Create utils.py

writing modularized code

utils.py

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+# 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