Update app.py

app.py

@@ -1,182 +1,3 @@
-# import subprocess
-
-# # Install required libraries
-# subprocess.check_call(["pip", "install", "torch>=1.11.0"])
-# subprocess.check_call(["pip", "install", "transformers>=4.31.0"])
-# 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", "gradio>=3.35.2"])
-# subprocess.check_call(["pip", "install", "huggingface_hub"])
-
-# import os
-# import threading
-# import numpy as np
-# import librosa
-# import torch
-# import gradio as gr
-# from functools import lru_cache
-# from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
-# from huggingface_hub import login
-# from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
-
-# # Ensure required dependencies are installed
-# def install_missing_packages():
-#     required_packages = {
-#         "librosa": None,
-#         "diffusers": ">=0.14.0",
-#         "gradio": ">=3.35.2",
-#         "huggingface_hub": None,
-#         "accelerate": ">=0.20.1",
-#         "transformers": ">=4.31.0"
-#     }
-#     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", 
-#     return_timestamps=True
-# )
-
-# # Load Stable Diffusion model for text-to-image
-# text_to_image = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
-# device = "cuda" if torch.cuda.is_available() else "cpu"
-# text_to_image.to(device)
-# text_to_image.enable_attention_slicing()
-# text_to_image.safety_checker = None
-# text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)
-
-# # Load ChatGPT-like conversational model
-# chat_model_name = "microsoft/DialoGPT-medium"
-# chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_name)
-# chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name)
-
-# # 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.float32)
-#     except Exception as e:
-#         return f"Error in preprocessing audio: {str(e)}"
-
-# # Speech-to-text function with long-form transcription support
-# @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)
-#         # Combine text from multiple segments for long-form transcription
-#         transcription = " ".join(segment["text"] for segment in result["chunks"])
-#         return transcription
-#     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)}"
-
-# # ChatGPT-like conversational response
-# def chat_with_gpt(prompt):
-#     try:
-#         inputs = chat_tokenizer.encode(prompt, return_tensors="pt")
-#         outputs = chat_model.generate(inputs, max_length=200, pad_token_id=chat_tokenizer.eos_token_id)
-#         response = chat_tokenizer.decode(outputs[0], skip_special_tokens=True)
-#         return response
-#     except Exception as e:
-#         return f"Error in chat response: {str(e)}"
-
-# # Combined processing function
-# def process_audio_and_generate_results(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 for speech-to-text
-# speech_to_text_iface = gr.Interface(
-#     fn=transcribe_audio,
-#     inputs=gr.Audio(type="filepath", label="Upload audio file for transcription (WAV/MP3)"),
-#     outputs=gr.Textbox(label="Transcription"),
-#     title="Speech-to-Text Transcription",
-#     description="Upload an audio file to transcribe speech into text.",
-# )
-
-# # Gradio interface for voice-to-image and chat
-# voice_to_image_and_chat_iface = gr.Interface(
-#     fn=process_audio_and_generate_results,
-#     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 and Chat",
-#     description="Upload an audio file to transcribe speech to text, generate an image based on the transcription, or chat with GPT.",
-# )
-
-# # Gradio interface for ChatGPT-like functionality
-# chat_iface = gr.Interface(
-#     fn=chat_with_gpt,
-#     inputs=gr.Textbox(label="Enter your prompt for ChatGPT"),
-#     outputs=gr.Textbox(label="ChatGPT Response"),
-#     title="ChatGPT",
-#     description="Chat with GPT-like conversational AI.",
-# )
-
-# # Combined Gradio app
-# iface = gr.TabbedInterface(
-#     interface_list=[speech_to_text_iface, voice_to_image_and_chat_iface, chat_iface],
-#     tab_names=["Speech-to-Text", "Voice-to-Image & Chat", "ChatGPT"]
-# )
-
-# # Launch Gradio interface
-# iface.launch(debug=True, share=True)
-
 import subprocess
 
 # Install required libraries
@@ -195,7 +16,7 @@ import librosa
 import torch
 import gradio as gr
 from functools import lru_cache
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 from huggingface_hub import login
 from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
 
@@ -240,6 +61,11 @@ text_to_image.enable_attention_slicing()
 text_to_image.safety_checker = None
 text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)
 
+# Load ChatGPT-like conversational model
+chat_model_name = "microsoft/DialoGPT-medium"
+chat_tokenizer = AutoTokenizer.from_pretrained(chat_model_name)
+chat_model = AutoModelForCausalLM.from_pretrained(chat_model_name)
+
 # Preprocess audio file into NumPy array
 def preprocess_audio(audio_path):
     try:
@@ -271,6 +97,16 @@ def generate_image_from_text(text):
     except Exception as e:
         return f"Error in image generation: {str(e)}"
 
