Update app.py

app.py

@@ -1,31 +1,36 @@
 import gradio as gr
 from gradio_client import Client
 from huggingface_hub import InferenceClient
+from deep_translator import GoogleTranslator
 import random
+
 ss_client = Client("https://omnibus-html-image-current-tab.hf.space/")
 
-models=[
+models = [
     "google/gemma-7b",
     "google/gemma-7b-it",
     "google/gemma-2b",
     "google/gemma-2b-it"
 ]
-clients=[
-InferenceClient(models[0]),
-InferenceClient(models[1]),
-InferenceClient(models[2]),
-InferenceClient(models[3]),
+
+clients = [
+    InferenceClient(models[0]),
+    InferenceClient(models[1]),
+    InferenceClient(models[2]),
+    InferenceClient(models[3]),
 ]
 
-VERBOSE=False
+VERBOSE = False
+
+def translate_to_english(prompt):
+    translated_prompt = GoogleTranslator(source='auto', target='en').translate(prompt)
+    return translated_prompt
 
 def load_models(inp):
-    if VERBOSE==True:    
+    if VERBOSE == True:
         print(type(inp))
         print(inp)
         print(models[inp])
-    #client_z.clear()
-    #client_z.append(InferenceClient(models[inp]))
     return gr.update(label=models[inp])
 
 def format_prompt(message, history, cust_p):
@@ -34,75 +39,64 @@ def format_prompt(message, history, cust_p):
         for user_prompt, bot_response in history:
             prompt += f"<start_of_turn>user{user_prompt}<end_of_turn>"
             prompt += f"<start_of_turn>model{bot_response}<end_of_turn></s>"
-            if VERBOSE==True:
+            if VERBOSE == True:
                 print(prompt)
-    #prompt += f"<start_of_turn>user\n{message}<end_of_turn>\n<start_of_turn>model\n"
-    prompt+=cust_p.replace("USER_INPUT",message)
+    prompt += cust_p.replace("USER_INPUT", message)
     return prompt
 
-def chat_inf(system_prompt,prompt,history,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,cust_p):
-    #token max=8192
-    print(client_choice)
-    hist_len=0
-    client=clients[int(client_choice)-1]
+def chat_inf(system_prompt, prompt, history, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, cust_p):
+    hist_len = 0
+    client = clients[int(client_choice) - 1]
     if not history:
         history = []
-        hist_len=0
     if not memory:
         memory = []
-        mem_len=0        
+
     if memory:
-        for ea in memory[0-chat_mem:]:
-            hist_len+=len(str(ea))
-    in_len=len(system_prompt+prompt)+hist_len
+        for ea in memory[0 - chat_mem:]:
+            hist_len += len(str(ea))
+    in_len = len(system_prompt + prompt) + hist_len
 
-    if (in_len+tokens) > 8000:
-        history.append((prompt,"Wait, that's too many tokens, please reduce the 'Chat Memory' value, or reduce the 'Max new tokens' value"))
-        yield history,memory
+    if (in_len + tokens) > 8000:
+        history.append((prompt, "Wait, that's too many tokens, please reduce the 'Chat Memory' value, or reduce the 'Max new tokens' value"))
+        yield history, memory
     else:
         generate_kwargs = dict(
-            #temperature=temp,
             max_new_tokens=tokens,
-            #top_p=top_p,
-            #repetition_penalty=rep_p,
-            #do_sample=True,
-            #seed=seed,
         )
         if system_prompt:
-            formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0-chat_mem:],cust_p)
+            formatted_prompt = format_prompt(f"{system_prompt}, {prompt}", memory[0 - chat_mem:], cust_p)
         else:
-            formatted_prompt = format_prompt(prompt, memory[0-chat_mem:],cust_p)
-        
-        
+            formatted_prompt = format_prompt(prompt, memory[0 - chat_mem:], cust_p)
+
+        translated_prompt = translate_to_english(formatted_prompt)
+
         chat = [
-    { "role": "user", "content": f"{formatted_prompt}" },
-    ]
+            {"role": "user", "content": f"{translated_prompt}"},
+        ]
 
-        stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
+        stream = client.text_generation(translated_prompt, **generate_kwargs, stream=True, details=True, return_full_text=True)
         output = ""
         for response in stream:
             output += response.token.text
-            yield [(prompt,output)],memory
-        history.append((prompt,output))
-        memory.append((prompt,output))
-        yield history,memory
-        
-    if VERBOSE==True:
-        print("\n######### HIST "+str(in_len))
-        print("\n######### TOKENS "+str(tokens))        
+            yield [(prompt, output)], memory
+        history.append((prompt, output))
+        memory.append((prompt, output))
+        yield history, memory
 
 def clear_fn():
-    return None,None,None,None
-rand_val=random.randint(1,1111111111111111)
+    return None, None, None, None
 
