Copied files from source repository

README.md

@@ -1,13 +1,13 @@
 ---
-title: QA Bot
+title: Document QA Bot
 emoji: 📚
 colorFrom: pink
 colorTo: purple
 sdk: gradio
-sdk_version: 4.44.1
+sdk_version: 4.44.0
 app_file: app.py
 pinned: false
-license: apache-2.0
+license: mit
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -12,12 +12,30 @@ import base64
 # Load environment variables
 load_dotenv()
 
-llm_models = {
-    "tiiuae/falcon-7b-instruct": "HundAI-7B-S",
-    "mistralai/Mixtral-8x7B-Instruct-v0.1": "Mixtral-8x7B",
-    "meta-llama/Meta-Llama-3-8B-Instruct": "Meta-Llama-8B",
-    "mistralai/Mistral-7B-Instruct-v0.2": "Mistral-7B",
-}
+llm_models = [
+    "mistralai/Mixtral-8x7B-Instruct-v0.1",
+    "meta-llama/Meta-Llama-3-8B-Instruct",
+    "mistralai/Mistral-7B-Instruct-v0.2",
+    "tiiuae/falcon-7b-instruct",
+    # "mistralai/Mixtral-8x22B-Instruct-v0.1",    ## 281GB>10GB
+    # "NousResearch/Yarn-Mistral-7b-64k",  ## 14GB>10GB
+    # "impira/layoutlm-document-qa",  ## ERR
+    # "Qwen/Qwen1.5-7B",      ## 15GB
+    # "Qwen/Qwen2.5-3B",      ## high response time
+    # "google/gemma-2-2b-jpn-it",   ## high response time
+    # "impira/layoutlm-invoices",   ## bad req
+    # "google/pix2struct-docvqa-large",  ## bad req
+    # "google/gemma-7b-it", ## 17GB > 10GB
+    # "google/gemma-2b-it",  ## high response time
+    # "HuggingFaceH4/zephyr-7b-beta",   ## high response time
+    # "HuggingFaceH4/zephyr-7b-gemma-v0.1",     ## bad req
+    # "microsoft/phi-2",    ## high response time
+    # "TinyLlama/TinyLlama-1.1B-Chat-v1.0",     ## high response time
+    # "mosaicml/mpt-7b-instruct",     ## 13GB>10GB
+    # "google/flan-t5-xxl" ## high respons time
+    # "NousResearch/Yarn-Mistral-7b-128k",  ## 14GB>10GB
+    # "Qwen/Qwen2.5-7B-Instruct",     ## 15GB>10GB
+]
 
 embed_models = [
     "BAAI/bge-small-en-v1.5",  # 33.4M
@@ -27,26 +45,41 @@ embed_models = [
 ]
 
 # Global variable for selected model
-selected_llm_model_name = list(llm_models.keys())[0]  # Default to the first model in the dictionary
+selected_llm_model_name = llm_models[0]  # Default to the first model in the list
+selected_embed_model_name = embed_models[0] # Default to the first model in the list
 vector_index = None
 
 # Initialize the parser
 parser = LlamaParse(api_key=os.getenv("LLAMA_INDEX_API"), result_type='markdown')
+# Define file extractor with various common extensions
 file_extractor = {
-    '.pdf': parser,
-    '.docx': parser,
-    '.txt': parser,
-    '.csv': parser,
-    '.xlsx': parser,
-    '.pptx': parser,
-    '.html': parser,
-    '.jpg': parser,
-    '.jpeg': parser,
-    '.png': parser,
-    '.webp': parser,
-    '.svg': parser,
+    '.pdf': parser,  # PDF documents
+    '.docx': parser,  # Microsoft Word documents
+    '.doc': parser,  # Older Microsoft Word documents
+    '.txt': parser,  # Plain text files
+    '.csv': parser,  # Comma-separated values files
+    '.xlsx': parser,  # Microsoft Excel files (requires additional processing for tables)
+    '.pptx': parser,  # Microsoft PowerPoint files (for slides)
+    '.html': parser,  # HTML files (web pages)
+    # '.rtf': parser,  # Rich Text Format files
+    # '.odt': parser,  # OpenDocument Text files
+    # '.epub': parser,  # ePub files (e-books)
+
+    # Image files for OCR processing
+    '.jpg': parser,  # JPEG images
+    '.jpeg': parser,  # JPEG images
+    '.png': parser,  # PNG images
+    # '.bmp': parser,  # Bitmap images
+    # '.tiff': parser,  # TIFF images
+    # '.tif': parser,  # TIFF images (alternative extension)
+    # '.gif': parser,  # GIF images (can contain text)
+
+    # Scanned documents in image formats
+    '.webp': parser,  # WebP images
+    '.svg': parser,  # SVG files (vector format, may contain embedded text)
 }
 
