Update app.py

app.py

@@ -1,9 +1,11 @@
 import gradio as gr
 from transformers import pipeline
+from huggingface_hub import InferenceClient
 # from IPython.display import Audio as IPythonAudio
 
 playground = gr.Blocks()
 
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 image_pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
 summary_pipe = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
 ner_pipe = pipeline("ner", model="dslim/bert-base-NER")
@@ -19,6 +21,32 @@ ner_pipe = pipeline("ner", model="dslim/bert-base-NER")
 #     audio = IPythonAudio(audio_data, rate=sampling_rate)
 #     return audio_data, sampling_rate
 
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        stream=True,
+        temperature=temperature,
+        top_p=top_p,
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
+
+
 def launch_image_pipe(input):
     out = image_pipe(input)
     text = out[0]['generated_text']
@@ -163,6 +191,27 @@ with playground:
                     ], inputs=[ner_text_input], outputs=[ner_text_output], run_on_click=True, cache_examples=True, fn=ner)
  
             ner_pipeline_button.click(ner, inputs=[ner_text_input], outputs=[ner_text_output])
+
+        ## ================================================================================================================================
+        ## Find entities
+        ## ================================================================================================================================
+        with gr.TabItem("Chatbot"):
+            gr.ChatInterface(
+                respond,
+                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)",
+                    ),
+                ],
+            )
+
             
     create_playground_footer()