-def check_rand(inp,val):
-    if inp==True:
-        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1,1111111111111111))
+rand_val = random.randint(1, 1111111111111111)
+
+def check_rand(inp, val):
+    if inp == True:
+        return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=random.randint(1, 1111111111111111))
     else:
         return gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, value=int(val))
-    
+
 with gr.Blocks() as app:
-    memory=gr.State()
+    memory = gr.State()
     chat_b = gr.Chatbot(height=500)
     with gr.Group():
         with gr.Row():
@@ -114,27 +108,26 @@ with gr.Blocks() as app:
                         btn = gr.Button("Chat")
                     with gr.Column(scale=1):
                         with gr.Group():
-                            stop_btn=gr.Button("Stop")
-                            clear_btn=gr.Button("Clear")                
-                client_choice=gr.Dropdown(label="Models",type='index',choices=[c for c in models],value=models[0],interactive=True)
-                with gr.Accordion("Prompt Format",open=False):
-                    custom_prompt=gr.Textbox(label="Modify Prompt Format", info="For testing purposes. 'USER_INPUT' is where 'SYSTEM_PROMPT, PROMPT' will be placed", lines=5,value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model")
+                            stop_btn = gr.Button("Stop")
+                            clear_btn = gr.Button("Clear")
+                client_choice = gr.Dropdown(label="Models", type='index', choices=[c for c in models], value=models[0], interactive=True)
+                with gr.Accordion("Prompt Format", open=False):
+                    custom_prompt = gr.Textbox(label="Modify Prompt Format", info="For testing purposes. 'USER_INPUT' is where 'SYSTEM_PROMPT, PROMPT' will be placed", lines=5, value="<start_of_turn>userUSER_INPUT<end_of_turn><start_of_turn>model")
             with gr.Column(scale=1):
                 with gr.Group():
                     rand = gr.Checkbox(label="Random Seed", value=True)
-                    seed=gr.Slider(label="Seed", minimum=1, maximum=1111111111111111,step=1, value=rand_val)
-                    tokens = gr.Slider(label="Max new tokens",value=1600,minimum=0,maximum=8000,step=64,interactive=True, visible=True,info="The maximum number of tokens")
-                    temp=gr.Slider(label="Temperature",step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-                    top_p=gr.Slider(label="Top-P",step=0.01, minimum=0.01, maximum=1.0, value=0.9)
-                    rep_p=gr.Slider(label="Repetition Penalty",step=0.1, minimum=0.1, maximum=2.0, value=1.0)
-                    chat_mem=gr.Number(label="Chat Memory", info="Number of previous chats to retain",value=4)
-
-    
-    client_choice.change(load_models,client_choice,[chat_b])
-    app.load(load_models,client_choice,[chat_b])
-    
-    chat_sub=inp.submit(check_rand,[rand,seed],seed).then(chat_inf,[sys_inp,inp,chat_b,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,custom_prompt],[chat_b,memory])
-    go=btn.click(check_rand,[rand,seed],seed).then(chat_inf,[sys_inp,inp,chat_b,memory,client_choice,seed,temp,tokens,top_p,rep_p,chat_mem,custom_prompt],[chat_b,memory])
-
-    clear_btn.click(clear_fn,None,[inp,sys_inp,chat_b,memory])
-app.queue(default_concurrency_limit=10).launch()
+                    seed = gr.Slider(label="Seed", minimum=1, maximum=1111111111111111, step=1, value=rand_val)
+                    tokens = gr.Slider(label="Max new tokens", value=1600, minimum=0, maximum=8000, step=64, interactive=True, visible=True, info="The maximum number of tokens")
+                    temp = gr.Slider(label="Temperature", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    top_p = gr.Slider(label="Top-P", step=0.01, minimum=0.01, maximum=1.0, value=0.9)
+                    rep_p = gr.Slider(label="Repetition Penalty", step=0.1, minimum=0.1, maximum=2.0, value=1.0)
+                    chat_mem = gr.Number(label="Chat Memory", info="Number of previous chats to retain", value=4)
+
+    client_choice.change(load_models, client_choice, [chat_b])
+    app.load(load_models, client_choice, [chat_b])
+
+    chat_sub = inp.submit(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory])
+    go = btn.click(check_rand, [rand, seed], seed).then(chat_inf, [sys_inp, inp, chat_b, memory, client_choice, seed, temp, tokens, top_p, rep_p, chat_mem, custom_prompt], [chat_b, memory])
+
+    clear_btn.click(clear_fn, None, [inp, sys_inp, chat_b, memory])
+app.queue(default_concurrency_limit=10).launch()