Update app.py

app.py

@@ -1,28 +1,138 @@
-import streamlit as st  
-import easyocr as ocr 
-import numpy as np 
-from PIL import Image 
-
-st.title("Image to Text Toy Application")
-
-image = st.file_uploader(label = "Upload your image here", type=['png','jpg','jpeg'])
-
-@st.cache_data
-def load_model(): 
-    reader = ocr.Reader(['en'],model_storage_directory='Salesforce/blip-image-captioning-large')
-    return reader 
-
-reader = load_model() 
-
-if image is not None:
-    input_image = Image.open(image) 
-    st.image(input_image)
-    with st.spinner("AI is processing your request."):
-        result = reader.readtext(np.array(input_image))
-        result_text = []
-        for text in result:
-            result_text.append(text[1])
-        st.write(result_text)
-    st.balloons()
-else:
-    st.write("Upload an Image")
+import os
+import time
+from typing import Any
+
+import requests
+import streamlit as st
+from langchain.chains import LLMChain
+from langchain.chat_models import ChatOpenAI
+from langchain.prompts import PromptTemplate
+from transformers import pipeline
+from utils import css_code
+
+HUGGINGFACE_API_TOKEN = st.secrets["HUGGINGFACE_API_TOKEN"]
+OPENAI_API_KEY = st.secrets["OPENAI_API_KEY"]
+MODEL = st.secrets["MODEL2"]
+
+
+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="nlpconnect/vit-gpt2-image-captioning")
+
+    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 story teller;
+    You can generate a long story based on a simple narrative, the story should be no more than 100 words and have more than 30 words;
+    
+    CONTEXT: {scenario}
+    STORY:
+    """
+
+    prompt: PromptTemplate = PromptTemplate(template=prompt_template, input_variables=["scenario"])
+
+    llm: Any = ChatOpenAI(model_name=MODEL, temperature=1)
+
+    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)
+        st.download_button(
+                label="Download audio (FLAC) file",
+                data=response.content,
+                file_name='generated_audio.flac',
+                mime='flac',
+            )
+
+
+def main() -> None:
+    """
+    Main function
+    :return: None
+    """
+    st.set_page_config(page_title="Image to audio story", page_icon="img/logo.png", layout="wide")
+
+    st.markdown(css_code, unsafe_allow_html=True)
+
+    with st.sidebar:
+        st.image("img/kandinsky.jpg")
+        #st.write("---")
+        st.title("Image to Story")
+
+    st.header("Generate audio story from an image")
+    uploaded_file: Any = st.file_uploader("Please choose a file to upload", type=["jpg", "png", "jpeg", "tif"])
+
+    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 scenario"):
+            st.write(scenario)
+        with st.expander("Generated story"):
+            st.write(story)
+
+        #st.audio("generated_audio.flac")
+
+
+if __name__ == "__main__":
+    main()