Update app.py

app.py

@@ -1,46 +1,21 @@
-
-# import gradio as gr
-# from groq import Groq
-
-# client = Groq(
-#     api_key=("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"),
-# )
-
-# def generate_response(input_text):
-#     chat_completion = client.chat.completions.create(
-#         messages=[
-#             {
-#                 "role": "user",
-#                 "content": input_text,
-#             }
-#         ],
-#         model="llama3-8b-8192",
-#     )
-#     return chat_completion.choices[0].message.content
-
-
-# iface = gr.Interface(
-#     fn=generate_response,
-#     inputs=gr.Textbox(label="ورودی" , lines=2, placeholder="اینجا یه چی بپرس... "),
-#     outputs=gr.Textbox(label="جواب"),
-#     title="💬 Parviz GPT",
-#     description="زنده باد",
-#     theme="dark",
-#     allow_flagging="never"
-
-# )
-# iface.launch()
-
 import gradio as gr
 from groq import Groq
 import time
 
-client = Groq(api_key="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")
+client = Groq(api_key="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")
+
+CONTEXT = (
+    "This is a conversation with ParvizGPT. It is an artificial intelligence model designed by Amir Mahdi Parviz, "
+    "an NLP expert, to help you with various tasks such as answering questions in persian, "
+    "providing recommendations, and assisting with decision-making. Ask it anything!"
+)
 
 def generate_response(message, chat_history):
+    full_message = CONTEXT + f"\nYou: {message}به فارسی بگو\nParvizGPT: "
+    
     chat_completion = client.chat.completions.create(
-        messages=[{"role": "user", "content": message}],
-        model="llama3-8b-8192",
+        messages=[{"role": "user", "content": full_message}],
+        model= "llama-3.1-8b-instant",
     )
     bot_message = chat_completion.choices[0].message.content
     
@@ -50,7 +25,6 @@ def generate_response(message, chat_history):
     
     yield chat_history + [(message, bot_message)]
 
-
 with gr.Blocks() as demo:
     gr.Markdown("<h1 style='text-align: center;'>💬 Parviz GPT</h1><p style='text-align: center; color: #e0e0e0;'>زنده باد</p>")
     
@@ -60,62 +34,5 @@ with gr.Blocks() as demo:
     msg.submit(generate_response, [msg, chatbot], chatbot)
     
     clear = gr.ClearButton([msg, chatbot])
-demo.launch()
-
-
-
-
-
-
-
-
-
-
-
-
-
-# import gradio as gr
-# import torch
-# from transformers import AutoTokenizer, AutoModelForCausalLM
-
-# tokenizer = AutoTokenizer.from_pretrained("universitytehran/PersianMind-v1.0", use_fast=True)
-# model = AutoModelForCausalLM.from_pretrained(
-#     "universitytehran/PersianMind-v1.0",
-#     torch_dtype=torch.bfloat16
-# ).to("cpu")
-
-# CONTEXT = (
-#     "This is a conversation with ParvizGPT. It is an artificial intelligence model designed by Amir Mahdi Parviz, "
-#     "an NLP expert, to help you with various tasks such as answering questions, "
-#     "providing recommendations, and assisting with decision-making. Ask it anything!"
-# )
-# pretokenized_context = tokenizer(CONTEXT, return_tensors="pt").input_ids.to("cpu")
-
-# def generate_response(message, chat_history):
-#     prompt = torch.cat(
-#         [pretokenized_context, tokenizer("\nYou: " + message + "\nParvizGPT: ", return_tensors="pt").input_ids.to("cpu")],
-#         dim=1
-#     )
-
-#     with torch.no_grad():
-#         outputs = model.generate(
-#             prompt,
-#             max_new_tokens=32,
-#             temperature=0.6,
-#             top_k=20,
-#             top_p=0.8,
-#             do_sample=True
-#         )
-
-#     result = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     response = result.split("ParvizGPT:")[-1].strip()
-#     return chat_history + [(message, response)]
-
-# with gr.Blocks() as demo:
-#     gr.Markdown("<h1 style='text-align: center;'>💬 Parviz GPT</h1>")
-#     chatbot = gr.Chatbot(label="Response")
-#     msg = gr.Textbox(label="Input", placeholder="Ask your question...", lines=1)
-#     msg.submit(generate_response, [msg, chatbot], chatbot)
-#     gr.ClearButton([msg, chatbot])
-
-# demo.launch()
+    
+demo.launch()