Upload app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+
+# Default clients for each model
+clients = {
+    "mistralai/Mistral-7B-Instruct-v0.3": InferenceClient("mistralai/Mistral-7B-Instruct-v0.3"),
+    "BICORP/Lake-1-Advanced": InferenceClient("BICORP/Lake-1-Advanced")
+}
+
+# Define presets for each model
+presets = {
+    "mistralai/Mistral-7B-Instruct-v0.3": {
+        "Fast": {"max_tokens": 256, "temperature": 1.0, "top_p": 0.8},
+        "Normal": {"max_tokens": 512, "temperature": 0.6, "top_p": 0.75},
+        "Quality": {"max_tokens": 1024, "temperature": 0.45, "top_p": 0.60},
+        "Unreal Performance": {"max_tokens": 1048, "temperature": 0.5, "top_p": 0.7},
+    },
+    "BICORP/Lake-1-Advanced": {
+        "Fast": {"max_tokens": 800, "temperature": 1.0, "top_p": 0.9},
+        "Normal": {"max_tokens": 4000, "temperature": 0.7, "top_p": 0.95},
+        "Quality": {"max_tokens": 32000, "temperature": 0.5, "top_p": 0.90},
+        "Unreal Performance": {"max_tokens": 128000, "temperature": 0.6, "top_p": 0.75},
+    }
+}
+
+# System messages for each model
+system_messages = {
+    "mistralai/Mistral-7B-Instruct-v0.3": "Your name is Lake 1 Base but mine is User",
+    "BICORP/Lake-1-Advanced": "Your name is Lake 1 Advanced [Alpha] but mine is User or what i will type as my name"
+}
+
+# Model names and their pseudonyms
+model_choices = [
+    ("mistralai/Mistral-7B-Instruct-v0.3", "Lake 1 Base"),
+    ("BICORP/Lake-1-Advanced", "Lake 1 Advanced [Alpha]")
+]
+
+# Convert pseudonyms to model names for the dropdown
+pseudonyms = [model[1] for model in model_choices]
+
+def respond(
+    message,
+    history: list,
+    model_name,
+    preset_name
+):
+    # Get the correct client
+    client = clients[model_name]
+    
+    # Get the system message for the model
+    system_message = system_messages[model_name]
+    
+    messages = [{"role": "system", "content": system_message}]
+
+    # Ensure history is a list of dictionaries
+    for val in history:
+        if isinstance(val, dict) and 'role' in val and 'content' in val:
+            messages.append({"role": val['role'], "content": val['content']})
+
+    messages.append({"role": "user", "content": message})
+
+    # Get the preset settings
+    preset = presets[model_name][preset_name]
+    max_tokens = preset["max_tokens"]
+    temperature = preset["temperature"]
+    top_p = preset["top_p"]
+
+    # Get the response from the model
+    response = client.chat_completion(
+        messages,
+        max_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+    )
+
+    # Extract the content from the response
+    final_response = response.choices[0].message['content']
+    
+    return final_response
+
+def respond_with_pseudonym(
+    message,
+    history: list,
+    selected_model,
+    selected_preset
+):
+    # Find the actual model name from the pseudonym
+    try:
+        model_name = next(model[0] for model in model_choices if model[1] == selected_model)
+    except StopIteration:
+        return f"Error: The selected model '{selected_model}' is not valid. Please select a valid model."
+
+    # Call the existing respond function
+    response = respond(message, history, model_name, selected_preset)
+    
+    return response
+
+# Gradio Chat Interface
+demo = gr.ChatInterface(
+    fn=respond_with_pseudonym,
+    additional_inputs=[
+        gr.Dropdown(choices=pseudonyms, label="Select Model", value=pseudonyms[0]),
+        gr.Dropdown(choices=list(presets[model_choices[0][0]].keys()), label="Select Preset", value="Fast")
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()