Update app.py

app.py

@@ -43,101 +43,6 @@ def parse_codeblock(text):
             if i > 0:
                 lines[i] = "<br/>" + line.replace("<", "&lt;").replace(">", "&gt;")
     return "".join(lines)
-    
-    
-# def predict(inputs, top_p, temperature, chat_counter, chatbot, history, request:gr.Request):
-#     payload = {
-#         "model": MODEL,
-#         "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}",
-#         "Headers": f"{request.kwargs['headers']}"
-#     }
-
-#     # print(f"chat_counter - {chat_counter}")
-#     if chat_counter != 0 :
-#         messages = []
-#         for i, data in enumerate(history):
-#             if i % 2 == 0:
-#                 role = 'user'
-#             else:
-#                 role = 'assistant'
-#             message = {}
-#             message["role"] = role
-#             message["content"] = data
-#             messages.append(message)
-        
-#         message = {}
-#         message["role"] = "user" 
-#         message["content"] = inputs
-#         messages.append(message)
-#         payload = {
-#             "model": MODEL,
-#             "messages": messages,
-#             "temperature" : temperature,
-#             "top_p": top_p,
-#             "n" : 1,
-#             "stream": True,
-#             "presence_penalty":0,
-#             "frequency_penalty":0,
-#         }
-
-#     chat_counter += 1
-
-#     history.append(inputs)
-#     token_counter = 0 
-#     partial_words = "" 
-#     counter = 0
-
-#     try:
-#         # 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_code = f"{response}"
-#         #if response_code.strip() != "<Response [200]>":
-#         #    #print(f"response code - {response}")
-#         #    raise Exception(f"Sorry, hitting rate limit. Please try again later. {response}")
-        
-#         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']:
-#                     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
-#                     token_counter += 1
-#                     yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=False), gr.update(interactive=False)  # resembles {chatbot: chat, state: history}  
-#     except Exception as e:
-#         print (f'error found: {e}')
-#     yield [(parse_codeblock(history[i]), parse_codeblock(history[i + 1])) for i in range(0, len(history) - 1, 2) ], history, chat_counter, response, gr.update(interactive=True), gr.update(interactive=True)
-#     print(json.dumps({"chat_counter": chat_counter, "payload": payload, "partial_words": partial_words, "token_counter": token_counter, "counter": counter}))
-
-def get_random_sample(lists, total_k=TOTAL_K):
-    output_list = []
-    while True:
-        k = random.choice(lists)
-        if not lists[k] in output_list:
-            output_list.append(lists[k])
-        if len(output_list) == total_k:
-            break
-    return output_list
 
 def reset_textbox():
     return gr.update(value='', interactive=False), gr.update(interactive=False)
@@ -178,12 +83,17 @@ Assistant: <utterance>
 In this app, you can explore the outputs of a gpt-3.5 LLM.
 """
 
-def submit(image):
+def submit(image, agents):
     global CURRENT_POSITION
     balloon = Image.open(os.path.join(os.path.dirname(__file__), 'data/balloon.png')).resize((64, 64))
     new_image = Image.fromarray(image).convert('RGBA')
     for k in range(TOTAL_K):
         new_image.paste(balloon, [CURRENT_POSITION[k][0], CURRENT_POSITION[k][1] - 64], balloon)
+
+    Answer = 'O'
+    for k in range(TOTAL_K):
+        agents[k].update(value = Answer)  
+        
     return np.array(new_image)
 
 theme = gr.themes.Default(primary_hue="green")
@@ -255,7 +165,7 @@ with gr.Blocks(css = """#col_container { margin-left: auto; margin-right: auto;}
     b0.click(random_sample, inputs = [], outputs = [image])
     b2.click(reset_sample, inputs = [], outputs = [image])
 
-    b1.click(submit, inputs = [image], outputs = [image])
+    b1.click(submit, inputs = [image, agents], outputs = [image])
                   
     # inputs.submit(reset_textbox, [], [inputs, b1], queue=False)
     # inputs.submit(predict, [inputs, top_p, temperature, chat_counter, chatbot, state], [chatbot, state, chat_counter, server_status_code, inputs, b1],)  #openai_api_key