Update app.py

app.py

@@ -15,52 +15,26 @@ app = gr.load(
     ]
 ).launch()
 """
-"""
-# Pipeline
-
-import gradio as gr
-from transformers import pipeline
-
-pipe = pipeline(model = "google/gemma-2-2b-it")
 
-def fn(input):
-    output = pipe(
-        input,
-        max_new_tokens = 2048
-    )
-    return output[0]["generated_text"]#[len(input):]
-
-app = gr.Interface(
-    fn = fn,
-    inputs = [gr.Textbox(label = "Input")],
-    outputs = [gr.Textbox(label = "Output")],
-    title = "Google Gemma",
-    description = "Pipeline",
-    examples = [
-        ["Hello, World."]
-    ]
-).launch()
-"""
+# Inference Client
 
 import gradio as gr
 from huggingface_hub import InferenceClient
-import os
+#import os
 
 #hf_token = os.getenv("HF_TOKEN")
 
 client = InferenceClient("google/gemma-2-2b-it")
 
-def respond(
+def fn_chat(
     message,
     history: list[tuple[str, str]],
     #system_message,
-    ##user_message,
     max_tokens,
     temperature,
     top_p,
 ):
     #messages = [{"role": "system", "content": system_message}]
-    ##messages = [{"role": "user", "content": user_message}]
     messages = []
 
     for val in history:
@@ -75,61 +49,55 @@ def respond(
 
     for message in client.chat_completion(
         messages,
-        max_tokens=max_tokens,
-        temperature=temperature,
-        top_p=top_p,
-        stream=True,
+        max_tokens = max_tokens,
+        temperature = temperature,
+        top_p = top_p,
+        stream = True,
     ):
         token = message.choices[0].delta.content
 
         response += token
         yield response
 
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
+app = gr.ChatInterface(
+    fn = fn_chat,
+    additional_inputs = [
         #gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
     ],
-)
-
+    title = "Google Gemma",
+    description = "Chatbot",
+    examples = [
+        ["Hello, World."]
+    ]
+).launch
+"""
 if __name__ == "__main__":
     demo.launch()
-
 """
-client = InferenceClient(api_key=hf_token)
+"""
+# Pipeline
 
-def fn(prompt, history=[]):
-    messages = []
-    
-    for user_prompt, bot_response in history:
-        messages.append({"role": "user", "content": user_prompt})
-        messages.append({"role": "bot", "content": bot_response})
-    
-    messages.append({"role": "user", "content": prompt})
-
-    stream = client.chat.completions.create(
-        model = "google/gemma-2-2b-it", 
-        messages = messages, 
-        #temperature = 0.5,
-        #max_tokens = 2048,
-        #top_p = 0.7,
-        stream = True
-    )
+import gradio as gr
+from transformers import pipeline
 
-    bot_response = "".join(chunk.choices[0].delta.content for chunk in stream)
+pipe = pipeline(model = "google/gemma-2-2b-it")
 
-    history.append((prompt, bot_response))
-    return bot_response, history
+def fn(input):
+    output = pipe(
+        input,
+        max_new_tokens = 2048
+    )
+    return output[0]["generated_text"]#[len(input):]
 
 app = gr.Interface(
-    fn = fn, 
+    fn = fn,
     inputs = [gr.Textbox(label = "Input")],
     outputs = [gr.Textbox(label = "Output")],
     title = "Google Gemma",
-    description = "Chatbot",
+    description = "Pipeline",
     examples = [
         ["Hello, World."]
     ]