+
 # File processing function
 def load_files(file_path: str, embed_model_name: str):
     try:
@@ -54,67 +87,91 @@ def load_files(file_path: str, embed_model_name: str):
         document = SimpleDirectoryReader(input_files=[file_path], file_extractor=file_extractor).load_data()
         embed_model = HuggingFaceEmbedding(model_name=embed_model_name)
         vector_index = VectorStoreIndex.from_documents(document, embed_model=embed_model)
+        print(f"Parsing done for {file_path}")
         filename = os.path.basename(file_path)
         return f"Ready to give response on {filename}"
     except Exception as e:
         return f"An error occurred: {e}"
 
+
 # Function to handle the selected model from dropdown
 def set_llm_model(selected_model):
     global selected_llm_model_name
-    selected_llm_model_name = next(key for key, value in llm_models.items() if value == selected_model)
+    selected_llm_model_name = selected_model  # Update the global variable
+    # print(f"Model selected: {selected_model_name}")
+    # return f"Model set to: {selected_model_name}"
 
-# Respond function
+
+# Respond function that uses the globally set selected model
 def respond(message, history):
     try:
+        # Initialize the LLM with the selected model
         llm = HuggingFaceInferenceAPI(
             model_name=selected_llm_model_name,
-            contextWindow=8192,
-            maxTokens=1024,
-            temperature=0.3,
-            topP=0.9,
-            frequencyPenalty=0.5,
-            presencePenalty=0.5,
+            contextWindow=8192,  # Context window size (typically max length of the model)
+            maxTokens=1024,  # Tokens per response generation (512-1024 works well for detailed answers)
+            temperature=0.3,  # Lower temperature for more focused answers (0.2-0.4 for factual info)
+            topP=0.9,  # Top-p sampling to control diversity while retaining quality
+            frequencyPenalty=0.5,  # Slight penalty to avoid repetition
+            presencePenalty=0.5,  # Encourages exploration without digressing too much
             token=os.getenv("TOKEN")
         )
+
+        # Set up the query engine with the selected LLM
         query_engine = vector_index.as_query_engine(llm=llm)
         bot_message = query_engine.query(message)
-        return f"{llm_models[selected_llm_model_name]}:\n{str(bot_message)}"
+
+        print(f"\n{datetime.now()}:{selected_llm_model_name}:: {message} --> {str(bot_message)}\n")
+        return f"{selected_llm_model_name}:\n{str(bot_message)}"
     except Exception as e:
         if str(e) == "'NoneType' object has no attribute 'as_query_engine'":
             return "Please upload a file."
         return f"An error occurred: {e}"
 
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Encode the images
+github_logo_encoded = encode_image("Images/github-logo.png")
+linkedin_logo_encoded = encode_image("Images/linkedin-logo.png")
+website_logo_encoded = encode_image("Images/ai-logo.png")
+
 # UI Setup
-with gr.Blocks(theme='Hev832/Applio', css='footer {visibility: hidden}') as demo:
-    gr.Markdown("")
+with gr.Blocks(theme=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
+    gr.Markdown("# DocBot📄🤖")
     with gr.Tabs():
-        with gr.TabItem("Introduction"):
+        with gr.TabItem("Intro"):
             gr.Markdown(md.description)
 
