final_commit

Dockerfile

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+# Use the official Python 3.9 image
+FROM python:3.9
+
+# Set the working directory to /code
+WORKDIR /code
+
+# Copy the current directory contents into the container at /code
+COPY ./requirements.txt /code/requirements.txt
+
+# Install requirements.txt 
+RUN pip install --no-cache-dir --upgrade -r /code/requirements.txt
+
+# Set up a new user named "user" with user ID 1000
+RUN useradd -m -u 1000 user
+# Switch to the "user" user
+USER user
+# Set home to the user's home directory
+ENV HOME=/home/user \
+	PATH=/home/user/.local/bin:$PATH
+
+# Set the working directory to the user's home directory
+WORKDIR $HOME/app
+
+# Copy the current directory contents into the container at $HOME/app setting the owner to the user
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "mai:app", "--host", "0.0.0.0", "--port", "7860"]

main.py

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+# Import necessary libraries
+from flask import Flask, render_template, request, jsonify
+from PIL import Image
+from peft import PeftModel
+from PIL import Image
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration, BitsAndBytesConfig
+from deep_translator import GoogleTranslator
+from transformers import M2M100ForConditionalGeneration, M2M100Tokenizer
+from fastapi import FastAPI
+import warnings 
+# from flask import Flask
+
+# from flask_ngrok import run_with_ngrok
+# app = Flask(__name__)
+# run_with_ngrok(app)
+app = FastAPI()   
+    
+warnings.filterwarnings('ignore') 
+
+# 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")
+# model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+# tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
+
+
+# model_id = r"C:\Users\prave\OneDrive\Desktop\MLOPS\Mlops_2\huggingface_model"
+# quantization_config = BitsAndBytesConfig(
+#     load_in_4bit=True,
+# )
+# base_model = LlavaForConditionalGeneration.from_pretrained(model_id)
+
+# processor = AutoProcessor.from_pretrained(r"C:\Users\prave\OneDrive\Desktop\MLOPS\Mlops_2\huggingface_processor")
+
+# Load the PEFT Lora model (adapter)
+# peft_lora_adapter_path = r"C:\Users\prave\OneDrive\Desktop\MLOPS\Mlops_2\huggingface_adapter"
+
+# Merge the adapters into the base model
+# model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+# tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
+      
+
+def inference(image_prompt, image):
+    prompt = f"USER: <image>\n{image_prompt} ASSISTANT:"
+    inputs = processor(text=prompt, images=image, return_tensors="pt")
+    generate_ids = base_model.generate(**inputs, max_new_tokens=1024)
+    decoded_response = processor.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+
+#     prompt = "USER: <image>\nWhat's the content of the image? ASSISTANT:"
+    # url = "https://www.ilankelman.org/stopsigns/australia.jpg"
+#     url = "/kaggle/input/images/images/1921.428_web.jpg"
+    # image = Image.open(url)
+    # image = Image.open(requests.get(url, stream=True).raw)
+    # processor = AutoProcessor.from_pretrained("llava-hf/llava-1.5-7b-hf")
+    # ... process the image and create inputs ...
+#     print("Generated response:", decoded_response)
+    return decoded_response
+
+def deep_translator_bn_en(input_sentence):
+  english_translation = GoogleTranslator(source="bn", target="en").translate(input_sentence)
+  return english_translation
+
+def deep_translator_en_bn(input_sentence):
+  bengali_translation = GoogleTranslator(source="en", target="bn").translate(input_sentence)
+  return bengali_translation
+
+def google_response(image, input_sentence):
+  image_prompt = deep_translator_bn_en(input_sentence)
+  response = inference(image_prompt, image)
+  assistant_index = response.find("ASSISTANT:")
+  extracted_string = response[assistant_index + len("ASSISTANT:"):].strip()
+  output = deep_translator_en_bn(extracted_string)
+  # print("বটী: ", output)
+
+#   url = input("ইমেজ url লিখুন: ")
+#   input_sentence = input("ছবি সম্পর্কে আপনার প্রশ্ন লিখুন: ")
+  return output
+
+
+def facebook_bn_en(input_sentence):
+
+  # Translate Bengali to English
+  tokenizer.src_lang = "bn"
+  encoded_bn = tokenizer(input_sentence, return_tensors="pt")
+  generated_tokens = model.generate(**encoded_bn, forced_bos_token_id=tokenizer.get_lang_id("en"))
+  translated_text_en = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+  return translated_text_en
+# print("Translated English:", translated_text_en)
+
+def facebook_en_bn(input_sentence):
+  # Translate English to Bengali
+#   model = M2M100ForConditionalGeneration.from_pretrained("facebook/m2m100_418M")
+#   tokenizer = M2M100Tokenizer.from_pretrained("facebook/m2m100_418M")
+  tokenizer.src_lang = "en"
+  encoded_en = tokenizer(input_sentence, return_tensors="pt")
+  generated_tokens = model.generate(**encoded_en, forced_bos_token_id=tokenizer.get_lang_id("bn"))
+  translated_text_bn = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
+  return translated_text_bn
+
+def facebook_response(url, input_sentence):
+  url = input("ইমেজ url লিখুন: ")
+  input_sentence = input("ছবি সম্পর্কে আপনার প্রশ্ন লিখুন: ")
+  image_prompt = facebook_bn_en(input_sentence)
+  response = inference(image_prompt, url)
+  assistant_index = response.find("ASSISTANT:")
+  extracted_string = response[assistant_index + len("ASSISTANT:"):].strip()
+  output = facebook_en_bn(extracted_string)
+  print("বটী: ", output)
+  return output
+
+
+image_cache = {}
+@app.post('/upload/')
+def upload_file():
+    try:
+        file = request.files['file']
+        if file.filename.endswith('.jpg'):
+            image = Image.open(file.stream)
+            # Store the image in cache (replace with a more suitable storage approach)
+            image_cache['image'] = image
+            # print("Processing complete. Image stored in cache.")
+            return jsonify({'status': 'success'})
+        else:
+            return jsonify({'status': 'error', 'message': 'Uploaded file is not a jpg image.'})
+    except Exception as e:
+        # print(f"Error during file upload: {e}")
+        return jsonify({'status': 'error', 'message': str(e)})
+
+
+@app.get("/")
+def home():
+    return render_template("index.html")
+
+@app.get("/get/")
+def get_bot_response():
+    try:
+      if 'image' in image_cache:
+          image = image_cache['image']
+          # print(image)
+          query = request.args.get('msg')
+          output = query
+          # output = google_response(image, query)
+          return output
+      else:
+          return "Please upload an image to continue"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+
+# Run the Flask app
+# if __name__ == "__main__":
+    app.run(debug = True)
+
+
+# from pymongo import MongoClient
+
+# # Connect to MongoDB
+# mongodb_client = MongoClient('mongodb://localhost:27017/')
+# database_name = 'your_database'
+# collection_name = 'file_store'
+
+# db = mongodb_client[database_name]
+# collection = db[collection_name]
+
+# # Store documents with unique ID and their chunks
+# for i, doc in enumerate(documents):
+#     doc_id = f'doc_{i}'  # Create a unique ID for each document
+#     collection.insert_one({'_id': doc_id, 'document': doc})
+
+# # Check if index exists, if not create a new one
+# if 'index' not in collection.list_indexes():
+#     index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)
+#     collection.insert_one({'_id': 'index', 'index': index})
+# else:
+#     index = collection.find_one({'_id': 'index'})['index']
+
+# # Retrieve documents
+# retrieved_text_chunks = index.as_retriever().retrieve(question)

