Update app.py

app.py

@@ -4,74 +4,8 @@ import requests
 from transformers import pipeline
 from typing import Dict
 from together import Together
+from utils import img2txt, txt2story, txt2speech, get_user_preferences
 
-# Image-to-text
-def img2txt(url: str) -> str:
-    print("Initializing captioning model...")
-    captioning_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    
-    print("Generating text from the image...")
-    text = captioning_model(url, max_new_tokens=20)[0]["generated_text"]
-    
-    print(text)
-    return text
-
-# Text-to-story generation with LLM model
-def txt2story(prompt: str, top_k: int, top_p: float, temperature: float) -> str:
-    # Load the Together API client
-    client = Together(api_key=os.environ.get("TOGETHER_API_KEY"))
-
-    # Modify the prompt based on user inputs and ensure a 250-word limit
-    story_prompt = f"Write a short story of no more than 250 words based on the following prompt: {prompt}"
-
-    # Call the LLM model
-    stream = client.chat.completions.create(
-        model="meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo",
-        messages=[
-            {"role": "system", "content": '''As an experienced short story writer, write a meaningful story influenced by the provided prompt.
-            Ensure the story does not exceed 250 words.'''},
-            {"role": "user", "content": story_prompt}
-        ],
-        top_k=top_k,
-        top_p=top_p,
-        temperature=temperature,
-        stream=True
-    )
-
-    # Concatenate story chunks
-    story = ''
-    for chunk in stream:
-        story += chunk.choices[0].delta.content
-
-    return story
-
-# Text-to-speech
-def txt2speech(text: str) -> None:
-    print("Initializing text-to-speech conversion...")
-    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
-    headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
-    payloads = {'inputs': text}
-    
-    response = requests.post(API_URL, headers=headers, json=payloads)
-    
-    with open('audio_story.mp3', 'wb') as file:
-        file.write(response.content)
-
-# Get user preferences for the story
-def get_user_preferences() -> Dict[str, str]:
-    preferences = {}
-
-    preferences['continent'] = st.selectbox("Continent", ["North America", "Europe", "Asia", "Africa", "Australia"])
-    preferences['genre'] = st.selectbox("Genre", ["Science Fiction", "Fantasy", "Mystery", "Romance"])
-    preferences['setting'] = st.selectbox("Setting", ["Future", "Medieval times", "Modern day", "Alternate reality"])
-    preferences['plot'] = st.selectbox("Plot", ["Hero's journey", "Solving a mystery", "Love story", "Survival"])
-    preferences['tone'] = st.selectbox("Tone", ["Serious", "Light-hearted", "Humorous", "Dark"])
-    preferences['theme'] = st.selectbox("Theme", ["Self-discovery", "Redemption", "Love", "Justice"])
-    preferences['conflict'] = st.selectbox("Conflict Type", ["Person vs. Society", "Internal struggle", "Person vs. Nature", "Person vs. Person"])
-    preferences['twist'] = st.selectbox("Mystery/Twist", ["Plot twist", "Hidden identity", "Unexpected ally/enemy", "Time paradox"])
-    preferences['ending'] = st.selectbox("Ending", ["Happy", "Bittersweet", "Open-ended", "Tragic"])
-    
-    return preferences
 
 # Main function
 def main():