Update app.py

app.py

@@ -1,64 +1,189 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
+import concurrent.futures
 
-"""
-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")
+# Available LLM models
+LLM_MODELS = {
+    "Llama-3.3": "meta-llama/Llama-3.3-70B-Instruct",
+    "QwQ-32B": "Qwen/QwQ-32B-Preview",
+    "C4AI-Command": "CohereForAI/c4ai-command-r-plus-08-2024",
+    "Marco-o1": "AIDC-AI/Marco-o1",
+    "Qwen2.5": "Qwen/Qwen2.5-72B-Instruct",
+    "Mistral-Nemo": "mistralai/Mistral-Nemo-Instruct-2407",
+    "Nemotron-70B": "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
+}
 
+# Default selected models
+DEFAULT_MODELS = [
+    "meta-llama/Llama-3.3-70B-Instruct",
+    "CohereForAI/c4ai-command-r-plus-08-2024",
+    "mistralai/Mistral-Nemo-Instruct-2407"
+]
 
-def respond(
+clients = {model: InferenceClient(model) for model in LLM_MODELS.values()}
+
+def process_file(file):
+    if file is None:
+        return ""
+    if file.name.endswith(('.txt', '.md')):
+        return file.read().decode('utf-8')
+    return f"Uploaded file: {file.name}"
+
+def respond_single(
+    client,
     message,
-    history: list[tuple[str, str]],
+    history,
     system_message,
     max_tokens,
     temperature,
     top_p,
 ):
     messages = [{"role": "system", "content": system_message}]
+    
+    for user, assistant in history:
+        if user:
+            messages.append({"role": "user", "content": user})
+        if assistant:
+            messages.append({"role": "assistant", "content": assistant})
+    
+    messages.append({"role": "user", "content": message})
+    
+    response = ""
+    try:
+        for msg in client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            stream=True,
+            temperature=temperature,
+            top_p=top_p,
+        ):
+            token = msg.choices[0].delta.content
+            response += token
+            yield response
+    except Exception as e:
+        yield f"Error: {str(e)}"
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+def respond_all(
+    message,
+    file,
+    history1,
+    history2,
+    history3,
+    selected_models,
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    if file:
+        file_content = process_file(file)
+        message = f"{message}\n\nFile content:\n{file_content}"
 
-    messages.append({"role": "user", "content": message})
+    while len(selected_models) < 3:
+        selected_models.append(selected_models[-1])
 
-    response = ""
+    def generate(client, history):
+        return respond_single(
+            client,
+            message,
+            history,
+            system_message,
+            max_tokens,
+            temperature,
+            top_p,
+        )
 
-    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
+    return (
+        generate(clients[selected_models[0]], history1),
+        generate(clients[selected_models[1]], history2),
+        generate(clients[selected_models[2]], history3),
+    )
 
-        response += token
-        yield response
+with gr.Blocks() as demo:
+    with gr.Row():
+        model_choices = gr.Checkboxgroup(
+            choices=list(LLM_MODELS.values()),
+            value=DEFAULT_MODELS,
+            label="Select Models (Choose up to 3)",
+            interactive=True
+        )
 
+    with gr.Row():
+        with gr.Column():
+            chat1 = gr.ChatInterface(
+                lambda message, history: None,
+                chatbot=gr.Chatbot(height=400, label="Chat 1"),
+                textbox=False,
+            )
+        with gr.Column():
+            chat2 = gr.ChatInterface(
+                lambda message, history: None,
+                chatbot=gr.Chatbot(height=400, label="Chat 2"),
+                textbox=False,
+            )
+        with gr.Column():
+            chat3 = gr.ChatInterface(
+                lambda message, history: None,
+                chatbot=gr.Chatbot(height=400, label="Chat 3"),
+                textbox=False,
+            )
 
-"""
-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)",
-        ),
-    ],
-)
+    with gr.Row():
+        with gr.Column():
+            system_message = gr.Textbox(
+                value="You are a friendly Chatbot.",
+                label="System message"
+            )
+            max_tokens = gr.Slider(
+                minimum=1,
+                maximum=2048,
+                value=512,
+                step=1,
+                label="Max new tokens"
+            )
+            temperature = gr.Slider(
+                minimum=0.1,
+                maximum=4.0,
+                value=0.7,
+                step=0.1,
+                label="Temperature"
+            )
+            top_p = gr.Slider(
+                minimum=0.1,
+                maximum=1.0,
+                value=0.95,
+                step=0.05,
+                label="Top-p"
+            )
+            
+    with gr.Row():
+        file_input = gr.File(label="Upload File (optional)")
+        msg_input = gr.Textbox(
+            show_label=False,
+            placeholder="Enter text and press enter",
+            container=False
+        )
+        
+    def submit_message(message, file):
+        return respond_all(
+            message,
+            file,
+            chat1.chatbot.value,
+            chat2.chatbot.value,
+            chat3.chatbot.value,
+            model_choices.value,
+            system_message.value,
+            max_tokens.value,
+            temperature.value,
+            top_p.value,
+        )
 
+    msg_input.submit(
+        submit_message,
+        [msg_input, file_input],
+        [chat1.chatbot, chat2.chatbot, chat3.chatbot],
+        api_name="submit"
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()