static/script.js

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+$(document).ready(function(){
+    $('#uploadForm').on('submit', function(e){
+        e.preventDefault();
+        $('#uploadStatus').html('<p>Status: Processing...</p>');
+        $.ajax({
+            url: '/upload',
+            type: 'POST',
+            data: new FormData(this),
+            contentType: false,
+            cache: false,
+            processData:false,
+            success: function(response){
+                $('#uploadStatus').html('<p>Status: '+response.status+'</p>');
+            }
+        });
+    });
+});
+
+
+(function () {
+    var Message;
+    Message = function (arg) {
+        this.text = arg.text, this.message_side = arg.message_side;
+        this.draw = function (_this) {
+            return function () {
+                var $message;
+                $message = $($('.message_template').clone().html());
+                $message.addClass(_this.message_side === 'user' ? 'user-message' : 'bot-message')
+                        .find('.text').html(_this.text);
+                $('.messages').append($message);
+                return setTimeout(function () {
+                    return $message.addClass('appeared');
+                }, 0);
+            };
+        }(this);
+        return this;
+    };
+
+    $(function () {
+        var getMessageText, sendMessage;
+        getMessageText = function () {
+            var $message_input;
+            $message_input = $('.message_input');
+            return $message_input.val();
+        };
+
+        sendMessage = function (text) {
+            var $messages, message;
+            if (text.trim() === '') {
+                return;
+            }
+            $('.message_input').val('');
+            $messages = $('.messages');
+
+            // Set message_side based on whether the message is from the user or chatbot
+            var userMessageSide = 'user';
+            var botMessageSide = 'bot';
+
+            // Draw user message with user-message class
+            message = new Message({
+                text: text,
+                message_side: userMessageSide
+            });
+            message.draw();
+
+            // Call getResponse() to get the chatbot's response
+            $.get("/get", { msg: text }).done(function(data) {
+                // Draw bot message with bot-message class
+                var botMessage = new Message({
+                    text: data,
+                    message_side: botMessageSide
+                });
+                botMessage.draw();
+                $messages.animate({ scrollTop: $messages.prop('scrollHeight') }, 300);
+            });
+
+            return $messages.animate({ scrollTop: $messages.prop('scrollHeight') }, 300);
+        };
+
+        $('.send_message').click(function (e) {
+            return sendMessage(getMessageText());
+        });
+        $('.message_input').keyup(function (e) {
+            if (e.which === 13) {
+                return sendMessage(getMessageText());
+            }
+        });
+
+        // Add initial bot message
+        var initialBotMessage = new Message({
+            text: 'How can I help you?',
+            message_side: 'bot'
+        });
+        initialBotMessage.draw();
+    });
+})();

