Update app.py

app.py

@@ -6,6 +6,7 @@ from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 import os
 from dotenv import load_dotenv
 import gradio as gr
+import markdowm as md
 import base64
 
 # Load environment variables
@@ -16,6 +17,32 @@ llm_models = [
     "meta-llama/Meta-Llama-3-8B-Instruct",
     "mistralai/Mistral-7B-Instruct-v0.2",
     "tiiuae/falcon-7b-instruct",
+    # "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B",
+    # "deepseek-ai/deepseek-vl2",  ## 54GB > 10GB
+    # "deepseek-ai/deepseek-vl2-small",  ## 32GB > 10GB
+    # "deepseek-ai/deepseek-vl2-tiny",  ## high response time
+    # "deepseek-ai/deepseek-llm-7b-chat",  ## 13GB > 10GB
+    # "deepseek-ai/deepseek-math-7b-instruct",  ## 13GB > 10GB
+    # "deepseek-ai/deepseek-coder-33b-instruct",  ## 66GB > 10GB
+    # "deepseek-ai/DeepSeek-R1-Zero",  ## 688GB > 10GB
+    # "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 = [
@@ -32,7 +59,6 @@ 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,  # PDF documents
@@ -40,105 +66,62 @@ file_extractor = {
     '.doc': parser,  # Older Microsoft Word documents
     '.txt': parser,  # Plain text files
     '.csv': parser,  # Comma-separated values files
-    '.xlsx': parser,  # Microsoft Excel files
-    '.pptx': parser,  # Microsoft PowerPoint files
-    '.html': parser,  # HTML 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
+    '.svg': parser,  # SVG files (vector format, may contain embedded text)
 }
 
-# Markdown content definitions
-description = """
-## Welcome to DocBot 📄🤖
-DocBot is an intelligent document analysis tool that can help you extract insights from various document formats including:
-- PDF documents
-- Word documents (.docx, .doc)
-- Text files
-- CSV files
-- Excel files
-- PowerPoint presentations
-- HTML files
-- Images with text (JPG, PNG, WebP, SVG)
-Simply upload your document, select your preferred embedding model and LLM, then start asking questions!
-"""
-
-guide = """
-### How to Use DocBot:
-1. **Upload Document**: Choose any supported file format
-2. **Select Embedding Model**: Choose from available embedding models (BAAI/bge-small-en-v1.5 is recommended for most cases)
-3. **Submit**: Click submit to process your document
-4. **Select LLM**: Choose your preferred language model
-5. **Ask Questions**: Start chatting with your document!
-### Tips:
-- Smaller embedding models (like bge-small-en-v1.5) are faster but may be less accurate
-- Larger models provide better understanding but take more time
-- Be specific in your questions for better results
-"""
-
-footer = """
-<div style="text-align: center; margin-top: 20px; padding: 20px; border-top: 1px solid #ddd;">
-    <p>Built with ❤️ using LlamaIndex and Gradio</p>
-    <div style="display: flex; justify-content: center; gap: 20px; margin-top: 10px;">
-        <a href="https://github.com" target="_blank">
-            <img src="data:image/png;base64,{0}" alt="GitHub" style="width: 24px; height: 24px;">
-        </a>
-        <a href="https://linkedin.com" target="_blank">
-            <img src="data:image/png;base64,{1}" alt="LinkedIn" style="width: 24px; height: 24px;">
-        </a>
-        <a href="https://your-website.com" target="_blank">
-            <img src="data:image/png;base64,{2}" alt="Website" style="width: 24px; height: 24px;">
-        </a>
-    </div>
-</div>
-"""
 
 # File processing function
 def load_files(file_path: str, embed_model_name: str):
     try:
-        if not file_path:
-            return "Please select a file first."
-        
-        if not embed_model_name:
-            return "Please select an embedding model."
-            
         global vector_index
         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 answer questions about: {filename}"
+        return f"Ready to give response on {filename}"
     except Exception as e:
-        return f"❌ An error occurred: {str(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
-    if selected_model:
-        selected_llm_model_name = selected_model
-    return f"LLM set to: {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 that uses the globally set selected model
 def respond(message, history):
     try:
-        if not vector_index:
-            return "Please upload and process a document first."
-            
-        if not message.strip():
-            return "Please enter a question."
-            
         # 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")
         )
 
