Delete app.py

app.py

@@ -1,89 +0,0 @@
-import streamlit as st
-from PIL import Image
-from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
-from gtts import gTTS
-import torch
-import subprocess
-import sys
-
-# Install transformers if not already installed
-try:
-    import transformers
-except ImportError:
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers"])
-try:
-    import transformers
-    st.write("Transformers module is already installed!")
-except ImportError:
-    st.write("Installing transformers...")
-    subprocess.check_call([sys.executable, "-m", "pip", "install", "transformers"])
-    st.write("Transformers installed successfully!")
-
-st.write("Environment is working!")
-# Debug: Start of the app
-st.title("Image-to-Audio Description Generator")
-
-# Step 1: Load LLaVA Processor and Model
-st.write("Loading processor and model...")
-try:
-    processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
-    st.write("Processor loaded successfully!")
-except Exception as e:
-    st.write(f"Error loading processor: {str(e)}")
-
-try:
-    model = LlavaNextForConditionalGeneration.from_pretrained(
-        "llava-hf/llava-v1.6-mistral-7b-hf",
-        torch_dtype=torch.float16,
-        low_cpu_mem_usage=True
-    ).to("cuda:0")
-    st.write("Model loaded successfully!")
-except Exception as e:
-    st.write(f"Error loading model: {str(e)}")
-
-# Step 2: Upload Image
-uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"])
-if uploaded_image:
-    st.write("Processing uploaded image...")
-    # Load and preprocess image
-    try:
-        image = Image.open(uploaded_image).convert("RGB")
-        image = image.resize((336, 336))
-        st.image(image, caption="Uploaded Image", use_column_width=True)
-    except Exception as e:
-        st.write(f"Error loading image: {str(e)}")
-
-    # Step 3: Generate Description
-    st.write("Generating description...")
-    try:
-        conversation = [
-            {
-                "role": "user",
-                "content": [
-                    {"type": "text", "text": "What is shown in this image?"},
-                    {"type": "image"},
-                ],
-            },
-        ]
-        prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
-        inputs = processor(images=image, text=prompt, return_tensors="pt").to("cuda:0")
-        output = model.generate(
-            **inputs, max_new_tokens=100, pad_token_id=processor.tokenizer.eos_token_id
-        )
-        description = processor.decode(output[0], skip_special_tokens=True)
-        st.write(f"Generated Description: {description}")
-    except Exception as e:
-        st.write(f"Error generating description: {str(e)}")
-
-    # Step 4: Text-to-Speech Conversion
-    st.write("Converting description to audio...")
-    try:
-        tts = gTTS(description)
-        audio_path = "output.mp3"
-        tts.save(audio_path)
-
-        # Step 5: Play Audio
-        st.audio(audio_path, format="audio/mp3")
-        st.write("Audio generated successfully!")
-    except Exception as e:
-        st.write(f"Error converting text to audio: {str(e)}")