static/styles.css

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+body {
+    margin: 0;
+    font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif;
+    background-color: #f8f9fa;
+}
+
+.container {
+    background-color: #ff0000;
+    padding: 10px;
+    text-align: center;
+    color: white;
+}
+
+
+.chat_window {
+    position: absolute;
+    width: 80%;
+    max-width: 800px;
+    border-radius: 10px;
+    background-color: #fff;
+    left: 50%;
+    top: 50%;
+    transform: translateX(-50%) translateY(-50%);
+    box-shadow: 0 10px 20px rgba(0, 0, 0, 0.1);
+    margin-top: 20px;
+}
+
+.top_menu {
+    background-color: #007bff;
+    width: 100%;
+    padding: 15px;
+    box-shadow: 0 1px 30px rgba(0, 0, 0, 0.1);
+}
+
+.top_menu .title {
+    text-align: center;
+    color: white;
+    font-size: 24px;
+    margin: 0;
+}
+
+.messages {
+    list-style: none;
+    padding: 20px 10px 0 10px;
+    margin: 0;
+    height: 347px;
+    overflow-y: scroll;
+}
+
+.messages::-webkit-scrollbar {
+    width: 8px;
+}
+
+.messages::-webkit-scrollbar-thumb {
+    background-color: #007bff;
+    border-radius: 10px;
+}
+
+.bottom_wrapper {
+    width: 100%;
+    background-color: #f8f9fa;  
+    padding: 20px 20px;
+    display: flex;
+    flex-direction: column;
+    align-items: stretch; 
+}
+
+.message_input_wrapper {
+    display: inline-block;
+    height: 50px;
+    border-radius: 25px;
+    border: 1px solid #bcbdc0;
+    width: calc(100% - 20px); 
+    position: relative;
+    padding: 0 20px;
+    margin-bottom: 10px;
+}
+.send_message {
+    width: 100%; 
+    height: 50px;
+    border-radius: 50px;
+    background-color: #007bff;
+    border: 2px solid #007bff;
+    color: #fff;
+    cursor: pointer;
+    transition: all 0.2s linear;
+    text-align: center;
+    margin-bottom: 10px;
+}
+
+
+.message_input {
+    border: none;
+    height: 100%;
+    box-sizing: border-box;
+    width: calc(100% - 40px);
+    position: absolute;
+    outline-width: 0;
+    color: gray;
+}
+
+
+.send_message:hover {
+    color: #007bff;
+    background-color: #fff;
+}
+
+.send_message .text {
+    font-size: 18px;
+    font-weight: 300;
+    display: inline-block;
+    line-height: 48px;
+}
+
+
+.message_template {
+    text-align: center;
+}
+
+
+.text_wrapper {
+    display: inline-block;
+    text-align: left;
+}
+
+.user-message {
+    background-color: #007bff;
+    color: #fff;
+    border-radius: 5px;
+    padding: 10px;
+    margin-bottom: 10px;
+    text-align: left;
+    float: right;
+    clear: both; 
+}
+
+.bot-message {
+    background-color: #28a745;
+    color: #fff;
+    border-radius: 5px;
+    padding: 10px;
+    margin-bottom: 10px;
+    text-align: left;
+    float: left;
+    clear: both; 
+}
+
+.upload_wrapper {
+    display: flex;
+    justify-content: space-between;
+    width: 100%;
+    margin-top: 10px;
+}
+
+#fileUpload {
+    width: calc(50% - 10px); 
+    margin-right: 20px;
+}
+
+#uploadBtn {
+    width: calc(50% - 10px); 
+    height: 50px;
+    border-radius: 50px;
+    background-color: #007bff;
+    border: 2px solid #007bff;
+    color: #fff;
+    cursor: pointer;
+    transition: all 0.2s linear;
+    text-align: center;
+}
+
+

templates/index.html

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+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <link rel="stylesheet" href="https://maxcdn.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css">
+    <link rel="stylesheet" href="static/styles.css"> <!-- Link to the new CSS file -->
+    <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.2.1/jquery.min.js"></script>
+</head>
+<body>
+    <div class="container">
+        <h1>Welcome to the chat world</h1>
+    </div>
+
+    <div class="chat_window">
+        <div class="top_menu">
+            <div class="title">Chat With Your Document</div>
+        </div>
+        <ul class="messages"></ul>
+
+        <div class="bottom_wrapper clearfix">
+            <div class="message_input_wrapper">
+                <input class="message_input" placeholder="Type your message here..." />
+            </div>
+            <div class="send_message">
+                <div class="icon"></div>
+                <div class="text">Send</div>
+            </div>
+            <form id="uploadForm" enctype="multipart/form-data" class="upload_wrapper">
+                <input type="file" name="file" id="fileInput" accept=".jpg">
+                <button type="submit">Upload</button>
+            </form>            
+            <div id="uploadStatus"></div>
+        </div>
+    </div>
+    <div class="message_template">
+        
+        <div class="text_wrapper">
+        <div class="text"></div>
+        </div>
+        
+    </div> 
+
+    <script src="static/script.js"></script>
+</body>
+</html>