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| import streamlit as st | |
| from diffusers import StableDiffusionPipeline | |
| import torch | |
| from PIL import Image, ImageDraw, ImageFont | |
| import tempfile | |
| import speech_recognition as sr | |
| # Load Stable Diffusion | |
| def load_model(): | |
| model_id = "runwayml/stable-diffusion-v1-5" | |
| pipe = StableDiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16) | |
| pipe = pipe.to("cuda" if torch.cuda.is_available() else "cpu") | |
| return pipe | |
| sd_pipe = load_model() | |
| # Meme generator function | |
| def generate_meme(prompt, caption=""): | |
| # Step 1: Generate base image | |
| image = sd_pipe(prompt, num_inference_steps=30).images[0] | |
| # Step 2: Add caption text | |
| if caption: | |
| draw = ImageDraw.Draw(image) | |
| try: | |
| font = ImageFont.truetype("DejaVuSans-Bold.ttf", 48) | |
| except: | |
| font = ImageFont.load_default() | |
| W, H = image.size | |
| text = caption.upper() | |
| # Wrap long text | |
| import textwrap | |
| lines = textwrap.wrap(text, width=25) | |
| y_text = 20 | |
| for line in lines: | |
| w, h = draw.textsize(line, font=font) | |
| draw.text(((W - w) / 2, y_text), line, font=font, | |
| fill="white", stroke_width=3, stroke_fill="black") | |
| y_text += h + 10 | |
| return image | |
| # Speech-to-text | |
| def speech_to_text(audio_file): | |
| recognizer = sr.Recognizer() | |
| with sr.AudioFile(audio_file) as source: | |
| audio = recognizer.record(source) | |
| try: | |
| return recognizer.recognize_google(audio) | |
| except: | |
| return "Could not recognize speech." | |
| # Streamlit UI | |
| st.title("🎭 Meme Generator (Text & Voice)") | |
| mode = st.radio("Choose Input Mode:", ["Text", "Voice"]) | |
| if mode == "Text": | |
| text_input = st.text_area("Enter meme text:") | |
| if st.button("Generate Meme"): | |
| if text_input.strip(): | |
| img = generate_meme("funny cartoon meme background", caption=text_input) | |
| st.image(img, caption="Generated Meme", use_column_width=True) | |
| else: | |
| st.warning("Please enter some text.") | |
| else: | |
| audio_file = st.file_uploader("Upload voice file (.wav)", type=["wav"]) | |
| if audio_file: | |
| with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as tmp_file: | |
| tmp_file.write(audio_file.read()) | |
| tmp_path = tmp_file.name | |
| text = speech_to_text(tmp_path) | |
| st.write(f"📝 Transcribed Text: {text}") | |
| if st.button("Generate Meme from Voice"): | |
| if text.strip(): | |
| img = generate_meme("funny cartoon meme background", caption=text) | |
| st.image(img, caption="Generated Meme", use_column_width=True) | |
| else: | |
| st.warning("Speech not recognized.") | |