Add application1 file3

app.py

@@ -9,103 +9,103 @@ 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()
+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
-# 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)
+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)