Added Files

Added working files

ImageToStory.py

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+import os
+import requests
+import streamlit as st
+
+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+
+from langchain import PromptTemplate, LLMChain
+from langchain.llms import GooglePalm
+
+load_dotenv(find_dotenv())
+
+llm = GooglePalm(temperature=0.9, google_api_key=os.getenv("GOOGLE_API_KEY"))
+
+# Iamge to Text
+def image_to_text(url):
+    #load a transformer
+    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+
+    text = image_to_text(url)[0]['generated_text']
+
+    print (text)
+    return text
+
+# llm
+def generate_story(scenario):
+    template = """
+    you are a very good story teller and a very rude person:
+    you can generate a short fairy tail based on a single narrative, the story should take 5 seconds to read.
+
+    CONTEXT: {scenario}
+    STORY:
+    """
+
+    prompt = PromptTemplate(template=template, input_variables=["scenario"])
+    story_llm = LLMChain(llm=llm, prompt=prompt, verbose=True)
+    story = story_llm.predict(scenario=scenario)
+    print(story)
+    return story
+
+# text to speech
+
+def text_to_speech(message):
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_TOKEN')}"}
+    payload = {"inputs": message}
+
+    response = requests.post(API_URL, headers=headers, json=payload)
+    print(response.content)
+    with open('audio.mp3', 'wb') as audio_file:
+        audio_file.write(response.content)
+
+def main():
+    st.set_page_config(page_title="Image to Story", page_icon="📚", layout="wide")
+
+    st.title("Image to Story")
+    uploaded_file = st.file_uploader("Choose an image...", type="png")
+
+    if uploaded_file is not None:
+        bytes_data = 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)
+        scenario = image_to_text(uploaded_file.name)
+        story = generate_story(scenario)
+        text_to_speech(story)
+
+        with st.expander("scenerio"):
+            st.write(scenario)
+        with st.expander("story"):
+            st.write(story)
+
+        st.audio("audio.mp3")
+
+if __name__ == '__main__':
+    main()

requirements.txt

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+google-generativeai
+langchain
+python-dotenv
+tensorflow
+transformers