+# ChatGPT-like conversational response
+def chat_with_gpt(prompt):
+    try:
+        inputs = chat_tokenizer.encode(prompt, return_tensors="pt")
+        outputs = chat_model.generate(inputs, max_length=200, pad_token_id=chat_tokenizer.eos_token_id)
+        response = chat_tokenizer.decode(outputs[0], skip_special_tokens=True)
+        return response
+    except Exception as e:
+        return f"Error in chat response: {str(e)}"
+
 # Combined processing function
 def process_audio_and_generate_results(audio_path):
     transcription_result = {"result": None}
@@ -314,24 +150,188 @@ speech_to_text_iface = gr.Interface(
     description="Upload an audio file to transcribe speech into text.",
 )
 
-# Gradio interface for voice-to-image
-voice_to_image_iface = gr.Interface(
+# Gradio interface for voice-to-image and chat
+voice_to_image_and_chat_iface = gr.Interface(
     fn=process_audio_and_generate_results,
     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",
-    description="Upload an audio file to transcribe speech to text and generate an image based on the transcription.",
+    title="Voice-to-Image and Chat",
+    description="Upload an audio file to transcribe speech to text, generate an image based on the transcription, or chat with GPT.",
+)
+
+# Gradio interface for ChatGPT-like functionality
+chat_iface = gr.Interface(
+    fn=chat_with_gpt,
+    inputs=gr.Textbox(label="Enter your prompt for ChatGPT"),
+    outputs=gr.Textbox(label="ChatGPT Response"),
+    title="ChatGPT",
+    description="Chat with GPT-like conversational AI.",
 )
 
 # Combined Gradio app
 iface = gr.TabbedInterface(
-    interface_list=[speech_to_text_iface, voice_to_image_iface],
-    tab_names=["Speech-to-Text", "Voice-to-Image"]
+    interface_list=[speech_to_text_iface, voice_to_image_and_chat_iface, chat_iface],
+    tab_names=["Speech-to-Text", "Voice-to-Image & Chat", "ChatGPT"]
 )
 
 # Launch Gradio interface
 iface.launch(debug=True, share=True)
 
+# import subprocess
+
+# # Install required libraries
+# subprocess.check_call(["pip", "install", "torch>=1.11.0"])
+# subprocess.check_call(["pip", "install", "transformers>=4.31.0"])
+# 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", "gradio>=3.35.2"])
+# subprocess.check_call(["pip", "install", "huggingface_hub"])
+
+# import os
+# import threading
+# import numpy as np
+# import librosa
+# import torch
+# import gradio as gr
+# from functools import lru_cache
+# from transformers import pipeline
+# from huggingface_hub import login
+# from diffusers import StableDiffusionPipeline, DPMSolverMultistepScheduler
+
+# # Ensure required dependencies are installed
+# def install_missing_packages():
+#     required_packages = {
+#         "librosa": None,
+#         "diffusers": ">=0.14.0",
+#         "gradio": ">=3.35.2",
+#         "huggingface_hub": None,
+#         "accelerate": ">=0.20.1",
+#         "transformers": ">=4.31.0"
+#     }
+#     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", 
+#     return_timestamps=True
+# )
+
+# # Load Stable Diffusion model for text-to-image
+# text_to_image = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5")
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# text_to_image.to(device)
+# text_to_image.enable_attention_slicing()
+# text_to_image.safety_checker = None
+# text_to_image.scheduler = DPMSolverMultistepScheduler.from_config(text_to_image.scheduler.config)
+
+# # 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.float32)
+#     except Exception as e:
+#         return f"Error in preprocessing audio: {str(e)}"
+
+# # Speech-to-text function with long-form transcription support
+# @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)
+#         # Combine text from multiple segments for long-form transcription
+#         transcription = " ".join(segment["text"] for segment in result["chunks"])
+#         return transcription
+#     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)}"
+
+# # Combined processing function
+# def process_audio_and_generate_results(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 for speech-to-text
+# speech_to_text_iface = gr.Interface(
+#     fn=transcribe_audio,
+#     inputs=gr.Audio(type="filepath", label="Upload audio file for transcription (WAV/MP3)"),
+#     outputs=gr.Textbox(label="Transcription"),
+#     title="Speech-to-Text Transcription",
+#     description="Upload an audio file to transcribe speech into text.",
+# )
+
+# # Gradio interface for voice-to-image
+# voice_to_image_iface = gr.Interface(
+#     fn=process_audio_and_generate_results,
+#     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",
+#     description="Upload an audio file to transcribe speech to text and generate an image based on the transcription.",
+# )
+
+# # Combined Gradio app
+# iface = gr.TabbedInterface(
+#     interface_list=[speech_to_text_iface, voice_to_image_iface],
+#     tab_names=["Speech-to-Text", "Voice-to-Image"]
+# )
+
+# # Launch Gradio interface
+# iface.launch(debug=True, share=True)
+