Update app.py

app.py

@@ -73,11 +73,10 @@ def generate(
     chat_history: list[tuple[str, str]],
     #system_prompt: str,
     max_new_tokens: int = 1024,
-    do_sample: bool = False,
-    #temperature: float = 0.1,
-    #top_p: float = 0.9,
-    #top_k: int = 50,
-    #repetition_penalty: float = 1.2,
+    temperature: float = 0.1,
+    top_p: float = 0.9,
+    top_k: int = 50,
+    repetition_penalty: float = 1.2,
 ) -> Iterator[str]:
     model1 = SentenceTransformer('intfloat/multilingual-e5-large')
     embeddings_query = model1.encode('query: '+str(message), normalize_embeddings=True)
@@ -105,12 +104,11 @@ def generate(
         {"input_ids": input_ids},
         streamer=streamer,
         max_new_tokens=max_new_tokens,
-        do_sample=False,
-        #top_p=top_p,
-        #top_k=top_k,
-        #temperature=temperature,
-        #num_beams=1,
-        #repetition_penalty=repetition_penalty,
+        top_p=top_p,
+        top_k=top_k,
+        temperature=temperature,
+        num_beams=1,
+        repetition_penalty=repetition_penalty,
     )
     t = Thread(target=model.generate, kwargs=generate_kwargs)
     t.start()
@@ -145,7 +143,7 @@ with gr.Blocks(css="style.css") as demo:
     gr.Markdown("# AvvoChat")
     gr.Markdown("Fai una domanda riguardante la legge italiana all'AvvoChat e ricevi una spiegazione semplice al tuo dubbio.")
     with gr.Row():
-        with gr.Column(scale=0.5, min_width = 100):
+        with gr.Column(scale=1, min_width = 100):
             gr.Image("AvvoVhat.png", width = 50, height=200, 
                      show_label=False, show_share_button=False, show_download_button=False, container=False),
         with gr.Column(scale=6):