Update app.py

app.py

@@ -1,45 +1,45 @@
-import gradio as gr
-from huggingface_hub import hf_hub_download
-from llama_cpp import Llama
-
-# download model
-model_name_or_path = "FabioSantos/llama3Finetune_unsloth" # repo id
-# 4bit
-model_basename = "llama3Finetune_unsloth-unsloth.Q8_0.gguf" # file name
-
-model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
-print(model_path)
-
-lcpp_llm = Llama(
-    model_path=model_path,
-    n_threads=2, # CPU cores
-    n_batch=512, # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
-    n_gpu_layers=43, # Change this value based on your model and your GPU VRAM pool.
-    n_ctx=4096, # Context window
-)
-
-prompt_template = "Responda as questões.\nHuman: {prompt}\nAssistant:\n"
-
-def get_response(text):
-    prompt = prompt_template.format(prompt=text)
-    response = lcpp_llm(
-        prompt=prompt,
-        max_tokens=256,
-        temperature=0.5,
-        top_p=0.95,
-        top_k=50,
-        stop=[''], # Dynamic stopping when such token is detected.
-        echo=True # return the prompt
-    )
-    return response['choices'][0]['text'].split('Assistant:\n')[1]
-
-interface = gr.Interface(
-    fn=get_response,
-    inputs="text",
-    outputs="text",
-    title="Assistente Virtual",
-    description="Forneça uma questão e visualize a resposta do assistente."
-)
-
-if __name__ == "__main__":
-    interface.launch()
+import gradio as gr
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+# download model
+model_name_or_path = "FabioSantos/llama3Finetune_unsloth" # repo id
+# 4bit
+model_basename = "llama3Finetune_unsloth-unsloth.Q8_0.gguf" # file name
+
+model_path = hf_hub_download(repo_id=model_name_or_path, filename=model_basename)
+print(model_path)
+
+lcpp_llm = Llama(
+    model_path=model_path,
+    n_threads=2, # CPU cores
+    n_batch=512, # Should be between 1 and n_ctx, consider the amount of VRAM in your GPU.
+    n_gpu_layers=43, # Change this value based on your model and your GPU VRAM pool.
+    n_ctx=4096, # Context window
+)
+
+prompt_template = "Responda as questões.\nHuman: {prompt}\nAssistant:\n"
+
+def get_response(text):
+    prompt = prompt_template.format(prompt=text)
+    response = lcpp_llm(
+        prompt=prompt,
+        max_tokens=256,
+        temperature=0.5,
+        top_p=0.95,
+        top_k=50,
+        stop=[''], # Dynamic stopping when such token is detected.
+        echo=True # return the prompt
+    )
+    return response
+
+interface = gr.Interface(
+    fn=get_response,
+    inputs="text",
+    outputs="text",
+    title="Assistente Virtual",
+    description="Forneça uma questão e visualize a resposta do assistente."
+)
+
+if __name__ == "__main__":
+    interface.launch()