app update

app.py

@@ -6,29 +6,52 @@ model_path = "finetuned_phi2"
 model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True)
 tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
 
+num_new_tokens = 200  # change to the number of new tokens you want to generate
 
+DESCRIPTION = """\
+# 🧑🏽‍💻Microsoft Phi2 Chatbot🤖
+This Space demonstrates model [Microsoft Phi2 2.7B](https://huggingface.co/microsoft/phi-2), a model with 2.78B parameters fine-tuned for chat instructions. Feel free to play with it, or duplicate to run generations without a queue! If you want to run your own service, you can also [deploy the model on Inference Endpoints](https://huggingface.co/inference-endpoints).
+🔎 For more details about the finetuning, take a look at the [GitHub](https://github.com/mkthoma/llm_finetuning) code.
+"""
 
-def generate(question, context):
+LICENSE = """
+As a derivate work of [Microsoft Phi2 2.7B](https://huggingface.co/microsoft/phi-2), this demo is governed by the original [license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE).
+"""
+
+def generate(question, context, max_new_tokens = 200, temperature = 0.6):
+  
     system_message = "You are a question answering chatbot. Provide a clear and detailed explanation"
     prompt = f"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n\n {question} [/INST]" # replace the command here with something relevant to your task
-    
-    num_new_tokens = 200  # change to the number of new tokens you want to generate
+
     # Count the number of tokens in the prompt
     num_prompt_tokens = len(tokenizer(prompt)['input_ids'])
     # Calculate the maximum length for the generation
-    max_length = num_prompt_tokens + num_new_tokens
-    
-    gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length)
+    max_length = num_prompt_tokens + max_new_tokens
+
+    gen = pipeline('text-generation', model=model, tokenizer=tokenizer, max_length=max_length, temperature=temperature)
     result = gen(prompt)
     return (result[0]['generated_text'].replace(prompt, ''))
-    
+
 
 bbchatbot = gr.Chatbot(
-    avatar_images=["logo/user logo.png", "logo/bot logo.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
+    avatar_images=["/content/logo/user logo.png", "/content/logo/bot logo.png"], bubble_full_width=False, show_label=False, show_copy_button=True, likeable=True,)
 
-demo = gr.ChatInterface(fn=generate, 
+examples = [["What is a large language model?"], ["How to calm down a person?"], ["What is aritificial intelligence?"], ["How to write a good resume?"]]
+
+additional_inputs =  additional_inputs=[gr.Slider(label="Max new tokens",minimum=100,maximum=2048,step=50,value=num_new_tokens),
+                                        gr.Slider(label="Temperature",minimum=0.1,maximum=4.0,step=0.1,value=0.6)]
+
+chat_interface  = gr.ChatInterface(fn=generate,
+                        additional_inputs=additional_inputs,
                         chatbot=bbchatbot,
-                        title="🧑🏽‍💻Microsoft Phi2 Chatbot🤖"
+                        title="",
+                        examples=examples
                        )
 
+with gr.Blocks(css="style.css") as demo:
+    gr.Markdown(DESCRIPTION)
+    gr.DuplicateButton(value="Duplicate for private use", elem_id="duplicate-button")
+    chat_interface.render()
+    gr.Markdown(LICENSE)
+
 demo.queue().launch(show_api=False)