Update app.py

app.py

@@ -1,79 +1,28 @@
+from transformers import AutoModel, AutoTokenizer
+import torch
 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
-"""
-model = InferenceClient("abdfajar707/rkp_lora_model")
-
-def get_completion(query: str, model, tokenizer) -> str:
-  device = "cuda:0"
-  prompt_template = """
-  <start_of_turn>user
-  Below is an instruction that describes a task. Write a response that appropriately completes the request.
-  {query}
-  <end_of_turn>\\n<start_of_turn>model
-  
-  """
-  prompt = prompt_template.format(query=query)
-  encodeds = tokenizer(prompt, return_tensors="pt", add_special_tokens=True)
-  model_inputs = encodeds.to(device)
-  generated_ids = model.generate(**model_inputs, max_new_tokens=1000, do_sample=True, pad_token_id=tokenizer.eos_token_id)
-  # decoded = tokenizer.batch_decode(generated_ids)
-  decoded = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
-  return (decoded)
-
-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 model.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(
-    get_completion,
-    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)",
-        ),
-    ],
+# Load the model and tokenizer
+model_name = "abdfajar707/rkp_llama3_lora_model"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModel.from_pretrained(model_name)
+
+# Define the function for text generation
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.last_hidden_state  # Adjust depending on your model's output
+    predicted_indices = torch.argmax(logits, dim=-1)
+    predicted_text = tokenizer.decode(predicted_indices[0], skip_special_tokens=True)
+    return predicted_text
+
+# Create the Gradio interface
+iface = gr.Interface(
+    fn=generate_text,
+    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter your prompt here..."),
+    outputs="text"
 )
 
-
-if __name__ == "__main__":
-    demo.launch()
+# Launch the Gradio interface
+iface.launch()