Delete app.py

app.py

@@ -1,101 +0,0 @@
-import gradio as gr
-from transformers import pipeline
-import torch
-from diffusers import DiffusionPipeline
-
-# Load speech-to-text model (Whisper)
-transcriber = pipeline("automatic-speech-recognition", model="openai/whisper-base")
-
-# Load image generation model (Stable Diffusion)
-device = "cuda" if torch.cuda.is_available() else "cpu"
-pipe = DiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", 
-    torch_dtype=torch.float16 if device == "cuda" else torch.float32
-)
-pipe = pipe.to(device)
-
-# Speech-to-text function
-def transcribe_audio(audio):
-    """Convert audio to text using Whisper"""
-    if audio is None:
-        return ""
-    
-    try:
-        # Handle both file path and numpy array inputs
-        if isinstance(audio, tuple):
-            # If audio is a tuple of (sample_rate, audio_data)
-            sample_rate, audio_data = audio
-            result = transcriber({"sampling_rate": sample_rate, "raw": audio_data})
-        else:
-            # If audio is a file path
-            result = transcriber(audio, chunk_length_s=30, stride_length_s=5)
-        
-        return result.get("text", "")
-    except Exception as e:
-        return f"Error transcribing audio: {str(e)}"
-
-# Image generation function
-def generate_image_from_text(prompt):
-    """Generate an image from a text prompt using Stable Diffusion"""
-    if not prompt or prompt.strip() == "":
-        return None, "Please provide a text prompt"
-    
-    try:
-        with torch.no_grad():
-            image = pipe(prompt, num_inference_steps=50, guidance_scale=7.5).images[0]
-        return image, f"✓ Generated image from prompt: '{prompt}'"
-    except Exception as e:
-        return None, f"Error generating image: {str(e)}"
-
-# Combined function: speech -> text -> image
-def speech_to_image(audio):
-    """Convert speech to text, then generate image from the text"""
-    # Step 1: Convert speech to text
-    text_prompt = transcribe_audio(audio)
-    
-    if text_prompt.startswith("Error"):
-        return None, text_prompt
-    
-    # Step 2: Generate image from text
-    image, status = generate_image_from_text(text_prompt)
-    
-    return image, f"Transcript: '{text_prompt}'\n\n{status}"
-
-# Gradio interface with tabs
-with gr.Blocks(title="AI Image Generation from Speech") as demo:
-    gr.Markdown("# 🎨 AI Image Generation from Speech")
-    gr.Markdown("Speak your image description, and the AI will generate an image based on your words!")
-    
-    with gr.Tab("🎤 Speech to Image"):
-        gr.Markdown("Record or upload audio with your image description")
-        audio_input = gr.Audio(label="Record Audio", type="numpy")
-        generate_btn = gr.Button("Generate Image from Speech", variant="primary")
-        output_image = gr.Image(label="Generated Image")
-        output_text = gr.Textbox(label="Status", interactive=False)
-        
-        generate_btn.click(
-            fn=speech_to_image,
-            inputs=audio_input,
-            outputs=[output_image, output_text]
-        )
-    
-    with gr.Tab("⌨️ Text to Image"):
-        gr.Markdown("Or type a description directly")
-        text_input = gr.Textbox(
-            label="Enter Image Description", 
-            placeholder="e.g., a beautiful sunset over mountains",
-            lines=3
-        )
-        text_generate_btn = gr.Button("Generate Image", variant="primary")
-        text_output_image = gr.Image(label="Generated Image")
-        text_output_status = gr.Textbox(label="Status", interactive=False)
-        
-        text_generate_btn.click(
-            fn=generate_image_from_text,
-            inputs=text_input,
-            outputs=[text_output_image, text_output_status]
-        )
-
-# Launch the interface
-if __name__ == "__main__":
-    demo.launch()