Update app.py

app.py

@@ -1,63 +1,41 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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 = ""
-
-    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
-
-        response += token
-        yield response
-
-"""
-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)",
-        ),
-    ],
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/CodeQwen1.5-7B-Chat",
+    torch_dtype="auto",
+    device_map="auto",
+    token=hf_token
 )
+tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
 
+messages = [
+    {"role": "system", "content": "You are a helpful assistant."},
+]
+import gradio as gr
 
-if __name__ == "__main__":
-    demo.launch()
+def greet(prompt):
+    messages.append({"role": "user", "content": prompt})
+    text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+    )
+    model_inputs = tokenizer([text], return_tensors="pt")
+
+    generated_ids = model.generate(
+        model_inputs.input_ids,
+        max_new_tokens=512
+    )
+    generated_ids = [
+        output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+    ]
+
+    response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0].text
+    
+    messages.append({"role": "bot", "content": response})
+
+    return response
+
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+demo.launch(share=True)