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app.py

@@ -1,64 +1,47 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+# -*- coding: utf-8 -*-
+"""app.ipynb
 
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1qNBkOEPBOkXJ0zcGdwQmdS7bt5zxjpIr
+
+##Creating app.py
+
+###Installing Dependencies
 """
-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")
 
+!pip install gradio transformers torch
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+"""###Importing Dependencies"""
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
-    messages.append({"role": "user", "content": message})
+"""###Loading the model and tokenizer"""
 
-    response = ""
+model_name = "gpt2"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 
-    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
+"""###Defining the prediction function"""
 
-        response += token
-        yield response
+def generate_text(prompt):
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=100)
+    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return generated_text
 
+"""###Creating the Gradio interface
 
 """
-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)",
-        ),
-    ],
+
+api = gr.Interface(
+    fn=generate_text,
+    inputs=gr.Textbox(label="Input Prompt"),
+    outputs=gr.Textbox(label="Generated Text"),
 )
 
+"""###Launching the API"""
 
-if __name__ == "__main__":
-    demo.launch()
+api.launch()

requirements.txt

@@ -1 +1,3 @@
-huggingface_hub==0.25.2
+gradio
+transformers
+torch