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.env

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+
+HUGGINGPHASE_API_TOEKEN = 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
+
+OPENAI_API_KEY=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

app.py

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+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+from langchain import PromptTemplate, LLMChain, OpenAI
+import requests
+import os
+import streamlit as st
+
+load_dotenv(find_dotenv())
+HUGGINGPHASE_API_TOKEN = os.getenv("HUGGINGPHASE_API_TOKEN")
+
+# Image2Text
+
+def img2text(url):
+    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    text = img2text(url)[0]["generated_text"]
+    print(text)
+    return text
+
+# LLM
+
+def generate_story(scenario):
+    template = """
+    you are a story teller
+    you can generate a short story based on simple narrative, the story should be more than 20 words;
+    CONTEXT:{scenario},
+    STORY:
+    """
+    prompt = PromptTemplate(template=template, input_variable=["scenario"])
+    story_llm = LLMChain(llm=OpenAI(model_name="gpt-3.5-turbo", temperature=1), prompt=prompt, verbose=True)
+    story = story_llm.predict(scenario=scenario)
+    print(story)
+    return story
+
+# Text to Speech
+
+def text2speech(message):
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {HUGGINGPHASE_API_TOKEN}"}
+    payloads = {
+        "inputs": message
+    }
+    
+    response = requests.post(API_URL, headers=headers, json=payloads)
+
+    with open('audio.flac', 'wb') as file:
+        file.write(response.content)
+
+def main():
+    st.set_page_config(page_title="img 2 Audio story", page_icon='🤖')
+    st.header("Turn img into an audio story")
+    uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        with open(uploaded_file.name, "wb") as file:
+            file.write(uploaded_file.getvalue())
+        st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
+        scenario = img2text(uploaded_file.name)
+        story = generate_story(scenario)
+        text2speech(story)
+
+        with st.expander("Scenario"):
+            st.write(scenario)
+        with st.expander("Story"):
+            st.write(story)
+        
+        st.audio("audio.flac")
+
+if __name__ == '__main__':
+    main()