@@ -147,163 +130,56 @@ def respond(message, history):
         bot_message = query_engine.query(message)
 
         print(f"\n{datetime.now()}:{selected_llm_model_name}:: {message} --> {str(bot_message)}\n")
-        return f"**{selected_llm_model_name}:**\n\n{str(bot_message)}"
+        return f"{selected_llm_model_name}:\n{str(bot_message)}"
     except Exception as e:
-        return f"❌ An error occurred: {str(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_safe(image_path):
-    """Safely encode image, return empty string if file doesn't exist"""
-    try:
-        if os.path.exists(image_path):
-            with open(image_path, "rb") as image_file:
-                return base64.b64encode(image_file.read()).decode('utf-8')
-    except Exception:
-        pass
-    return ""
-
-# Clear function for file processing components
-def clear_file_components():
-    return None, embed_models[0], ""
+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 (with fallback for missing images)
-github_logo_encoded = encode_image_safe("Images/github-logo.png")
-linkedin_logo_encoded = encode_image_safe("Images/linkedin-logo.png")
-website_logo_encoded = encode_image_safe("Images/ai-logo.png")
+# 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=gr.themes.Soft(),
-    css='footer {visibility: hidden}',
-    title="DocBot - Document Analysis Assistant"
-) as demo:
-    
-    gr.Markdown("# DocBot 📄🤖")
-    gr.Markdown("*Intelligent Document Analysis Assistant*")
-    
+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"):
-            gr.Markdown(description)
+        with gr.TabItem("Intro"):
+            gr.Markdown(md.description)
 
-        with gr.TabItem("🤖 DocBot"):
-            with gr.Accordion("📋 Quick Start Guide", open=False):
-                gr.Markdown(guide)
-                
+        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):
-                    with gr.Group():
-                        gr.Markdown("### Document Processing")
-                        file_input = gr.File(
-                            file_count="single", 
-                            type='filepath', 
-                            label="Step 1: Upload Document",
-                            file_types=['.pdf', '.docx', '.doc', '.txt', '.csv', '.xlsx', '.pptx', '.html', '.jpg', '.jpeg', '.png', '.webp', '.svg']
-                        )
-                        
-                        embed_model_dropdown = gr.Dropdown(
-                            choices=embed_models, 
-                            label="Step 2: Select Embedding Model", 
-                            interactive=True,
-                            value=embed_models[0]
-                        )
-                        
-                        with gr.Row():
-                            btn = gr.Button("🚀 Process Document", variant='primary', size="lg")
-                            clear_btn = gr.Button("🗑️ Clear", size="lg")
-                        
-                        output = gr.Textbox(
-                            label='Processing Status', 
-                            interactive=False,
-                            placeholder="Upload a document and click 'Process Document' to begin..."
-                        )
-                    
-                    with gr.Group():
-                        gr.Markdown("### Model Selection")
-                        llm_model_dropdown = gr.Dropdown(
-                            choices=llm_models, 
-                            label="Step 3: Select Language Model", 
-                            interactive=True,
-                            value=llm_models[0]
-                        )
-                        llm_status = gr.Textbox(
-                            label="Selected Model",
-                            interactive=False,
-                            value=f"LLM set to: {llm_models[0]}"
-                        )
-                
-                with gr.Column(scale=2):
-                    gr.Markdown("### Chat with Your Document")
-                    chatbot = gr.Chatbot(
-                        height=600,
-                        placeholder="Process a document first, then start asking questions!",
-                        show_label=False
-                    )
-                    
-                    msg = gr.Textbox(
-                        placeholder="Step 4: Ask questions about your document...",
-                        container=False,
-                        scale=7
-                    )
-                    
+                    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():
-                        submit_btn = gr.Button("Send", variant="primary")
-                        clear_chat_btn = gr.Button("Clear Chat")
-
-    # Add footer if images exist
-    if any([github_logo_encoded, linkedin_logo_encoded, website_logo_encoded]):
-        gr.HTML(footer.format(github_logo_encoded, linkedin_logo_encoded, website_logo_encoded))
-    
-    # Set up event handlers
-    def chat_respond(message, history):
-        if not message.strip():
-            return history, ""
-        
-        response = respond(message, history)
-        history.append([message, response])
-        return history, ""
-    
-    def clear_chat():
-        return [], ""
-    
-    # Event bindings
-    llm_model_dropdown.change(
-        fn=set_llm_model, 
-        inputs=[llm_model_dropdown], 
-        outputs=[llm_status]
-    )
-    
-    btn.click(
-        fn=load_files, 
-        inputs=[file_input, embed_model_dropdown], 
-        outputs=[output]
-    )
-    
-    submit_btn.click(
-        fn=chat_respond,
-        inputs=[msg, chatbot],
-        outputs=[chatbot, msg]
-    )
-    
-    msg.submit(
-        fn=chat_respond,
-        inputs=[msg, chatbot], 
-        outputs=[chatbot, msg]
-    )
-    
-    clear_btn.click(
-        fn=clear_file_components,
-        outputs=[file_input, embed_model_dropdown, output]
-    )
-    
-    clear_chat_btn.click(
-        fn=clear_chat,
-        outputs=[chatbot, msg]
-    )
+                        btn = gr.Button("Submit", variant='primary')
+                        clear = gr.ClearButton()
+                    output = gr.Text(label='Vector Index')
+                    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",
+                        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
+# Launch the demo with a public link option
 if __name__ == "__main__":
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+    demo.launch(share=True)  # Correct