Delete app.py

import torch
import transformers
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr
import random
import re
from typing import Dict, List
import warnings
import os

warnings.filterwarnings('ignore')

# Set environment variables for better performance
os.environ['TOKENIZERS_PARALLELISM'] = 'false'

class AIStoryteller:
"""AI Storyteller optimized for Hugging Face Spaces"""

def __init__(self):
self.model = None
self.tokenizer = None
self.model_loaded = False
self.load_model()

def load_model(self):
"""Load the AI model"""
try:
print("📥 Loading DistilGPT-2 model...")
model_name = "distilgpt2"

self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32,
)

if self.tokenizer.pad_token is None:
self.tokenizer.pad_token = self.tokenizer.eos_token

self.model_loaded = True
print("✅ Model loaded successfully!")
return True

except Exception as e:
print(f"❌ Model loading failed: {e}")
return False

def generate_story(self, prompt, max_length=100):
"""Generate a story from a prompt"""
if not self.model_loaded:
return "❌ Model not loaded. Please try again."

try:
inputs = self.tokenizer.encode(prompt, return_tensors='pt', max_length=256, truncation=True)

with torch.no_grad():
outputs = self.model.generate(
inputs,
max_length=min(inputs.shape[1] + max_length, 256),
temperature=0.8,
do_sample=True,
top_p=0.9,
top_k=50,
pad_token_id=self.tokenizer.eos_token_id,
no_repeat_ngram_size=2,
repetition_penalty=1.1
)

full_story = self.tokenizer.decode(outputs[0], skip_special_tokens=True)

if full_story.startswith(prompt):
generated_part = full_story[len(prompt):].strip()
return f"{prompt} {generated_part}"
else:
return full_story

except Exception as e:
return f"❌ Error generating story: {str(e)}"

# Initialize the storyteller
print("🚀 Initializing AI Storyteller...")
storyteller = AIStoryteller()

def generate_story_interface(prompt, genre, story_length):
"""Main interface function"""
if not prompt or not prompt.strip():
return "❌ Please enter a story prompt!"

genre_starters = {
"Fantasy": "In a realm of magic and wonder,",
"Sci-Fi": "In the distant future among the stars,",
"Mystery": "On a foggy night filled with secrets,",
"Horror": "In the darkness where nightmares dwell,",
"Romance": "When two hearts found each other,",
"Adventure": "On a daring quest for glory,",
"Comedy": "In a world of laughter and mishaps,",
"Drama": "In a tale of human emotion,"
}

if genre in genre_starters:
full_prompt = f"{genre_starters[genre]} {prompt.strip()}"
else:
full_prompt = prompt.strip()

return storyteller.generate_story(full_prompt, max_length=int(story_length))

# Create Gradio interface
interface = gr.Interface(
fn=generate_story_interface,
inputs=[
gr.Textbox(
label="📝 Story Prompt",
placeholder="Enter your story idea (e.g., 'a detective finds a mysterious letter')",
lines=3
),
gr.Dropdown(
choices=["Fantasy", "Sci-Fi", "Mystery", "Horror", "Romance", "Adventure", "Comedy", "Drama"],
label="🎭 Genre",
value="Fantasy"
),
gr.Slider(
minimum=30,
maximum=120,
value=80,
label="📏 Story Length"
)
],
outputs=gr.Textbox(
label="📚 Generated Story",
lines=8
),
title="🎭 AI Storyteller",
description="""
🌟 **Create Amazing Stories with AI!** 🌟

Enter a creative prompt, choose your favorite genre, and let AI craft a unique story for you!
Perfect for writers, students, and anyone who loves creative storytelling.
""",
examples=[
["a young wizard discovers a hidden library", "Fantasy", 100],
["a detective receives a cryptic phone call", "Mystery", 80],
["robots develop feelings", "Sci-Fi", 90],
["two strangers meet in a coffee shop", "Romance", 70],
["an explorer finds a secret cave", "Adventure", 85]
],
theme=gr.themes.Soft()
)

# Launch the app
if __name__ == "__main__":
interface.launch()

app.py

@@ -1,64 +0,0 @@
-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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()