Uploaded Gen App.py

app.py

@@ -0,0 +1,135 @@
+import os
+import time
+from typing import Any
+
+import requests
+import streamlit as st
+from dotenv import find_dotenv, load_dotenv
+from langchain.chains import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from transformers import pipeline
+
+from utils.custom import css_code
+
+load_dotenv(find_dotenv())
+HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
+
+
+def progress_bar(amount_of_time: int) -> Any:
+    """
+    A very simple progress bar the increases over time,
+    then disappears when it reached completion
+    :param amount_of_time: time taken
+    :return: None
+    """
+    progress_text = "Please wait, Generative models hard at work"
+    my_bar = st.progress(0, text=progress_text)
+
+    for percent_complete in range(amount_of_time):
+        time.sleep(0.04)
+        my_bar.progress(percent_complete + 1, text=progress_text)
+    time.sleep(1)
+    my_bar.empty()
+
+
+def generate_text_from_image(url: str) -> str:
+    """
+    A function that uses the blip model to generate text from an image.
+    :param url: image location
+    :return: text: generated text from the image
+    """
+    image_to_text: Any = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+    generated_text: str = image_to_text(url)[0]["generated_text"]
+
+    print(f"IMAGE INPUT: {url}")
+    print(f"GENERATED TEXT OUTPUT: {generated_text}")
+    return generated_text
+
+
+def generate_story_from_text(scenario: str) -> str:
+    """
+    A function using a prompt template and GPT to generate a short story. LangChain is also
+    used for chaining purposes
+    :param scenario: generated text from the image
+    :return: generated story from the text
+    """
+    prompt_template: str = f"""
+    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: {scenario}
+    STORY:
+    """
+
+    prompt: PromptTemplate = PromptTemplate(template=prompt_template, input_variables=["scenario"])
+
+    llm: Any = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.9)
+
+    story_llm: Any = LLMChain(llm=llm, prompt=prompt, verbose=True)
+
+    generated_story: str = story_llm.predict(scenario=scenario)
+
+    print(f"TEXT INPUT: {scenario}")
+    print(f"GENERATED STORY OUTPUT: {generated_story}")
+    return generated_story
+
+
+def generate_speech_from_text(message: str) -> Any:
+    """
+    A function using the ESPnet text to speech model from HuggingFace
+    :param message: short story generated by the GPT model
+    :return: generated audio from the short story
+    """
+    API_URL: str = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers: dict[str, str] = {"Authorization": f"Bearer {HUGGINGFACE_API_TOKEN}"}
+    payloads: dict[str, str] = {
+        "inputs": message
+    }
+
+    response: Any = requests.post(API_URL, headers=headers, json=payloads)
+    with open("generated_audio.flac", "wb") as file:
+        file.write(response.content)
+
+
+def main() -> None:
+    """
+    Main function
+    :return: None
+    """
+    st.set_page_config(page_title= "IMAGE TO STORY CONVERTER", page_icon= "🖼️")
+
+    st.markdown(css_code, unsafe_allow_html=True)
+
+    with st.sidebar:
+        st.image("img/gkj.jpg")
+        st.write("---")
+        st.write("AI App created by @ Gurpreet Kaur")
+
+    st.header("Image-to-Story Converter")
+    uploaded_file: Any = st.file_uploader("Please choose a file to upload", type="jpg")
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        bytes_data: Any = uploaded_file.getvalue()
+        with open(uploaded_file.name, "wb") as file:
+            file.write(bytes_data)
+        st.image(uploaded_file, caption="Uploaded Image",
+                 use_column_width=True)
+        progress_bar(100)
+        scenario: str = generate_text_from_image(uploaded_file.name)
+        story: str = generate_story_from_text(scenario)
+        generate_speech_from_text(story)
+
+        with st.expander("Generated Image scenario"):
+            st.write(scenario)
+        with st.expander("Generated short story"):
+            st.write(story)
+
+        st.audio("generated_audio.flac")
+
+
+if __name__ == "__main__":
+    main()