Update app.py

app.py

@@ -1,73 +1,12 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-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
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = 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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
-
 # import gradio as gr
 # from huggingface_hub import InferenceClient
 
+# """
+# For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
+# """
 # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
-# # Function to respond to user input while maintaining chat history
+
 # def respond(
 #     message,
 #     history: list[tuple[str, str]],
@@ -76,43 +15,37 @@ if __name__ == "__main__":
 #     temperature,
 #     top_p,
 # ):
-#     try:
-#         # Append system message at the start
-#         messages = [{"role": "system", "content": system_message}]
-
-#         # Append the chat history
-#         for user_msg, bot_reply in history:
-#             if user_msg:
-#                 messages.append({"role": "user", "content": user_msg})
-#             if bot_reply:
-#                 messages.append({"role": "assistant", "content": bot_reply})
-
-#         # Add the latest user message
-#         messages.append({"role": "user", "content": message})
-
-#         response = ""
-
-#         # Stream the response token by token to avoid loading delays
-#         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
-
-#     except Exception:
-#         # Handle any error silently (without showing the error icon or message)
-#         yield "An internal error occurred. But let's continue."  # Custom message or silent
-
-# # Gradio interface for customizable chatbot behavior
+#     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
+
+# """
+# For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
+# """
 # demo = gr.ChatInterface(
-#     fn=respond,
+#     respond,
 #     additional_inputs=[
-#         gr.Textbox(value="You are an AI dermatologist Chatbot.", label="System message"),
+#         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(
@@ -125,7 +58,100 @@ if __name__ == "__main__":
 #     ],
 # )
 
+
 # if __name__ == "__main__":
 #     demo.launch()
 
 
+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Initialize the client with your desired model
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Format the conversation prompt with history
+def format_prompt(message, history):
+    prompt = "<s>"  # Beginning of sequence for formatting
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST]"
+        prompt += f" {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"  # Format current user message
+    return prompt
+
+# Function to generate responses while keeping conversation context
+def generate(
+    prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0
+):
+    temperature = float(temperature)
+    if temperature < 1e-2:
+        temperature = 1e-2
+    top_p = float(top_p)
+
+    generate_kwargs = dict(
+        temperature=temperature,
+        max_new_tokens=max_new_tokens,
+        top_p=top_p,
+        repetition_penalty=repetition_penalty,
+        do_sample=True,
+        seed=42,  # Seed for reproducibility
+    )
+
+    # Format the prompt with the history and current message
+    formatted_prompt = format_prompt(prompt, history)
+
+    # Stream the generated response
+    stream = client.text_generation(
+        formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False
+    )
+    output = ""
+    for response in stream:
+        output += response.token.text
+        yield output  # Yield the streamed output as it's generated
+
+# Customizable input controls for the chatbot interface
+additional_inputs = [
+    gr.Slider(
+        label="Temperature",
+        value=0.9,
+        minimum=0.0,
+        maximum=1.0,
+        step=0.05,
+        interactive=True,
+        info="Higher values produce more diverse outputs",
+    ),
+    gr.Slider(
+        label="Max new tokens",
+        value=256,
+        minimum=0,
+        maximum=1048,
+        step=64,
+        interactive=True,
+        info="The maximum numbers of new tokens",
+    ),
+    gr.Slider(
+        label="Top-p (nucleus sampling)",
+        value=0.90,
+        minimum=0.0,
+        maximum=1,
+        step=0.05,
+        interactive=True,
+        info="Higher values sample more low-probability tokens",
+    ),
+    gr.Slider(
+        label="Repetition penalty",
+        value=1.2,
+        minimum=1.0,
+        maximum=2.0,
+        step=0.05,
+        interactive=True,
+        info="Penalize repeated tokens",
+    )
+]
+
+# Define the chatbot interface with interactive sliders and chatbot panel
+gr.ChatInterface(
+    fn=generate,
+    chatbot=gr.Chatbot(show_label=False, show_share_button=False, show_copy_button=True, likeable=True, layout="panel"),
+    additional_inputs=additional_inputs,
+    title="""AI Dermatologist Chatbot"""
+).launch(show_api=False)