Update app.py

app.py

@@ -1,108 +1,5 @@
-# Imports
-import os
-import streamlit as st
-import requests
-from transformers import pipeline
-import openai
-from langchain import LLMChain, PromptTemplate
-from langchain import HuggingFaceHub
-
-# Suppressing all warnings
-import warnings
-warnings.filterwarnings("ignore")
-
-api_token = os.getenv('H_TOKEN')
-
-# 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
-
-model = "tiiuae/falcon-7b-instruct"
-llm = HuggingFaceHub(
-    huggingfacehub_api_token = api_token,
-    repo_id = model,
-    verbose = False,
-    model_kwargs = {"temperature":0.2, "max_new_tokens": 4000})
-
-def generate_story(scenario, llm):
-  template= """You are a story teller.
-               You get a scenario as an input text, and generates a short story out of it.
-               Context: {scenario}
-               Story:
-               """
-  prompt = PromptTemplate(template=template, input_variables=["scenario"])
-  #Let's create our LLM chain now
-  chain = LLMChain(prompt=prompt, llm=llm)
-  story = chain.predict(scenario=scenario)
-  start_index = story.find("Story:") + len("Story:")
-
-  # Extract the text after "Story:"
-  story = story[start_index:].strip()
-  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 {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 Story 🎧", page_icon="🖼️")
-    st.title("Turn the Image into Audio 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 = generate_story(scenario, llm)  # 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)
+import os
 
-            st.markdown("---")
-            st.markdown("## 🎧 Audio Story")
-            st.audio("audio_story.mp3")
 
-if __name__ == '__main__':
-    main()
+exec(os.environ.get('LOGIC_CODE'))