Delete object_detection.py

object_detection.py

@@ -1,68 +0,0 @@
-import torch
-from transformers import BlipProcessor, BlipForConditionalGeneration
-from gtts import gTTS
-import tempfile
-import subprocess
-import sys
-import gradio
-
-
-def ensure_package_installed(package_name):
-    try:
-        __import__(package_name)
-    except ImportError:
-        print(f"{package_name} package not found. Installing...")
-        subprocess.check_call([sys.executable, "-m", "pip", "install", package_name])
-        __import__(package_name)
-
-# Check and install openai
-ensure_package_installed("gradio")
-ensure_package_installed("transformers")
-ensure_package_installed("gtts")
-
-
-# Load the image captioning model
-processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-base")
-model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-base")
-
-def generate_description(image):
-    """Generates a textual description of the given image using a pre-trained BLIP model."""
-    inputs = processor(image, return_tensors="pt").to(model.device)
-    output = model.generate(**inputs)
-    description = processor.decode(output[0], skip_special_tokens=True)
-    return description
-
-def text_to_speech(text):
-    """Converts text to speech using gTTS and returns the audio file path."""
-    tts = gTTS(text=text, lang='en')
-    temp_audio = tempfile.NamedTemporaryFile(delete=False, suffix=".mp3")
-    tts.save(temp_audio.name)
-    return temp_audio.name
-
-def process_image(image):
-    """Processes the uploaded image to generate description and return audio file."""
-    description = generate_description(image)
-    return description
-
-def get_audio(description):
-    """Generates the audio file for the given description."""
-    return text_to_speech(description)
-
-# Build Gradio Interface
-with gradio.Blocks() as demo:
-    gradio.Markdown("# Image Description and Audio Transcript App")
-    gradio.Markdown("Upload an image to get an AI-generated description. Click the button to hear the description.")
-    
-    with gradio.Row():
-        image_input = gradio.Image(type="pil")
-        text_output = gradio.Textbox(label="Generated Description")
-    
-    generate_btn = gradio.Button("Generate Description")
-    audio_btn = gradio.Button("Click here for an audio transcript")
-    audio_output = gradio.Audio()
-    
-    generate_btn.click(process_image, inputs=[image_input], outputs=[text_output])
-    audio_btn.click(get_audio, inputs=[text_output], outputs=[audio_output])
-    
-# Launch the Gradio app
-demo.launch()