Upload app.py

app.py

@@ -8,28 +8,31 @@ Original file is located at
 
 ##Creating app.py
 
-"""###Importing Dependencies"""
+"""
+###Importing Dependencies
 
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+import requests
+import json
+import os
 
-"""###Loading the model and tokenizer"""
+"""###Defining the prediction function that calls the model via API"""
 
-model_name = "gpt2"
-model = AutoModelForCausalLM.from_pretrained(model_name)
-tokenizer = AutoTokenizer.from_pretrained(model_name)
+API_URL = "https://api.yourmodelprovider.com/generate"  # Replace with your API endpoint
+HEADERS = {"Authorization": f"Bearer {os.getenv('API_KEY')}"}  # Read the API key from an environment variable
 
-"""###Defining the prediction function"""
 
 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
+    payload = {"prompt": prompt, "max_length": 100}
+    response = requests.post(API_URL, headers=HEADERS, json=payload)
+    if response.status_code == 200:
+        generated_text = response.json().get("generated_text", "")
+        return generated_text
+    else:
+        return f"Error: {response.status_code} - {response.text}"
 
-"""###Creating the Gradio interface
 
-"""
+"""###Creating the Gradio interface"""
 
 api = gr.Interface(
     fn=generate_text,
@@ -37,6 +40,7 @@ api = gr.Interface(
     outputs=gr.Textbox(label="Generated Text"),
 )
 
+
 """###Launching the API"""
 
 api.launch()