app.py

# import gradio as gr
# import requests
# import time
# from PIL import Image
# from io import BytesIO

# # AssemblyAI API Key
# ASSEMBLYAI_API_KEY = "your_assemblyai_api_key_here"
# # DeepAI API Key
# DEEPAI_API_KEY = "your_deepai_api_key_here"

# # Function to convert speech to text using AssemblyAI API
# def speech_to_text(audio_file):
#     # Upload audio to AssemblyAI for transcription
#     upload_url = "https://api.assemblyai.com/v2/upload"
#     headers = {
#         "authorization": ASSEMBLYAI_API_KEY
#     }

#     # Upload the audio file to AssemblyAI
#     with open(audio_file, 'rb') as file:
#         response = requests.post(upload_url, headers=headers, files={"file": file})

#     if response.status_code != 200:
#         return "Error uploading audio."

#     audio_url = response.json()["upload_url"]

#     # Request transcription from AssemblyAI
#     transcript_url = "https://api.assemblyai.com/v2/transcript"
#     transcript_request = {
#         "audio_url": audio_url
#     }
#     transcript_response = requests.post(transcript_url, json=transcript_request, headers=headers)

#     if transcript_response.status_code != 200:
#         return "Error requesting transcription."

#     transcript_id = transcript_response.json()["id"]

#     # Poll for transcription completion
#     while True:
#         polling_url = f"https://api.assemblyai.com/v2/transcript/{transcript_id}"
#         polling_response = requests.get(polling_url, headers=headers)

#         if polling_response.status_code != 200:
#             return "Error polling for transcription status."

#         status = polling_response.json()["status"]
#         if status == "completed":
#             return polling_response.json()["text"]
#         elif status == "failed":
#             return "Transcription failed."

#         time.sleep(5)  # Wait 5 seconds before polling again

# # Function to generate an image based on text using DeepAI's Image Generation API
# def generate_image_from_text(text):
#     image_generation_url = "https://api.deepai.org/api/text2img"
#     headers = {
#         "api-key": DEEPAI_API_KEY
#     }
#     payload = {
#         "text": text
#     }

#     # Request image generation from DeepAI
#     response = requests.post(image_generation_url, data=payload, headers=headers)

#     if response.status_code == 200:
#         # Get the image URL from the response
#         image_url = response.json()["output_url"]
#         return image_url
#     else:
#         return "Failed to generate image."

# # Function to download image from URL and return as a PIL image
# def get_image_from_url(image_url):
#     try:
#         response = requests.get(image_url)
#         img = Image.open(BytesIO(response.content))
#         return img
#     except Exception as e:
#         return "Error downloading image: " + str(e)

# # Gradio Interface function
# def process_audio(audio_file):
#     # Convert speech to text
#     text = speech_to_text(audio_file)
#     if text and text != "Error uploading audio." and text != "Error requesting transcription.":
#         print(f"Transcribed text: {text}")  # Debug output for transcribed text
        
#         # Generate image from the transcribed text
#         image_url = generate_image_from_text(text)
#         if "Failed" not in image_url:
#             print(f"Image URL: {image_url}")  # Debug output for image URL
#             # Download the image from URL and return it as a PIL image
#             return get_image_from_url(image_url)
#         else:
#             return image_url
#     else:
#         return "Error processing audio."

# # Set up Gradio interface
# iface = gr.Interface(fn=process_audio,
#                      inputs=gr.Audio(type="filepath"),  # Audio input
#                      outputs=gr.Image(type="pil"),  # Image output as PIL image
#                      live=True,
#                      title="Speech-to-Text to Image Generator")

# iface.launch()


# import gradio as gr
# import requests
# import time
# from PIL import Image
# from io import BytesIO

# # API keys
# ASSEMBLYAI_API_KEY = "your_assemblyai_api_key_here"
# STABILITY_AI_API_KEY = "your_stability_ai_api_key_here"

# # Function to convert speech to text using AssemblyAI API
# def speech_to_text(audio_file):
#     upload_url = "https://api.assemblyai.com/v2/upload"
#     headers = {
#         "authorization": ASSEMBLYAI_API_KEY
#     }