-        with gr.TabItem("Chatbot"):
-            with gr.Accordion("IMPORTANT: READ ME FIRST", open=False):
+        with gr.TabItem("DocBot"):
+            with gr.Accordion("=== IMPORTANT: READ ME FIRST ===", open=False):
                 guid = gr.Markdown(md.guide)
             with gr.Row():
                 with gr.Column(scale=1):
-                    file_input = gr.File(file_count="single", type='filepath', label="Upload document")
-                    embed_model_dropdown = gr.Dropdown(embed_models, label="Select Embedding", interactive=True)
+                    file_input = gr.File(file_count="single", type='filepath', label="Step-1: Upload document")
+                    # gr.Markdown("Dont know what to select check out in Intro tab")
+                    embed_model_dropdown = gr.Dropdown(embed_models, label="Step-2: Select Embedding", interactive=True)
                     with gr.Row():
                         btn = gr.Button("Submit", variant='primary')
                         clear = gr.ClearButton()
                     output = gr.Text(label='Vector Index')
-                    llm_model_dropdown = gr.Dropdown(list(llm_models.values()), label="Select LLM", interactive=True)
+                    llm_model_dropdown = gr.Dropdown(llm_models, label="Step-3: Select LLM", interactive=True)
                 with gr.Column(scale=3):
                     gr.ChatInterface(
                         fn=respond,
                         chatbot=gr.Chatbot(height=500),
-                        theme="soft",
-                        textbox=gr.Textbox(placeholder="Ask me any questions on the uploaded document!", container=False)
+                        theme = "soft",
+                        show_progress='full',
+                        # cache_mode='lazy',
+                        textbox=gr.Textbox(placeholder="Step-4: Ask me questions on the uploaded document!", container=False)
                     )
-    
+    gr.HTML(md.footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))
+    # Set up Gradio interactions
     llm_model_dropdown.change(fn=set_llm_model, inputs=llm_model_dropdown)
     btn.click(fn=load_files, inputs=[file_input, embed_model_dropdown], outputs=output)
     clear.click(lambda: [None] * 3, outputs=[file_input, embed_model_dropdown, output])
 
+# Launch the demo with a public link option
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()

markdowm.py

@@ -1,10 +1,10 @@
 description = '''
-# 📄 **QueryVault Chatbots: A RAG-Based chatbots for Interactive Document Querying**
+# 📄 **Document QA Bot: A RAG-Based Application for Interactive Document Querying**
 
-Welcome to the HundAI QueryVault Chatbot, a sophisticated Retrieval-Augmented Generation (RAG) application that utilizes Large Language Models to answer questions based on documents you upload. This bot is designed to empower you with rapid, insightful responses, providing a choice of language models (LLMs) and embedding models that cater to various requirements, including performance, accuracy, and response time.
+Welcome to the Document QA Bot, a sophisticated Retrieval-Augmented Generation (RAG) application that utilizes **LlamaIndex** and **Hugging Face** models to answer questions based on documents you upload. This bot is designed to empower you with rapid, insightful responses, providing a choice of language models (LLMs) and embedding models that cater to various requirements, including performance, accuracy, and response time.
 
 ## ✨ **Application Overview**
-With QueryVault Chatbot, you can interactively query your document, receive contextual answers, and dynamically switch between LLMs as needed for optimal results. The bot supports various file formats, allowing you to upload and analyze different types of documents and even some image formats. 
+With Document QA Bot, you can interactively query your document, receive contextual answers, and dynamically switch between LLMs as needed for optimal results. The bot supports various file formats, allowing you to upload and analyze different types of documents and even some image formats. 
 
 ### **Key Features**
 - **Choice of Models:** Access a list of powerful LLMs and embedding models for optimal results.
@@ -14,10 +14,10 @@ With QueryVault Chatbot, you can interactively query your document, receive cont
 
 ---
 
-## 🚀 **Steps to Use the HundAI QueryVault Chatbot**
+## 🚀 **Steps to Use the Document QA Bot**
 
 1. **Upload Your File**  
-   Begin by uploading a document. Supported formats include .pdf, .docx, .txt, .csv, .xlsx, .pptx, .html, .jpg, .png, and more.
+   Begin by uploading a document. Supported formats include `.pdf`, `.docx`, `.txt`, `.csv`, `.xlsx`, `.pptx`, `.html`, `.jpg`, `.png`, and more.
    
