Update app.py

app.py

@@ -1,11 +1,10 @@
 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("meta-llama/Llama-3.2-3B-Instruct")
-
+# Define the InferenceClient for different models
+client_chatgpt = InferenceClient("openai/gpt-3.5-turbo")  # Example for ChatGPT model
+client_llama = InferenceClient("meta-llama/Llama-3.2-3B-Instruct")  # Llama model
+client_claude = InferenceClient("anthropic/claude-1")  # Claude model (adjust with correct model path)
 
 def respond(
     message,
@@ -27,7 +26,7 @@ def respond(
 
     response = ""
 
-    for message in client.chat_completion(
+    for message in client_llama.chat_completion(  # Defaulting to Llama, update dynamically later
         messages,
         max_tokens=max_tokens,
         stream=True,
@@ -39,6 +38,41 @@ def respond(
         response += token
         yield response
 
+# Function to handle button clicks for different models
+def on_button_click(model_name, message, history, system_message, max_tokens, temperature, top_p):
+    # Choose the client based on the selected model
+    if model_name == "Chatgpt":
+        client = client_chatgpt
+    elif model_name == "Llama":
+        client = client_llama
+    elif model_name == "Claude":
+        client = client_claude
+    else:
+        return "Unknown model selected."
+
+    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 = ""
+
+    # Call the selected model for completion
+    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
 
 # CSS for styling the interface
 css = """
@@ -46,7 +80,6 @@ body {
     background-color: #06688E; /* Dark background */
     color: white; /* Text color for better visibility */
 }
-
 .gr-button {
     background-color: #42B3CE !important; /* White button color */
     color: black !important; /* Black text for contrast */
@@ -54,39 +87,32 @@ body {
     padding: 8px 16px !important;
     border-radius: 5px !important;
 }
-
 .gr-button:hover {
     background-color: #e0e0e0 !important; /* Slightly lighter button on hover */
 }
-
 .gr-slider-container {
     color: white !important; /* Slider labels in white */
 }
 """
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Interface using Blocks context
 with gr.Blocks() as demo:
-    # Add all your components here, including buttons
-    gr.Textbox(value="You are a virtual health assistant designed to provide accurate and reliable information related to health, wellness, and medical topics. Your primary goal is to assist users with their health-related queries, offer general guidance, and suggest when to consult a licensed medical professional. If a user asks a question that is unrelated to health, wellness, or medical topics, respond politely but firmly with: 'I'm sorry, I can't help with that because I am a virtual health assistant designed to assist with health-related needs. Please let me know if you have any health-related questions.' Never provide advice or information outside the health domain. Remain professional, empathetic, and clear in all responses. Always prioritize user safety and encourage professional medical consultation for critical or complex health concerns.", label="System message", visible=False)
+    # Add your components including buttons
+    gr.Textbox(value="You are a virtual health assistant...", label="System message", visible=False)
     
-    # Create buttons with event handlers
-    gr.Button("Chatgpt").click(on_button_click, inputs="Chatgpt", outputs="text")
-    gr.Button("Llama").click(on_button_click, inputs="Llama", outputs="text")
-    gr.Button("Claude").click(on_button_click, inputs="Claude", outputs="text")
+    # Buttons to select the model
+    gr.Button("Chatgpt").click(on_button_click, inputs=["Chatgpt", "textbox", "history", "system_message", "max_tokens", "temperature", "top_p"], outputs="text")
+    gr.Button("Llama").click(on_button_click, inputs=["Llama", "textbox", "history", "system_message", "max_tokens", "temperature", "top_p"], outputs="text")
+    gr.Button("Claude").click(on_button_click, inputs=["Claude", "textbox", "history", "system_message", "max_tokens", "temperature", "top_p"], outputs="text")
     
+    # Sliders for max tokens, temperature, and top-p
     gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens", visible=False)
     gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature", visible=False)
     gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)", visible=False)
-
-    # Optional: customize your layout with CSS if needed
-    css = """
-    /* Your custom CSS here */
-    """
     
-    demo.css = css,  # Pass the custom CSS here
-
+    # Apply the custom CSS for styling
+    demo.css = css
 
+# Launch the app
 if __name__ == "__main__":
     demo.launch(share=True)