#     # Upload the audio file to AssemblyAI
#     with open(audio_file, 'rb') as file:
#         response = requests.post(upload_url, headers=headers, files={"file": file})

#     if response.status_code != 200:
#         return "Error uploading audio."

#     audio_url = response.json()["upload_url"]

#     # Request transcription from AssemblyAI
#     transcript_url = "https://api.assemblyai.com/v2/transcript"
#     transcript_request = {
#         "audio_url": audio_url
#     }
#     transcript_response = requests.post(transcript_url, json=transcript_request, headers=headers)

#     if transcript_response.status_code != 200:
#         return "Error requesting transcription."

#     transcript_id = transcript_response.json()["id"]

#     # Poll for transcription completion
#     while True:
#         polling_url = f"https://api.assemblyai.com/v2/transcript/{transcript_id}"
#         polling_response = requests.get(polling_url, headers=headers)

#         if polling_response.status_code != 200:
#             return "Error polling for transcription status."

#         status = polling_response.json()["status"]
#         if status == "completed":
#             return polling_response.json()["text"]
#         elif status == "failed":
#             return "Transcription failed."

#         time.sleep(5)  # Wait 5 seconds before polling again

# # Function to generate an image based on text using Stability AI (Stable Diffusion)
# def generate_image_from_text(text):
#     image_generation_url = "https://stability.ai/api/v3/generate"  # Stability AI API endpoint (assuming)
#     headers = {
#         "Authorization": f"Bearer {STABILITY_AI_API_KEY}"
#     }
#     payload = {
#         "text": text,
#         "width": 512,  # Adjust image dimensions as needed
#         "height": 512
#     }

#     # Request image generation from Stability AI
#     response = requests.post(image_generation_url, json=payload, headers=headers)

#     if response.status_code == 200:
#         # Get the image URL from the response (assuming the response contains a URL)
#         image_url = response.json().get("image_url", "")
#         if image_url:
#             return image_url
#         else:
#             return "Failed to generate image: No image URL found in response."
#     else:
#         return f"Failed to generate image: {response.status_code}"

# # Function to download image from URL and return as a PIL image
# def get_image_from_url(image_url):
#     try:
#         response = requests.get(image_url)
#         img = Image.open(BytesIO(response.content))
#         return img
#     except Exception as e:
#         return f"Error downloading image: {str(e)}"

# # Gradio Interface function
# def process_audio(audio_file):
#     # Convert speech to text
#     text = speech_to_text(audio_file)
#     if text and text != "Error uploading audio." and text != "Error requesting transcription.":
#         print(f"Transcribed text: {text}")  # Debug output for transcribed text
        
#         # Generate image from the transcribed text
#         image_url = generate_image_from_text(text)
#         if "Failed" not in image_url:
#             print(f"Image URL: {image_url}")  # Debug output for image URL
#             # Download the image from URL and return it as a PIL image
#             return get_image_from_url(image_url)
#         else:
#             return image_url
#     else:
#         return "Error processing audio."

# # Set up Gradio interface
# iface = gr.Interface(fn=process_audio,
#                      inputs=gr.Audio(type="filepath"),  # Audio input
#                      outputs=gr.Image(type="pil"),  # Image output as PIL image
#                      live=True,
#                      title="Speech-to-Text to Image Generator")

# iface.launch()
#1st D
import subprocess

# Install required libraries
subprocess.check_call(["pip", "install", "torch>=1.11.0"])
subprocess.check_call(["pip", "install", "transformers"])
subprocess.check_call(["pip", "install", "diffusers>=0.14.0"])
subprocess.check_call(["pip", "install", "librosa"])
subprocess.check_call(["pip", "install", "accelerate>= 0.20.1"])
subprocess.check_call(["pip", "install", "safetensors>=0.1.0"])
subprocess.check_call(["pip", "install", "huggingface_hub>=0.16.4"])

import os
import threading
import numpy as np
import diffusers
from functools import lru_cache
import gradio as gr
from transformers import pipeline
from huggingface_hub import login
from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
import librosa
import accelerate
import pandas
import safetensors
import torch  # Import torch here to avoid the NameError