 2. **Select Embedding Model**  
    Choose an embedding model to parse and index the document’s contents, then submit. Wait for the confirmation message that the document has been successfully indexed.
@@ -39,32 +39,32 @@ Upon uploading a document, the bot utilizes **LlamaParse** to parse its content.
 ## 🔍 **Available LLMs and Embedding Models**
 
 ### **Embedding Models** (For indexing document content)
-1. **BAAI/bge-large-en**  
+1. **`BAAI/bge-large-en`**  
    - **Size**: 335M parameters  
    - **Best For**: Complex, detailed embeddings; slower but yields high accuracy.
-2. **BAAI/bge-small-en-v1.5**  
+2. **`BAAI/bge-small-en-v1.5`**  
    - **Size**: 33.4M parameters  
    - **Best For**: Faster embeddings, ideal for lighter workloads and quick responses.
-3. **NeuML/pubmedbert-base-embeddings**  
+3. **`NeuML/pubmedbert-base-embeddings`**  
    - **Size**: 768-dimensional dense vector space  
    - **Best For**: Biomedical or medical-related text; highly specialized.
-4. **BAAI/llm-embedder**  
+4. **`BAAI/llm-embedder`**  
    - **Size**: 109M parameters  
    - **Best For**: Basic embeddings for straightforward use cases.
 
 ### **LLMs** (For generating answers)
-1. **Mixtral-8x7B-Instruct**  
+1. **`mistralai/Mixtral-8x7B-Instruct-v0.1`**  
    - **Size**: 46.7B parameters
    - **Purpose**: Demonstrates compelling performance with minimal fine-tuning. Suited for unmoderated or exploratory use.
-2. **Meta-Llama-3-8B-Instruct**  
+2. **`meta-llama/Meta-Llama-3-8B-Instruct`**  
    - **Size**: 8.03B parameters
    - **Purpose**: Optimized for dialogue, emphasizing safety and helpfulness. Excellent for structured, instructive responses.
-3. **Mistral-7B**  
+3. **`mistralai/Mistral-7B-Instruct-v0.2`**  
    - **Size**: 7.24B parameters
    - **Purpose**: Fine-tuned for effectiveness; lacks moderation, useful for quick demonstration purposes.
-4. **HundAI-7B-S**  
+4. **`tiiuae/falcon-7b-instruct`**  
    - **Size**: 7.22B parameters
-   - **Purpose**: Robust fine-tuned model for inference, leveraging large-scale data for highly contextual responses.
+   - **Purpose**: Robust open-source model for inference, leveraging large-scale data for highly contextual responses.
 
 ---
 
@@ -74,18 +74,18 @@ The choice of embedding models plays a crucial role in determining the speed and
 
 | **Scenario**                  | **Embedding Model**                  | **Strengths**                                      | **Trade-Offs**                       |
 |:-----------------------------:|:------------------------------------:|:--------------------------------------------------:|:------------------------------------:|
-| **Fastest Response**          | BAAI/bge-small-en-v1.5            | Speed-oriented, ideal for high-frequency querying  | May miss nuanced details             |
-| **High Accuracy for Large Texts** | BAAI/bge-large-en               | High accuracy, captures complex document structure | Slower response time                |
-| **Balanced General Purpose**  | BAAI/llm-embedder | Reliable, quick response, adaptable across topics | Moderate accuracy, general use case  |
-| **Biomedical & Specialized Text** | NeuML/pubmedbert-base-embeddings | Optimized for medical and scientific text          | Specialized, slightly slower         |
+| **Fastest Response**          | `BAAI/bge-small-en-v1.5`            | Speed-oriented, ideal for high-frequency querying  | May miss nuanced details             |
+| **High Accuracy for Large Texts** | `BAAI/bge-large-en`               | High accuracy, captures complex document structure | Slower response time                |
+| **Balanced General Purpose**  | `BAAI/llm-embedder` | Reliable, quick response, adaptable across topics | Moderate accuracy, general use case  |
+| **Biomedical & Specialized Text** | `NeuML/pubmedbert-base-embeddings` | Optimized for medical and scientific text          | Specialized, slightly slower         |
 
 ---
 
 ## 📂 **Supported File Formats**
 
 The bot supports a range of document formats, making it versatile for various data sources. Below are the currently supported formats:
-- **Documents**: .pdf, .docx, .doc, .txt, .csv, .xlsx, .pptx, .html
-- **Images**: .jpg, .jpeg, .png, .webp, .svg
+- **Documents**: `.pdf`, `.docx`, `.doc`, `.txt`, `.csv`, `.xlsx`, `.pptx`, `.html`
+- **Images**: `.jpg`, `.jpeg`, `.png`, `.webp`, `.svg`
 
