<h1 align="center">Multi_Agent_APP</h1>"""
+title = """<h1 align="center">Customizable Chatbot with OpenAI API</h1>"""
 description = """
-Explore the reproduce of o1 by combining multi LLM with prompt engineering
+Explore the outputs of a GPT-3.5 model, with the ability to customize system prompts, enter your OpenAI API key, and interact with a history of conversation logs.
 """
 
 with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-right: auto;}
@@ -84,6 +84,7 @@ with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-r
         system_prompt = gr.Textbox(placeholder="Enter system prompt (optional)", label="System Prompt", lines=2)
         chatbot = gr.Chatbot(elem_id='chatbot')
         inputs = gr.Textbox(placeholder="Type your message here!", label="Input", lines=1)
+        send_btn = gr.Button("Send")
         state = gr.State([])
         chat_counter = gr.Number(value=0, visible=False, precision=0)
         reset_btn = gr.Button("Reset Chat")
@@ -93,8 +94,8 @@ with gr.Blocks(css="""#col_container {width: 1000px; margin-left: auto; margin-r
             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")
         
-        # Submit input for model prediction
-        inputs.submit(predict, [inputs, top_p, temperature, openai_api_key, system_prompt, chat_counter, chatbot, state], 
+        # Submit input for model prediction with the send button
+        send_btn.click(predict, [inputs, top_p, temperature, openai_api_key, system_prompt, chat_counter, chatbot, state], 
                       [chatbot, state, chat_counter])
         reset_btn.click(reset_textbox, [], [inputs])
         inputs.submit(reset_textbox, [], [inputs])