app v1

app.py

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+from diffusers import DiffusionPipeline
+from torchvision.transforms.functional import to_tensor
+import torch
+import matplotlib.pyplot as plt
+from torchvision.transforms.functional import to_pil_image
+import gradio as gr
+from torchvision.utils import save_image
+from PIL import Image
+import os
+from gtts import gTTS
+import torch
+import gradio as gr
+from torchvision.transforms import Compose, Resize, CenterCrop, ToTensor, Normalize
+from transformers import pipeline, GPT2LMHeadModel, GPT2Tokenizer
+
+example_1 = "ninja turtles fighting against a mosquito, in the sea"
+example_2 = "warrior fighting zombies with a sword, in the forest"
+example_3 = "western cowboy fighting against a dragon, in the desert"
+
+def load_image(image_path):
+    images = []
+    image = Image.open(image_path)
+    image_tensor = to_tensor(image)
+    image_tensor = image_tensor / image_tensor.max()
+    images.append(image_tensor)
+    return to_pil_image(images[0])
+
+def text_to_comics(text):
+    if text == example_1:
+        return load_image("e1_comics_0.png")
+    if text == example_2:
+        return load_image("e2_comics_0.png")
+    if text == example_3:
+        return load_image("e3_comics_0.png")
+
+    pipeline = DiffusionPipeline.from_pretrained("ogkalu/Comic-Diffusion")
+    output = pipeline(text, prompt_len=70, num_images=1, return_tensors=True)
+
+    images = []
+    for i in range(1):
+        image = output.images[i]
+        image_tensor = to_tensor(image)
+        image_tensor = image_tensor / image_tensor.max()
+        save_image(image_tensor, f"comics_{i}.png")
+        images.append(to_pil_image(image_tensor))
+    return images[0]
+
+#text_to_comics(example_1)
+#text_to_comics(example_2)
+#text_to_comics(example_3)
+
+def generate_story(description):
+    model = GPT2LMHeadModel.from_pretrained("gpt2")
+    tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+    inputs = tokenizer.encode(description + " a thriller/action story.", return_tensors='pt')
+    outputs = model.generate(input_ids=inputs, 
+                             max_length=200, 
+                             num_return_sequences=1, 
+                             temperature=0.7, 
+                             no_repeat_ngram_size=2)
+    story = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return story
+
+def convert_to_audio(text):
+    tts = gTTS(text)
+    audio_file_path = "audio.mp3"
+    tts.save(audio_file_path)
+    return audio_file_path
+
+def audio_to_text(audio_file_path):
+    pipe = pipeline("automatic-speech-recognition", "openai/whisper-large-v2")
+    result = pipe("audio.mp3")
+    print(result)
+    return result['text']
+
+def sentiment_analysis(text):
+    sentiment_analyzer = pipeline("sentiment-analysis")
+    result = sentiment_analyzer(text)
+    print(result)
+    return result
+
+def app(text):
+    comics = text_to_comics(text)
+    story = generate_story(text)
+    audio_file = convert_to_audio(story)
+    transcribed_text = audio_to_text(audio_file)
+    sentiment = sentiment_analysis(transcribed_text)
+    return comics, audio_file,transcribed_text, sentiment
+
+ui = gr.Interface(fn=app, 
+                  inputs="text", 
+                  outputs=["image", "audio", "text", "text"],
+                  title="GenAI Multi-model LLM comics: Type some text get comics!",
+                  description="This model generates comics based on the text(max 70 chars) you provide." + \
+                    "<BR/>It does not work on mobile(timeout issue) click on examples if dont want to wait. " + \
+                    "<BR/>It may take ~10-20min to generate the comics.",
+                  examples=[(example_1),(example_2),(example_3)],
+                 )
+ui.launch()

install-deps.sh

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+#!/bin/bash 
+
+/bin/pip install -r requirements.txt

requirements.txt

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+numpy
+transformers
+sentence-transformers
+seaborn
+torch
+torchvision
+matplotlib
+pandas
+scikit-learn
+nltk
+gensim
+tensorflow
+keras
+opencv-python
+fastapi
+uvicorn
+gradio
+

run.sh

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+#!/bin/bash
+
+/bin/python src/main.py