basic img2story

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -1,23 +1,83 @@
 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())
+HF_API_KEY=os.getenv("HF_API_KEY")
 
 # img2text
 def img2text(url):
-    image_to_text_model = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
-    text = image_to_text_model(url)
+    image_to_text_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-large")
+    text = image_to_text_model(url)[0]["generated_text"]
 
     print(text)
     return text
 
-img2text("test.jpg")
+
 # make the story of it using LLM
+def generate_story(scenario):
+    template = """
+    You are a story teller;
+    You can generate a short story based on a simple narrative, the story should be no more than 30 words;
+
+    CONTEXT: {scenario}
+    STORY;
+    """
+
+    prompt = PromptTemplate(template=template, input_variables=["scenario"])
+
+    story_llm = LLMChain(llm=OpenAI(model_name="gpt-4", temperature=1), prompt=prompt, verbose=True)
+    story = story_llm.predict(scenario=scenario).replace('"', '')
+
+    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 {HF_API_KEY}"}
+    payload = {
+        "inputs": message
+    }
+    
+    response = requests.post(API_URL, headers=headers, json=payload)
+    with open('audio.flac', 'wb') as file:
+        file.write(response.content)
+
+# generate_story(img2text("test1.jpeg"))
+# text2speech("Access tokens programmatically authenticate your identity to the Hugging Face Hub")
+
+def main():
+    st.set_page_config(page_title="image-to-audio-story", page_icon="😊")
+    st.header("Image to audio story")
+    uploaded_file = st.file_uploader("Choose an image", type=['png', 'jpg'])
+
+    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)
 
+        st.text('Processing img2text...')
+        scenario = img2text(uploaded_file.name)
+        with st.expander("scenario"):
+            st.write(scenario)
 
+        st.text('Generating story on given scenario...')
+        story = generate_story(scenario)
+        with st.expander("story"):
+            st.write(story)
+        
+        st.text('Processing text2speech...')
+        text2speech(story)
+        st.audio("audio.flac")
 
+if __name__ == '__main__':
+    main()

requirements.txt

@@ -1,2 +1,6 @@
 dotenv
-transformers
+transformers
+langchain
+openai
+requests
+streamlit