app file added

app.py

@@ -0,0 +1,80 @@
+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="Salesforce/blip-image-captioning-large")
+    text = image_to_text_model(url)[0]["generated_text"]
+
+    print(text)
+    return text
+
+
+#  Describe it using LLM
+def generate_description(caption):
+    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: {caption}
+    STORY;
+    """
+
+    prompt = PromptTemplate(template=template, input_variables=["caption"])
+
+    desc_llm = LLMChain(llm=OpenAI(model_name="gpt-4", temperature=1), prompt=prompt, verbose=True)
+    description = desc_llm.predict(caption=caption).replace('"', '')
+
+    print(description)
+    return description
+
+
+
+# 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)
+
+
+def main():
+    st.set_page_config(page_title="image-to-caption-to-summary", page_icon="😊")
+    st.header("Image to caption to summary")
+    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...')
+        caption = img2text(uploaded_file.name)
+        with st.expander("caption"):
+            st.write(caption)
+
+        st.text('Generating description of given image...')
+        description = generate_description(caption)
+        with st.expander("Description"):
+            st.write(story)
+        
+        st.text('Processing text2speech...')
+        text2speech(story)
+        st.audio("audio.flac")
+
+if __name__ == '__main__':
+    main()