# Ensure required dependencies are installed
def install_missing_packages():
    required_packages = {
        "librosa": None, 
        "diffusers": ">=0.14.0",
        "gradio": ">=3.35.2",
        "huggingface_hub": ">=0.16.4",
        "accelerate": ">= 0.20.1",
        "safetensors":">=0.1.0",
        "torch":">=1.11.0",
    }
    for package, version in required_packages.items():
        try:
            __import__(package)
        except ImportError:
            package_name = f"{package}{version}" if version else package
            subprocess.check_call(["pip", "install", package_name])

install_missing_packages()

# Get Hugging Face token for authentication
hf_token = os.getenv("HF_TOKEN")
if hf_token:
    login(hf_token)
else:
    raise ValueError("HF_TOKEN environment variable not set.")

# Load speech-to-text model (Whisper)
speech_to_text = pipeline("automatic-speech-recognition", model="openai/whisper-tiny")

#Load Stable Diffusion model for text-to-image
text_to_image = StableDiffusionPipeline.from_pretrained(
    "runwayml/stable-diffusion-v1-5"     
)
# text_to_image = StableDiffusionPipeline.from_pretrained(
#     "runwayml/stable-diffusion-v1-5",
#     cache_dir="./my_model_cache",  # Custom cache directory
#     revision="fp16"
# )

device = "cuda" if torch.cuda.is_available() else "cpu"  # This will now work since torch is imported
text_to_image.to(device)
text_to_image.enable_attention_slicing()  # Optimizes memory usage
text_to_image.safety_checker = None  # Disables safety checker to improve speed
text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)  # Faster scheduler

# Preprocess audio file into NumPy array
def preprocess_audio(audio_path):
    try:
        audio, sr = librosa.load(audio_path, sr=16000)  # Resample to 16kHz
        return np.array(audio, dtype=np.float16)
    except Exception as e:
        return f"Error in preprocessing audio: {str(e)}"

# Speech-to-text function
@lru_cache(maxsize=10)
def transcribe_audio(audio_path):
    try:
        audio_array = preprocess_audio(audio_path)
        if isinstance(audio_array, str):  # Error message from preprocessing
            return audio_array
        result = speech_to_text(audio_array)
        return result["text"]
    except Exception as e:
        return f"Error in transcription: {str(e)}"

# Text-to-image function
@lru_cache(maxsize=10)
def generate_image_from_text(text):
    try:
        image = text_to_image(text, height=256, width=256).images[0]  # Generate smaller images for speed
        return image
    except Exception as e:
        return f"Error in image generation: {str(e)}"

# Optimized combined processing function
def process_audio_and_generate_image(audio_path):
    transcription_result = {"result": None}
    image_result = {"result": None}

    # Function to run transcription and image generation in parallel
    def transcription_thread():
        transcription_result["result"] = transcribe_audio(audio_path)

    def image_generation_thread():
        transcription = transcription_result["result"]
        if transcription and "Error" not in transcription:
            image_result["result"] = generate_image_from_text(transcription)

    # Start both tasks in parallel
    t1 = threading.Thread(target=transcription_thread)
    t2 = threading.Thread(target=image_generation_thread)

    t1.start()
    t2.start()

    t1.join()  # Wait for transcription to finish
    t2.join()  # Wait for image generation to finish

    transcription = transcription_result["result"]
    image = image_result["result"]

    if "Error" in transcription:
        return None, transcription
    if isinstance(image, str) and "Error" in image:
        return None, image

    return image, transcription

# Gradio interface
iface = gr.Interface(
    fn=process_audio_and_generate_image,
    inputs=gr.Audio(type="filepath", label="Upload audio file (WAV/MP3)"),
    outputs=[gr.Image(label="Generated Image"), gr.Textbox(label="Transcription")],
    title="Voice-to-Image Generator",
    description="Upload an audio file to transcribe speech to text, and then generate an image based on the transcription.",
)

# Launch Gradio interface
iface.launch(debug=True, share=True)
#2 D