Application1

requirements.txt

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+accelerate==0.29.3
+beautifulsoup4==4.12.3
+bitsandbytes==0.42.0
+certifi==2024.2.2
+charset-normalizer==3.3.2
+deep-translator==1.11.4
+filelock==3.14.0
+fsspec==2024.3.1
+huggingface-hub==0.22.2
+idna==3.7
+Jinja2==3.1.3
+MarkupSafe==2.1.5
+mpmath==1.3.0
+networkx==3.2.1
+numpy==1.26.4
+packaging==24.0
+peft==0.10.0
+psutil==5.9.8
+PyYAML==6.0.1
+regex==2024.4.28
+requests==2.31.0
+safetensors==0.4.3
+scipy==1.13.0
+soupsieve==2.5
+sympy==1.12
+tokenizers==0.19.1
+torch==2.2.2
+tqdm==4.66.2
+transformers==4.40.1
+typing_extensions==4.11.0
+urllib3==2.2.1

try3.py

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+# -*- coding: utf-8 -*-
+"""Untitled1.ipynb
+
+Automatically generated by Colab.
+
+Original file is located at
+    https://colab.research.google.com/drive/1vA1O3q8yuzV8Hi3O8LhNuLGWS18yVEkb
+"""
+
+import streamlit as st
+import PIL.Image
+import base64
+import time
+import os
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration, BitsAndBytesConfig
+from peft import PeftModel
+from deep_translator import GoogleTranslator
+
+
+@st.cache_resource
+def load_model():
+    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")
+
+    return base_model, processor
+
+base_model, processor = load_model()
+
+# 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
+
+# Interface Code
+st.title('My_BoT')
+
+# Create a sidebar
+sidebar = st.sidebar
+sidebar.header('User Inputs')
+
+# Add a file uploader to the sidebar
+uploaded_file = sidebar.file_uploader("Upload an Image", type=['png', 'jpg', 'jpeg'])
+
+# Add a text input to the sidebar
+text_input = sidebar.text_input("Enter text and press enter")
+
+# Initialize session state for history if it doesn't exist
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
+# Check if text is entered and no image is uploaded
+if text_input and uploaded_file is None:
+    st.write("Please upload an image.")
+
+# Add a button to the sidebar
+submit_button = sidebar.button("Submit")
+
+# When the button is clicked, generate the response and display it
+if submit_button:
+    if uploaded_file is not None:
+        image = PIL.Image.open(uploaded_file)
+        st.session_state.history = llm_response(st.session_state.history, text_input, image)
+        for text, output in st.session_state.history:
+            st.write(f"User: {text}")
+            if output is not None:
+                st.write(f"Assistant: {output}")