Delete app.py

app.py

@@ -1,92 +0,0 @@
-import torch
-from transformers import LlavaForConditionalGeneration, BitsAndBytesConfig, AutoProcessor
-from transformers import MBartForConditionalGeneration, MBart50TokenizerFast
-import requests
-from PIL import Image
-import requests
-import gradio as gr
-
-
-# Load translation model and tokenizer
-translate_model_name = "facebook/mbart-large-50-many-to-many-mmt"
-translate_model = MBartForConditionalGeneration.from_pretrained(translate_model_name)
-tokenizer = MBart50TokenizerFast.from_pretrained(translate_model_name)
-
-# load the base model in 4 bit quantized
-quantization_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-)
-
-# finetuned model adapter path (Hugging Face Hub)
-model_id = 'somnathsingh31/llava-1.5-7b-hf-ft-merged_model'
-
-# merge the models
-merged_model = LlavaForConditionalGeneration.from_pretrained(model_id,
-                                                           quantization_config=quantization_config,
-                                                           torch_dtype=torch.float16)
-
-# create processor from base model
-processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
-
-# function to translate
-def translate(text, source_lang, target_lang):
-    # Set source language
-    tokenizer.src_lang = source_lang
-
-    # Encode the text
-    encoded_text = tokenizer(text, return_tensors="pt")
-
-    # Force target language token
-    forced_bos_token_id = tokenizer.lang_code_to_id[target_lang]
-
-    # Generate the translation
-    generated_tokens = translate_model.generate(**encoded_text, forced_bos_token_id=forced_bos_token_id)
-
-    # Decode the translation
-    translation = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
-
-    return translation
-
-
-# function for making inference
-def ask_vlm(hindi_input_text, image):
-    # translate from Hindi to English
-    prompt_eng = translate(hindi_input_text, "hi_IN", "en_XX")
-    prompt = "USER: <image>\n" + prompt_eng + " ASSISTANT:"
-
-    # If image is uploaded, open the image from bytes, else open from URL
-    if hasattr(image, 'read'):
-        image = Image.open(image)
-    else:
-        image = Image.open(requests.get(image, stream=True).raw)
-    
-    inputs = processor(text=prompt, images=image, return_tensors="pt")
-    generate_ids = merged_model.generate(**inputs, max_new_tokens=250)
-    decoded_response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    assistant_index = decoded_response.find("ASSISTANT:")
-
-    # Extract text after "ASSISTANT:"
-    if assistant_index != -1:
-        text_after_assistant = decoded_response[assistant_index + len("ASSISTANT:"):]
-        # Remove leading and trailing whitespace
-        text_after_assistant = text_after_assistant.strip()
-    else:
-        text_after_assistant = None
-
-    hindi_output_text = translate(text_after_assistant, "en_XX", "hi_IN")
-    return hindi_output_text
-
-# Define Gradio interface
-input_image = gr.inputs.Image(type="pil", label="Input Image (Upload or URL)")
-input_question = gr.inputs.Textbox(lines=2, label="Question (Hindi)")
-output_text = gr.outputs.Textbox(label="Response (Hindi)")
-
-# Create Gradio app
-gr.Interface(fn=ask_vlm, inputs=[input_question, input_image], outputs=output_text, title="Image and Text-based Dialogue System", description="Enter a question in Hindi and an image, either by uploading or providing URL, and get a response in Hindi.").launch()
-
-
-if __name__ ==  '__main__':
-    image_url = 'https://images.metmuseum.org/CRDImages/ad/original/138425.jpg'
-    user_query = 'यह किस प्रकार की कला है? विस्तार से बताइये'
-    output = ask_vlm(user_query, image_url)
-    print('Output:\n', output)