Update app.py

app.py

@@ -63,22 +63,117 @@
 #     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)
+
 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
+# Define the system prompt as an AI Dermatologist
 def format_prompt(message, history):
-    prompt = "<s>"  # Beginning of sequence for formatting
+    prompt = "<s>"
+    # Start the conversation with a system message
+    prompt += "[INST] You are an AI Dermatologist designed to assist users with skin and hair care. Provide thoughtful, medically-informed advice on various skin and hair conditions. Offer diet recommendations, treatment options, and suggest appropriate skincare or haircare routines based on the user's symptoms. Always ensure that your advice is educational, supportive, and easy to understand, and recommend consulting a dermatologist for personalized medical guidance if necessary [/INST]"
     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
+    prompt += f"[INST] {message} [/INST]"
     return prompt
 
-# Function to generate responses while keeping conversation context
+# Function to generate responses with the AI Dermatologist context
 def generate(
     prompt, history, temperature=0.9, max_new_tokens=256, top_p=0.95, repetition_penalty=1.0
 ):
@@ -93,20 +188,20 @@ def generate(
         top_p=top_p,
         repetition_penalty=repetition_penalty,
         do_sample=True,
-        seed=42,  # Seed for reproducibility
+        seed=42,
     )
 
-    # 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
+        yield output
+    return output
 
 # Customizable input controls for the chatbot interface
 additional_inputs = [
@@ -148,10 +243,10 @@ additional_inputs = [
     )
 ]
 
-# Define the chatbot interface with interactive sliders and chatbot panel
+# Define the chatbot interface with the starting system message as AI Dermatologist
 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"""
+    title="AI Dermatologist"
 ).launch(show_api=False)