Update app.py

app.py

@@ -2,17 +2,16 @@ from huggingface_hub import InferenceClient
 import gradio as gr
 from deep_translator import GoogleTranslator
 
-# Initialize the Hugging Face client with your model
 client = InferenceClient("mistralai/Mixtral-8x7B-Instruct-v0.1")
 
-# Define the translation functions
 def translate_to_arabic(text):
-    return GoogleTranslator(source='auto', target='ar').translate(text)
+    translator = GoogleTranslator(source='auto', target='ar')
+    return translator.translate(text)
 
 def translate_to_english(text):
-    return GoogleTranslator(source='auto', target='en').translate(text)
+    translator = GoogleTranslator(source='auto', target='en')
+    return translator.translate(text)
 
-# Format the prompt for the model
 def format_prompt(message, history):
     prompt = "<s>"
     for user_prompt, bot_response in history:
@@ -21,50 +20,28 @@ def format_prompt(message, history):
     prompt += f"[INST] {message} [/INST]"
     return prompt
 
-# Generate a response from the model
-def generate(prompt, history=[], temperature=0.1, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0):
-    # Translate the Arabic prompt to English before sending to the model
+def generate(prompt, history=[]):
+    # Translate the Arabic prompt to English
     prompt_in_english = translate_to_english(prompt)
+    # Call the format_prompt function to format the input for the model
     formatted_prompt = format_prompt(prompt_in_english, history)
-
-    generate_kwargs = {
-        "temperature": temperature,
-        "max_new_tokens": max_new_tokens,
-        "top_p": top_p,
-        "repetition_penalty": repetition_penalty,
-        "do_sample": True,
-        "seed": 42,  # Seed for reproducibility, remove or change if randomness is preferred
-    }
-
-    # Generate the response
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+    
+    # Generate the response from the model
+    stream = client.text_generation(formatted_prompt, stream=True, details=True, return_full_text=False)
     
     output = ""
     for response in stream:
         output += response["token"]["text"]
-
+    
     # Translate the English response back to Arabic
     response_in_arabic = translate_to_arabic(output)
     return response_in_arabic
 
-# Define additional inputs for Gradio interface
-additional_inputs = [
-    gr.Slider(label="Temperature", value=0.9, minimum=0.0, maximum=1.0, step=0.05),
-    gr.Slider(label="Max new tokens", value=256, minimum=0, maximum=1048, step=64),
-    gr.Slider(label="Top-p (nucleus sampling)", value=0.90, minimum=0.0, maximum=1.0, step=0.05),
-    gr.Slider(label="Repetition penalty", value=1.2, minimum=1.0, maximum=2.0, step=0.05)
-]
-
-# Set up the Gradio interface
 iface = gr.Interface(
     fn=generate,
-    inputs=[
-        gr.Textbox(lines=5, placeholder='Type your Arabic query here...', label='Arabic Query'),
-        *additional_inputs
-    ],
+    inputs=[gr.Textbox(lines=5, placeholder='Type your Arabic query here...', label='Arabic Query')],
     outputs='text',
-    title="DorjGPT Arabic-English Translation Chatbot",
+    title="DorjGPT Arabic-English Translation Chatbot"
 )
 
-# Launch the Gradio interface
 iface.launch()