Create app.py

app.py

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+import torch
+
+
+device = "cpu"
+model_id ="ALLaM-AI/ALLaM-7B-Instruct-preview"
+
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+
+tokenizer = AutoTokenizer.from_pretrained("ALLaM-AI/ALLaM-7B-Instruct-preview")
+
+messages = [
+    {"role": "user", "content": "write a long story that takes 3 min to read"}
+]
+
+
+generator = pipeline(
+    "text-generation",   
+    model=model,         
+    tokenizer=tokenizer, 
+    return_full_text=False,  
+    max_new_tokens=500,      
+    do_sample=False           
+)
+
+from kokoro import KPipeline
+from IPython.display import display, Audio
+import soundfile as sf
+
+pipeline = KPipeline(lang_code='b', model=False)
+
+import numpy as np
+
+def Generate_audio(text, voice='bm_lewis', speed=1):
+
+    pipeline = KPipeline(lang_code='b')
+
+
+    generator = pipeline(text, voice=voice, speed=speed, split_pattern=r'\n+')
+
+
+    full_audio = []
+    for _, _, audio in generator:
+        full_audio.extend(audio)
+
+
+    full_audio = np.array(full_audio)
+
+
+    return full_audio, 24000
+
+from transformers import pipeline as transformers_pipeline
+
+
+
+captionImage = transformers_pipeline("image-to-text",
+                                 model="Salesforce/blip-image-captioning-large")
+
+def Image_Caption(image):
+    caption = captionImage(image)
+    caption = caption[0]['generated_text']
+    return caption
+
+def Generate_story(textAbout):
+  storyAbout =  {"role": "user", "content": f'write a long story about {textAbout} that takes 3 min to read'},
+
+  story = generator(storyAbout)
+  story = story[0]['generated_text']
+  story = story.replace('\n', ' ').replace('arafed', ' ')
+  return story
+
+def Mustalhim(image):
+  caption = Image_Caption(image)
+  story = Generate_story(caption)
+  audio = Generate_audio(story)
+  return audio
+
+def gradio_interface(image):
+
+    audio_waveform, sampling_rate = Mustalhim(image)
+    
+
+    audio_file = "output_audio.wav"
+    sf.write(audio_file, audio_waveform, sampling_rate)
+    
+
+    return audio_file
+
+example_image = "Example.PNG"
+
+
+app = gr.Interface(
+    fn=gradio_interface,  
+    inputs=gr.Image(type="pil"),  
+    outputs=gr.Audio(type="filepath"),  
+    title="Image to Audio Story",
+    description="Upload an image, and the app will generate a story and convert it to audio.",
+    examples=[[example_image]]
+)
+
+# Launch the app
+app.launch()
+