Initial commit

app.py

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+import os
+from dotenv import load_dotenv, find_dotenv
+from huggingface_hub import InferenceClient
+import gradio as gr
+
+# Load Hugging Face token from .env
+load_dotenv(find_dotenv())
+hf_token = os.getenv("HF_TOKEN")
+
+# Define available models
+models = {
+    "Llama-3.3-70B-Instruct": "meta-llama/llama-3.3-70B-instruct",
+    "c4ai-command-r7b-12-2024": "CohereForAI/c4ai-command-r7b-12-2024",
+    "Qwen2.5-Coder-32B-Instruct": "qwen/qwen2.5-coder-32B-instruct",
+    "Mistral-Nemo-Instruct-2407": "mistralai/Mistral-Nemo-Instruct-2407",
+    "Gemma-2-9b-it": "google/gemma-2-9b-it",
+    "Phi-3-mini-4k-instruct": "microsoft/phi-3-mini-4k-instruct",
+}
+
+# Initialize the InferenceClient with a selected model
+def get_inference_client(selected_model):
+    return InferenceClient(
+        models[selected_model],
+        token=hf_token,
+    )
+
+# Function to get a response from the chatbot
+def get_response(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p):
+    client = get_inference_client(selected_model)
+    
+    # Add system message
+    messages = [{"role": "system", "content": system_prompt}]
+    
+    # Include previous conversation history
+    for h in history:
+        messages.append({"role": h['role'], "content": h['content']})
+    
+    # Add the current user input to the messages
+    messages.append({"role": "user", "content": user_input})
+    
+    # Get response from the model
+    response = client.chat_completion(
+        messages, 
+        max_tokens=max_tokens, 
+        temperature=temperature, 
+        top_p=top_p,
+    )
+    
+    bot_response = response.choices[0].message.content
+    history.append({"role": "user", "content": user_input})
+    history.append({"role": "assistant", "content": bot_response})
+    
+    return history
+
+# Gradio interface
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Set the type to 'messages' to avoid the deprecation warning
+            chatbot = gr.Chatbot(type="messages")
+            with gr.Row():
+                user_input = gr.Textbox(show_label=False, placeholder="Enter your message...")
+                send_button = gr.Button("Send")
+        with gr.Column(scale=1):
+            with gr.Accordion("Settings", open=False):
+                # Model selection
+                selected_model = gr.Dropdown(choices=list(models.keys()), label="Select Model", value="Llama-3.3-70B-Instruct")
+                
+                # Chat settings
+                system_prompt = gr.Textbox(value="You are a friendly and open-minded chatbot.", label="System Prompt", lines=5)
+                temperature = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.7, label="Temperature")
+                max_tokens = gr.Slider(minimum=10, maximum=1000, step=10, value=250, label="Max Tokens")
+                top_p = gr.Slider(minimum=0.0, maximum=1.0, step=0.1, value=0.9, label="Top-p")
+    
+    # Chatbot interaction
+    def submit_message(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p):
+        # Get updated history including user input and bot response
+        history = get_response(user_input, history, selected_model, system_prompt, temperature, max_tokens, top_p)
+        return "", history
+    
+    # Set up the send button click functionality
+    send_button.click(
+        submit_message, 
+        [user_input, chatbot, selected_model, system_prompt, temperature, max_tokens, top_p], 
+        [user_input, chatbot]
+    )
+    
+    # Trigger sending message when Enter key is pressed
+    user_input.submit(
+        submit_message, 
+        [user_input, chatbot, selected_model, system_prompt, temperature, max_tokens, top_p], 
+        [user_input, chatbot]
+    )
+
+# Launch the Gradio interface
+demo.launch()