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main.py

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+from pydub import AudioSegment
+from dotenv import find_dotenv, load_dotenv
+from transformers import pipeline
+from elevenlabs import generate, play, voices 
+from elevenlabs import set_api_key
+from elevenlabslib import ElevenLabsUser
+import streamlit as st
+import uuid
+
+from langchain import LLMChain, PromptTemplate, OpenAI
+import os
+
+# Load environment variables
+load_dotenv(find_dotenv())
+
+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
+
+def generated_story(scenario):
+    template = """
+    You are the famous comedian and podcaster Tim Dillon. You will generate a short story in the style of his podcast using a funny narrative with explicit language and an angry rant-like tone, 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
+
+
+def textToSpeech(story):
+    # Error Handling: Print available user-generated voices
+    try:
+        available_voices = user.get_available_voices()
+        print("Available Voices:", available_voices)
+    except Exception as e:
+        print("Error fetching available voices:", e)
+
+    # Set API key for ElevenLabs
+    set_api_key = os.getenv("ELEVENLABS_API_KEY")
+    user = ElevenLabsUser(set_api_key)
+    voice = user.get_voice_by_ID("cgOzEASJmlEWHtXnZJ5q")
+
+    # Generate the audio data
+    result = voice.generate_audio_v2(story)
+
+    # Assuming the audio data is the first element of the tuple
+    audio_data = result[0]
+
+    # Save the audio data to a file in the project folder
+    random_id = str(uuid.uuid4())
+    name = f"story_{random_id}.mp3"
+
+    #Save the audio data to a file in the project folder
+    with open(name, 'wb') as f:
+        f.write(audio_data)
+    return name
+
+def main():
+    st.set_page_config(page_title="Tim Dillon Image To Story", page_icon="📖", layout="wide")
+    st.header("Tim Dillon Image To Story")
+    uploaded_file = st.file_uploader("Upload an image...", type="jpg")
+    if uploaded_file is not None:
+        print(uploaded_file)
+        bytes_data = uploaded_file.getvalue()
+        with open (uploaded_file.name, 'wb') as f:
+            f.write(bytes_data)
+        st.image(bytes_data, caption='Uploaded Image.', use_column_width=True)
+        scenario = img2text(uploaded_file.name)
+        story = generated_story(scenario)
+        generated_file_name = textToSpeech(story)
+
+        with st.expander("scenario"):
+            st.write(scenario)
+        with st.expander("story"):
+            st.write(story)
+        
+        st.audio(generated_file_name)
+        
+
+if __name__ == "__main__":
+    main()

packages.txt

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+build-essential
+libasound-dev
+portaudio19-dev
+python3-pyaudio
+libportaudio2
+ffmpeg

requirements.txt

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+config==0.5.1
+elevenlabs==0.2.24
+elevenlabslib==0.11.1
+langchain==0.0.274
+pydub==0.25.1
+python-dotenv==1.0.0
+Requests==2.31.0
+streamlit==1.24.0
+transformers==4.32.0
+torch>=1.8