Create app.py

app.py

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+from dotenv import load_dotenv, find_dotenv
+import requests
+import os
+import streamlit as st
+import torch
+from parler_tts import ParlerTTSForConditionalGeneration
+from transformers import AutoTokenizer
+import soundfile as sf
+
+# print(torch.cuda.is_available())
+
+load_dotenv(find_dotenv())
+HUGGINGFACEHUB_API_TOKEN = os.getenv("token")
+headers = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"}
+
+def img2text(path):
+
+    API_URL = "https://api-inference.huggingface.co/models/Salesforce/blip-image-captioning-large"
+
+    def query(filename):
+        with open(filename, "rb") as f:
+            data = f.read()
+        response = requests.post(API_URL, headers=headers, data=data)
+        return response.json()
+
+    output = query(path)[0]['generated_text']
+
+    print(output)
+    return output
+
+
+
+def generate_story(scene):
+    template = f'''
+                You are a poet;
+                You can generate a poem from a simple narrative, understand the theme, and use proper rhyming words.
+                The poem should not be shorter than 16 lines and not be longer than 20 lines.
+
+                Scenario: {scene}
+
+                Write a poem based on the provided scenario.
+                '''
+
+    API_URL = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+
+    def query(payload):
+        response = requests.post(API_URL, headers=headers, json=payload)
+        return response.json()
+
+    story = query({
+        "inputs": template,
+    })
+
+    story = str(story[0]['generated_text']).split("\n")
+
+    story = story[12:]
+    s = ""
+    for i in story:
+        s += (i+"\n")
+
+    story = s
+    del(s)
+
+    print(story)
+    return story
+
+def gen_audio(message):
+
+    device = "cuda:0" if torch.cuda.is_available() else "cpu"
+
+    model = ParlerTTSForConditionalGeneration.from_pretrained("parler-tts/parler_tts_mini_v0.1").to(device)
+    tokenizer = AutoTokenizer.from_pretrained("parler-tts/parler_tts_mini_v0.1")
+
+    prompt = message
+    description = "A female speaker with a slightly low-pitched, quite expressive voice delivers her words at a normal  pace in a poetic manner with proper pauses while speaking inside a confined space with very clear audio."
+
+    input_ids = tokenizer(description, return_tensors="pt").input_ids.to(device)
+    prompt_input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)
+
+    generation = model.generate(input_ids=input_ids, prompt_input_ids=prompt_input_ids)
+    audio_arr = generation.cpu().numpy().squeeze()
+    sf.write("audio.wav", audio_arr, model.config.sampling_rate)
+
+
+def main():
+    st.set_page_config(page_title="img 2 poem", page_icon="🤖")
+    st.header("Trun image into poem")
+    uploaded_file = st.file_uploader("choose an image.....", type=["png","jpg","jpeg","svg"])
+
+    if uploaded_file is not None:
+        print(uploaded_file)
+        bytes_data = uploaded_file.getvalue()
+        print(bytes_data)
+        with open(uploaded_file.name, "wb") as file:
+            file.write(bytes_data)
+
+        st.image(uploaded_file, caption="Uploaded Image")
+
+        scenario = img2text(uploaded_file.name)
+        story = generate_story(scenario)
+        gen_audio(story)
+
+        with st.expander("Scenario"):
+            st.write(scenario)
+        with st.expander("Poem"):
+            st.write(story)
+
+        st.audio("audio.wav")
+
+if __name__ == "__main__":
+    # main()
+    scene = img2text("couples.jpg")
+    story = generate_story(scene)
+    # gen_audio(story)
+
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