Delete app.py

app.py

@@ -1,118 +0,0 @@
-import os
-from dotenv import load_dotenv
-from transformers import pipeline
-from langchain import OpenAI, PromptTemplate, LLMChain
-import requests
-import streamlit as st
-
-load_dotenv()
-HUGGINGFACE_API_TOKEN = os.getenv('HUGGINGFACE_API_TOKEN')
-OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
-
-#img to text
-
-def image_to_text(image_url):
-    """
-    Generates context text from image using image-to-text transformer model.
-
-    Parameters:
-        image_url (str): URL of the image to be processed.
-
-    Returns:
-        str: Context text.
-    """
-    pipe = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
-    text = pipe(image_url)[0]['generated_text']
-    print(text)
-    return text
-
-
-#llm
-def text_to_story(text, model='gpt-3.5-turbo-instruct-0914', temperature=0.9):
-    """
-    Generates a short story based on input text by prompting OpenAI's language model.
-
-    Args:
-        text (str): Input text used as context for generating the story.
-        model (str): OpenAI model to be used for story generation. Defaults to 'gpt-3.5-turbo-instruct-0914'.
-        temperature (float): Controls the randomness of the generated story. Defaults to 0.9.
-
-    Returns:
-        str: Generated short story based on the input context.
-    """
-    llm = OpenAI(openai_api_key=os.getenv("OPEN_API_KEY"), model=model, temperature=temperature)
-    prompt = PromptTemplate(
-        input_variables = ['text'],
-        template = '''
-        You are a talented story teller who can create a story from a simple narrative./
-        Create a story using the following scenario; the story should have be maximum 50 words long.
-        context = {text}
-        '''
-        )
-    chain = LLMChain(llm=llm, prompt=prompt)
-    story = chain.predict(text=text)
-
-    print(story)
-    return story
-
-
-
-# text to speech
-def story_to_speech(story):
-    """
-    Generates and saves an audio file of speech narrating the short story using a text-to-speech tranformer model.
-
-    Args:
-        story (str): Short story to be converted to speech.
-    """
-    API_URL = 'https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits'
-    headers = {"Authorization": f'Bearer {HUGGINGFACE_API_TOKEN}'}
-    payload = {"inputs": story}
-    response = requests.post(API_URL, headers=headers, json=payload)
-    with open('story_speech.flac', 'wb') as file:
-        file.write(response.content)
-
-
-# user interface
-
-def main():
-    """
-    Main function to run the Streamlit user interface for the Image-to-Story App.
-    """
-    st.set_page_config(page_title= "IMAGE TO STORY CONVERTER", page_icon= "🖼️")
-    st.header("Image-to-Story Converter")
-    #file uploader
-    file_upload = st.file_uploader("Please choose a file to upload", type="jpg")
-    #save file
-    if file_upload is not None:
-        try:
-            image_bytes = file_upload.getvalue()
-            with open(f'{file_upload.name}', "wb") as file:
-                file.write(image_bytes)
-            #display image
-            st.image(file_upload, caption = "Uploaded image")
-            #run functions
-            file_name = file_upload.name
-            text = image_to_text(f'{file_name}')
-            if text:
-                story = text_to_story(text)
-                with st.expander('Generated image scenario'):
-                        st.write(text)
-                if story:
-                    story_to_speech(story)
-                    with st.expander('Generated short story'):
-                        st.write(story)
-                    st.audio('story_speech.flac')
-                else:
-                    st.error("Failed to generate a story from the text.")
-            else:
-                st.error("Failed to generate a text from the image.")
-
-        except Exception as e:
-            st.error(f"An error occurred: {e}")
-    else:
-        st.warning("Please upload an image to generate a story.")
-
-
-if __name__ == '__main__':
-    main()