Add application1 file2

app.py

@@ -1,90 +1,111 @@
 import torch
-from transformers import AutoProcessor, LlavaForConditionalGeneration, BitsAndBytesConfig
-from peft import PeftModel
+from transformers import  AutoProcessor,LlavaForConditionalGeneration, BitsAndBytesConfig
+from peft import  PeftModel
+from PIL import Image
 from deep_translator import GoogleTranslator
 import gradio as gr
-import base64
-
-
-
-model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
-quantization_config = BitsAndBytesConfig(load_in_4bit=True)
-base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
-
-# Load the PEFT Lora adapter
-peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
-peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
-base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
-
-processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
-
-# Function to translate text from Bengali to English
-def deep_translator_bn_en(input_sentence):
-    english_translation = GoogleTranslator(source="bn", target="en").translate(input_sentence)
-    return english_translation
-
-# Function to translate text from English to Bengali
-def deep_translator_en_bn(input_sentence):
-    bengali_translation = GoogleTranslator(source="en", target="bn").translate(input_sentence)
-    return bengali_translation
-
-def inference(image, image_prompt):
-    prompt = f"USER: <image>\n{image_prompt} ASSISTANT:"
-
-    # Assuming your model can handle PIL images
-    image = image.convert("RGB")  # Ensure image is RGB mode
-
-    inputs = processor(text=prompt, images=image, return_tensors="pt")
-    generate_ids = base_model.generate(**inputs, max_new_tokens=15)
-    decoded_response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
-    return decoded_response
-
-def image_to_base64(image_path):
-    with open(image_path, 'rb') as img:
-        encoded_string = base64.b64encode(img.read())
-    return encoded_string.decode('utf-8')
-
-# Function that takes User Inputs and displays it on ChatUI
-def query_message(history,txt,img):
-    image_prompt = deep_translator_bn_en(txt)
-    history += [(image_prompt,None)]
-    base64 = image_to_base64(img)
-    data_url = f"data:image/jpeg;base64,{base64}"
-    history += [(f"{image_prompt} ![]({data_url})", None)]
-    return history
-
-# Function that takes User Inputs, generates Response and displays on Chat UI
-def llm_response(history,text,img):
-        image_prompt = deep_translator_bn_en(text)
-        response = inference(img,image_prompt)
-        assistant_index = response.find("ASSISTANT:")
-        extracted_string = response[assistant_index + len("ASSISTANT:"):].strip()
-        output = deep_translator_en_bn(extracted_string)
-        history += [(text,output)]
-        return history
+from transformers import TextIteratorStreamer
+from threading import Thread
+import time
+
+
+# model_id = "HuggingFaceH4/vsft-llava-1.5-7b-hf-trl"
+# quantization_config = BitsAndBytesConfig(load_in_4bit=True)
+# base_model = LlavaForConditionalGeneration.from_pretrained(model_id, quantization_config=quantization_config, torch_dtype=torch.float16)
+
+# # Load the PEFT Lora adapter
+# peft_lora_adapter_path = "Praveen0309/llava-1.5-7b-hf-ft-mix-vsft-3"
+# peft_lora_adapter = PeftModel.from_pretrained(base_model, peft_lora_adapter_path, adapter_name="lora_adapter")
+# base_model.load_adapter(peft_lora_adapter_path, adapter_name="lora_adapter")
+
+# processor = AutoProcessor.from_pretrained("HuggingFaceH4/vsft-llava-1.5-7b-hf-trl")
+
+# # Function to translate text from Bengali to English
+# def deep_translator_bn_en(input_sentence):
+#     english_translation = GoogleTranslator(source="bn", target="en").translate(input_sentence)
+#     return english_translation
+
+# # Function to translate text from English to Bengali
+# def deep_translator_en_bn(input_sentence):
+#     bengali_translation = GoogleTranslator(source="en", target="bn").translate(input_sentence)
+#     return bengali_translation
+
+# def bot_streaming(message, history):
+#     print(message)
+
+#     if message["files"]:
+#         # message["files"][-1] is a Dict or just a string
+#         if type(message["files"][-1]) == dict:
+#             image = message["files"][-1]["path"]
+#         else:
+#             image = message["files"][-1]
+#     else:
+#         # if there's no image uploaded for this turn, look for images in the past turns
+#         # kept inside tuples, take the last one
+#         for hist in history:
+#             if type(hist[0]) == tuple:
+#                 image = hist[0][0]
+#                 break  # Exit the loop after finding the first image
+
+#     try:
+#         if image is None:
+#             # Handle the case where image is None
+#             raise Exception("You need to upload an image for LLaVA to work.")
+#     except NameError:
+#         # Handle the case where 'image' is not defined at all
+#         raise Exception("You need to upload an image for LLaVA to work.")
+
+#     # Translate Bengali input to English before processing
+#     english_prompt = deep_translator_bn_en(message['text'])
+
+#     prompt = f"<|start_header_id|>user<|end_header_id|>\n\n<image>\n{english_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+#     # print(f"prompt: {prompt}")
+
+#     image = Image.open(image)
+#     inputs = processor(prompt, image, return_tensors='pt').to(0, torch.float16)
+
+#     streamer = TextIteratorStreamer(processor, **{"skip_special_tokens": False, "skip_prompt": True})
+#     generation_kwargs = dict(inputs, streamer=streamer, max_new_tokens=512, do_sample=False)
+
+#     thread = Thread(target=base_model.generate, kwargs=generation_kwargs)
+#     thread.start()
+
+#     text_prompt = f"<|start_header_id|>user<|end_header_id|>\n\n{english_prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
+#     # print(f"text_prompt: {text_prompt}")
+
+#     buffer = ""
+#     time.sleep(0.5)
+#     for new_text in streamer:
+#         # find <|eot_id|> and remove it from the new_text
+#         if "<|eot_id|>" in new_text:
+#             new_text = new_text.split("<|eot_id|>")[0]
+#         buffer += new_text
+
+#         # generated_text_without_prompt = buffer[len(text_prompt):]
+#         generated_text_without_prompt = buffer
+
+#         # Translate English response from LLaVA back to Bengali
+#         bengali_response = deep_translator_en_bn(generated_text_without_prompt)
+
+#         # print(f"new_text: {bengali_response}")
+#         yield bengali_response
+
+#     thread.join()
+
 
 # Interface Code
-with gr.Blocks() as app:
-    with gr.Row():
-        image_box = gr.Image(type="pil")
-
-        chatbot = gr.Chatbot(
-            scale = 2,
-            height=500
-        )
-    text_box = gr.Textbox(
-            placeholder="Enter text and press enter, or upload an image",
-            container=False,
-        )
-
-    btn = gr.Button("Submit")
-    clicked = btn.click(query_message,
-                        [chatbot,text_box,image_box],
-                        chatbot
-                        ).then(llm_response,
-                                [chatbot,text_box,image_box],
-                                chatbot
-                                )
-app.queue()
-app.launch(debug=True)
+# chatbot=gr.Chatbot(scale=1)
+# chat_input = gr.MultimodalTextbox(interactive=True, file_types=["image"], placeholder="Enter message or upload file...", show_label=False)
+# with gr.Blocks(fill_height=True, ) as app:
+#     gr.ChatInterface(
+#     fn=bot_streaming,
+#     description="Try Cleaveland Chatbot. Upload an image and start chatting about it, or simply try one of the examples below. If you don't upload an image, you will receive an error.",
+#     stop_btn="Stop Generation",
+#     multimodal=True,
+#     textbox=chat_input,
+#     chatbot=chatbot,
+#     )
+
+# app.queue(api_open=False)
+# app.launch(show_api=False, share=True)