Update app.py

app.py

@@ -1,45 +1,19 @@
 import gradio as gr
-from transformers import pipeline
-import requests
+from transformers import T5Tokenizer, T5ForConditionalGeneration, pipeline
 
-# Use the text-generation task
-standard_model = pipeline("text-generation", model="google/mt5-base")
-premium_model = pipeline("text-generation", model="google/mt5-base")
+# Load the model and tokenizer
+tokenizer = T5Tokenizer.from_pretrained("google/mt5-base")
+model = T5ForConditionalGeneration.from_pretrained("google/mt5-base")
 
-# Your Boosty API key or any method of checking subscriptions
-BOOSTY_API_KEY = "YOUR_BOOSTY_API_KEY"
-BOOSTY_API_URL = "https://boosty.to/api/subscription_check"  # Replace with the actual Boosty API URL
+# Set up the Gradio interface for text generation
+def generate_text(prompt):
+    # Use the tokenizer and model to generate text
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(inputs["input_ids"], max_length=50, num_return_sequences=1)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
 
-# Function to check if a user is a premium member on Boosty
-def is_premium_user(boosty_user_id: str) -> bool:
-    response = requests.get(f"{BOOSTY_API_URL}?user_id={boosty_user_id}", headers={"Authorization": f"Bearer {BOOSTY_API_KEY}"})
-    
-    if response.status_code != 200:
-        return False  # Assuming non-200 response means not premium
-    
-    # Assuming the response contains a field like 'is_premium': True/False
-    return response.json().get("is_premium", False)
+# Create a Gradio interface
+interface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
 
-# Chat function to use the correct model based on Boosty subscription
-def chat_with_ai(user_input, boosty_user_id):
-    # Check if the user is a premium member
-    if is_premium_user(boosty_user_id):
-        model_to_use = premium_model
-    else:
-        model_to_use = standard_model
-
-    # Get AI response
-    conversation = model_to_use(user_input)
-    return conversation[0]["generated_text"]
-
-# Create the Gradio interface
-iface = gr.Interface(
-    fn=chat_with_ai, 
-    inputs=["text", gr.Textbox(label="Boosty User ID")], 
-    outputs="text", 
-    live=True, 
-    title="AI Chatbot"
-)
-
-# Launch the Gradio app as an API
-iface.launch(server_name="0.0.0.0", server_port=7860, share=True)
+# Launch the interface
+interface.launch()