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gradio_app.py

@@ -16,7 +16,6 @@ client = Client("http://20.83.177.108:8080")
 # print(text)
 
 
-
 def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
     # Get the model and tokenizer, and tokenize the user text.
     user_text = f"""You are an expert legal assistant with extensive knowledge about Indian law. Your task is to respond to the given query in a consice and factually correct manner. Also mention the relevant sections of the law wherever applicable.
@@ -24,7 +23,7 @@ def run_generation(user_text, top_p, temperature, top_k, max_new_tokens):
     ### Response: """
 
     text = ""
-    for response in client.generate_stream(user_text, max_new_tokens=max_new_tokens,repetition_penalty=1.05):
+    for response in client.generate_stream(user_text, max_new_tokens=max_new_tokens, repetition_penalty=1.05):
         if not response.token.special:
             text += response.token.text
             yield text
@@ -43,7 +42,8 @@ with gr.Blocks() as demo:
                 placeholder="What is the punishment for taking dowry. explain in detail.",
                 label="Question"
             )
-            model_output = gr.Textbox(label="AI Response", lines=10, interactive=False)
+            model_output = gr.Textbox(
+                label="AI Response", lines=10, interactive=False)
             button_submit = gr.Button(value="Submit")
 
         with gr.Column(scale=1):
@@ -60,8 +60,9 @@ with gr.Blocks() as demo:
                 minimum=0.1, maximum=1.0, value=0.8, step=0.1, interactive=True, label="Temperature",
             )
 
-    user_text.submit(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
-    button_submit.click(run_generation, [user_text, top_p, temperature, top_k, max_new_tokens], model_output)
-
-    demo.queue(max_size=32).launch(enable_queue=True,share=True)
+    user_text.submit(run_generation, [
+                     user_text, top_p, temperature, top_k, max_new_tokens], model_output)
+    button_submit.click(run_generation, [
+                        user_text, top_p, temperature, top_k, max_new_tokens], model_output)
 
+    demo.queue(max_size=32).launch(enable_queue=True)