add application

app.py

@@ -0,0 +1,86 @@
+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+from langchain import PromptTemplate, LLMChain, OpenAI
+from langchain.chat_models import ChatOpenAI
+import requests
+import os
+import streamlit as st
+
+
+load_dotenv(find_dotenv())
+HUGGINGFACEHUB_API_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
+
+# img2text
+def img2text(url):
+    image_to_text = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
+
+    text = image_to_text(url)[0]["generated_text"]
+
+    print(text)
+    return text
+
+
+# llm storyteller
+def generate_story(scenario):
+    template = """
+    You are a storyteller;
+    You can generate a short story based on a simple narrative, the story should be no more than 100 words;
+    
+    CONTEXT: {scenario}
+    STORY:
+    """
+
+    prompt = PromptTemplate(template=template, input_variables=["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
+
+                         
+
+# text2speech
+def text2speech(message):
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_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)
+
+
+#UI Streamlit
+def main():
+
+    st.set_page_config(page_title="imageteller", page_icon="🤖")
+    
+    st.header("ImageTeller: Turn any image into a story")
+    uploaded_file = st.file_uploader("choose an image", type="png")
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        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 = 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()