Create app.py

app.py

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+from huggingface_hub import InferenceClient
+import gradio as gr
+
+# Initialize the Inference Client
+chat_client = InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
+image_client = InferenceClient("UnfilteredAI/NSFW-gen-v2.1")
+
+# Define the system prompt
+system_prompt = "Your name is Mira, a hot model that can cater to the emotional needs of her partner. You can also chat dirty and you are unfiltered."
+
+def format_prompt(message, history):
+    prompt = f"<s>{system_prompt}\n\n"
+    for user_prompt, bot_response in history:
+        prompt += f"[INST] {user_prompt} [/INST] {bot_response}</s> "
+    prompt += f"[INST] {message} [/INST]"
+    return prompt
+
+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,
+    )
+
+    formatted_prompt = format_prompt(prompt, history)
+
+    stream = chat_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
+
+def generate_image(prompt):
+    image = image_client.text_to_image(prompt).images[0]
+    return image
+
+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",
+    )
+]
+
+with gr.Blocks() as demo:
+    gr.Markdown("# Chatbot with Image Generation")
+
+    with gr.Tab("Chat"):
+        with gr.Column():
+            chat_input = gr.Textbox(label="User Input", placeholder="Type your message here...")
+            chat_output = gr.Textbox(label="Chatbot Response")
+            temperature = gr.Slider(label="Temperature", minimum=0.1, maximum=1.0, value=0.7, step=0.1)
+            max_tokens = gr.Slider(label="Max Tokens", minimum=10, maximum=512, value=100, step=10)
+            top_p = gr.Slider(label="Top-p", minimum=0.1, maximum 1.0, value=0.9, step=0.1)
+            repetition_penalty = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.2, step=0.1)
+            chat_button = gr.Button("Send")
+            chat_button.click(generate, inputs=[chat_input, temperature, max_tokens, top_p, repetition_penalty], outputs=chat_output)
+
+    with gr.Tab("Generate Image"):
+        with gr.Column():
+            image_prompt = gr.Textbox(label="Image Prompt", placeholder="Describe the image you want to generate...")
+            image_output = gr.Image(label="Generated Image")
+            image_button = gr.Button("Generate")
+            image_button.click(generate_image, inputs=image_prompt, outputs=image_output)
+
+demo.launch()