Update app.py

app.py

@@ -18,60 +18,12 @@
 #     )
 #     return chat_completion.choices[0].message.content
 
-# custom_css = """
-# body {
-#     background-color: #f5f5f5;
-#     font-family: 'Arial', sans-serif;
-#     color: #333;
-# }
-
-# .gradio-container {
-#     border-radius: 12px;
-#     padding: 20px;
-#     background-color: #ffffff;
-#     box-shadow: 0 2px 10px rgba(0, 0, 0, 0.1);
-# }
-
-# input[type="text"], textarea {
-#     border-radius: 10px;
-#     border: 1px solid #ddd;
-#     padding: 12px;
-#     width: 100%;
-#     font-size: 14px;
-#     color: #333;
-#     background-color: #f9f9f9;
-# }
-
-# button {
-#     background-color: #007bff;
-#     color: white;
-#     border: none;
-#     padding: 12px 24px;
-#     border-radius: 10px;
-#     cursor: pointer;
-#     font-size: 14px;
-#     font-weight: bold;
-# }
-
-# button:hover {
-#     background-color: #0056b3;
-# }
-
-# h1 {
-#     font-weight: 600;
-#     color: #333;
-# }
-
-# textarea {
-#     resize: none;
-# }
-# """
 
 # 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 Chatbot",
+#     title="💬 Parviz GPT",
 #     description="زنده باد",
 #     theme="dark",
 #     allow_flagging="never"
@@ -79,36 +31,36 @@
 # )
 # iface.launch()
 
-# import gradio as gr
-# from groq import Groq
-# import time
+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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")
 
-# def generate_response(message, chat_history):
-#     chat_completion = client.chat.completions.create(
-#         messages=[{"role": "user", "content": message}],
-#         model="llama3-8b-8192",
-#     )
-#     bot_message = chat_completion.choices[0].message.content
+def generate_response(message, chat_history):
+    chat_completion = client.chat.completions.create(
+        messages=[{"role": "user", "content": message}],
+        model="llama3-8b-8192",
+    )
+    bot_message = chat_completion.choices[0].message.content
     
-#     for i in range(0, len(bot_message), 10):
-#         yield chat_history + [(message, bot_message[:i + 10])]
-#         time.sleep(0.1)
+    for i in range(0, len(bot_message), 10):
+        yield chat_history + [(message, bot_message[:i + 10])]
+        time.sleep(0.1)
     
-#     yield chat_history + [(message, bot_message)]
+    yield chat_history + [(message, bot_message)]
 
 
-# with gr.Blocks() as demo:
-#     gr.Markdown("<h1 style='text-align: center;'>💬 Parviz Chatbot</h1><p style='text-align: center; color: #e0e0e0;'>زنده باد</p>")
+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>")
     
-#     chatbot = gr.Chatbot(label="جواب")
-#     msg = gr.Textbox(label="ورودی", placeholder="اینجا یه چی بپرس... ", lines=1)
+    chatbot = gr.Chatbot(label="جواب")
+    msg = gr.Textbox(label="ورودی", placeholder="اینجا یه چی بپرس... ", lines=1)
     
-#     msg.submit(generate_response, [msg, chatbot], chatbot)
+    msg.submit(generate_response, [msg, chatbot], chatbot)
     
-#     clear = gr.ClearButton([msg, chatbot])
-# demo.launch()
+    clear = gr.ClearButton([msg, chatbot])
+demo.launch()
 
 
 
@@ -117,60 +69,53 @@
 
 
 
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
-import re
-import time
 
 
-tokenizer = AutoTokenizer.from_pretrained("universitytehran/PersianMind-v1.0")
-model = AutoModelForSeq2SeqLM.from_pretrained("universitytehran/PersianMind-v1.0")
 
 
-def generate_response(message, chat_history):
 
-    TEMPLATE = "{context}\nYou: {prompt}\nParvizGPT "
-    CONTEXT = "This is a conversation with ParvizGPT. It is an artificial intelligence model designed by Amir Mahdi Parviz " \
-        "NLP expert to help you with various tasks such as answering questions, " \
-        "providing recommendations, and helping with decision making. You can ask it anything you want and " \
-        "it will do its best to give you accurate and relevant information."
-    
-    prompt = TEMPLATE.format(context=CONTEXT, prompt=message)
-
-    generation_config = GenerationConfig(
-        max_new_tokens=128,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        temperature=0.8,
-        repetition_penalty=1.2
-    )
-
-    tokenized_test_text = tokenizer(prompt, return_tensors='pt').input_ids.to("cpu")
-    model.to("cpu")
-    
-
-    outputs = model.generate(tokenized_test_text, generation_config=generation_config, max_new_tokens=128)
-    result = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-
-    for i in range(0, len(result), 10):
-        yield chat_history + [(message, result[:i + 10])]
-        time.sleep(0.1)
+# 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")
 
-    yield chat_history + [(message, result)]
+# 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><p style='text-align: center;'>made by A.M.Parviz \</p>")
-    
-    chatbot = gr.Chatbot(label="جواب")
-    msg = gr.Textbox(label="ورودی", placeholder="سوال خودتو رو بپرس", lines=1)
-    
-    msg.submit(generate_response, [msg, chatbot], chatbot)
-    
-    clear = gr.ClearButton([msg, chatbot])
-
-demo.launch()
+# 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()