 ---
 
@@ -106,7 +106,7 @@ The bot supports a range of document formats, making it versatile for various da
 ---
 
 ### 🌟 **Get Started Today and Experience Document-Centric Question Answering**  
-Whether you're a student, researcher, or professional, HundAI QueryVault Chatbot is your go-to tool for interactive, accurate document analysis. Upload your file, select your model, and dive into a seamless question-answering experience tailored to your document's unique content.
+Whether you're a student, researcher, or professional, the Document QA Bot is your go-to tool for interactive, accurate document analysis. Upload your file, select your model, and dive into a seamless question-answering experience tailored to your document's unique content.
 '''
 
 guide = '''
@@ -114,17 +114,42 @@ guide = '''
 
 | **Embedding Model**         | **Speed (Vector Index)** | **Advantages**                      | **Trade-Offs**                  |
 |-----------------------------|-------------------|-------------------------------------|---------------------------------|
-| BAAI/bge-small-en-v1.5    | **Fastest**       | Ideal for quick indexing            | May miss nuanced details        |
-| BAAI/llm-embedder         | **Fast**          | Balanced performance and detail     | Slightly less precise than large models |
-| BAAI/bge-large-en         | **Slow**          | Best overall precision and detail   | Slower due to complexity        |
+| `BAAI/bge-small-en-v1.5`    | **Fastest**       | Ideal for quick indexing            | May miss nuanced details        |
+| `BAAI/llm-embedder`         | **Fast**          | Balanced performance and detail     | Slightly less precise than large models |
+| `BAAI/bge-large-en`         | **Slow**          | Best overall precision and detail   | Slower due to complexity        |
     
 
 ### Language Models (LLMs) and Use Cases
 
 | **LLM**                             | **Best Use Case**                       |
 |------------------------------------|-----------------------------------------|
-| Mixtral-8x7B-Instruct-v0.1 | Works well for **both short and long answers** |
-| Meta-Llama-3-8B-Instruct  | Ideal for **long-length answers**         |
-| HundAI-7B-S           | Best suited for **short-length answers**  |
+| `mistralai/Mixtral-8x7B-Instruct-v0.1` | Works well for **both short and long answers** |
+| `meta-llama/Meta-Llama-3-8B-Instruct`  | Ideal for **long-length answers**         |
+| `tiiuae/falcon-7b-instruct`           | Best suited for **short-length answers**  |
 
-'''
+'''
+
+footer = """
+<div style="background-color: #1d2938; color: white; padding: 10px; width: 100%; bottom: 0; left: 0; display: flex; justify-content: space-between; align-items: center; padding: .2rem 35px; box-sizing: border-box; font-size: 16px;">
+    <div style="text-align: left;">
+        <p style="margin: 0;">&copy; 2024 </p>
+    </div>
+    <div style="text-align: center; flex-grow: 1;">
+        <p style="margin: 0;">      This website is made with ❤ by SARATH CHANDRA</p>
+    </div>
+    <div class="social-links" style="display: flex; gap: 20px; justify-content: flex-end; align-items: center;">
+        <a href="https://github.com/21bq1a4210" target="_blank" style="text-align: center;">
+            <img src="data:image/png;base64,{}" alt="GitHub" width="40" height="40" style="display: block; margin: 0 auto;">
+            <span style="font-size: 14px;">GitHub</span>
+        </a>
+        <a href="https://www.linkedin.com/in/sarath-chandra-bandreddi-07393b1aa/" target="_blank" style="text-align: center;">
+            <img src="data:image/png;base64,{}" alt="LinkedIn" width="40" height="40" style="display: block; margin: 0 auto;">
+            <span style="font-size: 14px;">LinkedIn</span>
+        </a>
+        <a href="https://21bq1a4210.github.io/MyPortfolio-/" target="_blank" style="text-align: center;">
+            <img src="data:image/png;base64,{}" alt="Portfolio" width="40" height="40" style="display: block; margin-right: 40px;">
+            <span style="font-size: 14px;">Portfolio</span>
+        </a>
+    </div>
+</div>
+"""