Upload app.py

app.py

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+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:
+        # Gradio Audio with type="numpy" returns tuple of (sample_rate, audio_data)
+        if isinstance(audio, tuple):
+            sample_rate, audio_data = audio
+            # Create a dictionary with the audio data for the pipeline
+            result = transcriber({"array": audio_data, "sampling_rate": sample_rate})
+        else:
+            result = transcriber(audio)
+        
+        text = result.get("text", "").strip()
+        return text if text else "No speech detected"
+    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()