Create app.py

app.py

@@ -0,0 +1,108 @@
+# Imports
+import os
+import streamlit as st
+import requests
+from transformers import pipeline
+import openai
+
+# Suppressing all warnings
+import warnings
+warnings.filterwarnings("ignore")
+
+# Image-to-text
+def img2txt(url):
+    print("Initializing captioning model...")
+    captioning_model = pipeline("image-to-text", model="Salesforce/blip-image-captioning-base")
+    
+    print("Generating text from the image...")
+    text = captioning_model(url, max_new_tokens=20)[0]["generated_text"]
+    
+    print(text)
+    return text
+
+# Text-to-story
+def txt2story(img_text, top_k, top_p, temperature):
+
+    headers = {"Authorization": f"Bearer {os.environ['TOGETHER_API_KEY']}"}
+
+    data = {
+        "model": "togethercomputer/llama-2-70b-chat",
+        "messages": [
+            {"role": "system", "content": '''As an experienced short story writer, write story title and then create a meaningful story influenced by provided words. 
+        Ensure stories conclude positively within 100 words. Remember the story must end within 100 words''', "temperature": temperature},
+            {"role": "user", "content": f"Here is input set of words: {img_text}", "temperature": temperature} 
+        ],
+        "top_k": top_k,
+        "top_p": top_p,
+        "temperature": temperature
+    }
+    
+    response = requests.post("https://api.together.xyz/inference", headers=headers, json=data)
+    
+    story = response.json()["output"]["choices"][0]["text"] 
+    return story
+
+
+# Text-to-speech
+def txt2speech(text):
+    print("Initializing text-to-speech conversion...")
+    API_URL = "https://api-inference.huggingface.co/models/espnet/kan-bayashi_ljspeech_vits"
+    headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACEHUB_API_TOKEN']}"}
+    payloads = {'inputs': text}
+
+    response = requests.post(API_URL, headers=headers, json=payloads)
+    
+    with open('audio_story.mp3', 'wb') as file:
+        file.write(response.content)
+
+        
+# Streamlit web app main function
+def main():
+    st.set_page_config(page_title="🎨 Image-to-Audio/Text Story 🎧", page_icon="🖼️")
+    st.title("Turn the Image into Audio/Text Story")
+
+    # Allows users to upload an image file
+    uploaded_file = st.file_uploader("# 📷 Upload an image...", type=["jpg", "jpeg", "png"])
+
+    # Parameters for LLM model (in the sidebar)
+    st.sidebar.markdown("# LLM Inference Configuration Parameters")
+    top_k = st.sidebar.number_input("Top-K", min_value=1, max_value=100, value=5)
+    top_p = st.sidebar.number_input("Top-P", min_value=0.0, max_value=1.0, value=0.8)
+    temperature = st.sidebar.number_input("Temperature", min_value=0.1, max_value=2.0, value=1.5)
+
+    if uploaded_file is not None:
+        # Reads and saves uploaded image file
+        bytes_data = uploaded_file.read()
+        with open("uploaded_image.jpg", "wb") as file:
+            file.write(bytes_data)
+
+        st.image(uploaded_file, caption='🖼️ Uploaded Image', use_column_width=True)
+
+        # Initiates AI processing and story generation
+        with st.spinner("## 🤖 AI is at Work! "):
+            scenario = img2txt("uploaded_image.jpg")  # Extracts text from the image
+            story = txt2story(scenario, top_k, top_p, temperature)  # Generates a story based on the image text, LLM params
+            txt2speech(story)  # Converts the story to audio
+
+            st.markdown("---")
+            st.markdown("## 📜 Image Caption")
+            st.write(scenario)
+
+            st.markdown("---")
+            st.markdown("## 📖 Story")
+            st.write(story)
+
+            st.markdown("---")
+            st.markdown("## 🎧 Audio Story")
+            st.audio("audio_story.mp3")
+
+if __name__ == '__main__':
+    main()
+
+# Credits
+st.markdown("### Credits")
+st.caption('''
+            Made by Nithin John\n
+            Utilizes Image-to-text, Text Generation, Text-to-speech Transformer Models\n
+            Gratitude to Streamlit, 🤗 Spaces for Deployment & Hosting
+            ''')