Upload app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, GenerationConfig
+import re
+import time
+
+
+tokenizer = AutoTokenizer.from_pretrained("GIGAParviz/T5_fa_law_chatbot")
+model = AutoModelForSeq2SeqLM.from_pretrained("GIGAParviz/T5_fa_law_chatbot")
+
+
+
+
+def clear_text(text):
+    if isinstance(text, str):
+        text = re.sub("[^آ-ی۰-۹]+", " ", text)
+    else:
+        text = str(text)
+    return text
+
+
+def generate_response(message, chat_history):
+
+
+    question = clear_text(message)
+    start = "Answer The Question in farsi: "
+    end = "Answer: "
+    prompt = [start + question + end]
+
+    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.decode(outputs[0], skip_special_tokens=True)
+
+    for i in range(0, len(result), 10):
+        yield chat_history + [(message, result[:i + 10])]
+        time.sleep(0.1)
+
+    yield chat_history + [(message, result)]
+
+
+
+with gr.Blocks() as demo:
+    gr.Markdown("<h1 style='text-align: center;'>💬 Legal Chatbot</h1><p style='text-align: center;'>made by A.M.Parviz \</p>")
+    
+    chatbot = gr.Chatbot(label="جواب")
+    msg = gr.Textbox(label="ورودی", placeholder="سوال حقوقی خودتون رو بپرسید", lines=1)
+    
+    msg.submit(generate_response, [msg, chatbot], chatbot)
+    
+    clear = gr.ClearButton([msg, chatbot])
+
+demo.launch()