Create app.py

app.py

@@ -0,0 +1,64 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+import spaces
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+from transformers import pipeline
+
+pipe = pipeline("text-generation", model="BioMistral/BioMistral-7B", torch_dtype=torch.bfloat16, device_map="auto")
+
+
+@spaces.GPU
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+    hf_token,
+):
+
+    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 = ""
+    
+    prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+    outputs = pipe(prompt, max_new_tokens=max_tokens, do_sample=True, temperature=temperature, top_k=50, top_p=top_p)
+    last_space_index = outputs[0]["generated_text"].rfind('[/INST]')
+# Extract the substring after the last space character
+    substring_after_last_space = outputs[0]["generated_text"][last_space_index + 7:]
+    yield substring_after_last_space
+
+
+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)",
+    
+        ),
+        gr.Textbox(label="Hugging Face Token", placeholder="Enter your Hugging Face token here"),
+    ],
+    css="footer{display:none !important}",
+)
+
+
+if __name__ == "__main__":
+    demo.launch()