Update app.py

app.py

@@ -6,73 +6,180 @@ 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
 load_dotenv()
 
-# Initialize the LLM and parser
-llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    token=os.getenv("TOKEN")
-)
+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",
+    # "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 = [
+    "BAAI/bge-small-en-v1.5",  # 33.4M
+    "NeuML/pubmedbert-base-embeddings",
+    "BAAI/llm-embedder", # 109M
+    "BAAI/bge-large-en" # 335M
+]
+
+# Global variable for selected model
+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')
-file_extractor = {'.pdf': parser, '.docx': parser, '.doc': parser}
+# Define file extractor with various common extensions
+file_extractor = {
+    '.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)
 
-# Embedding model and index initialization (to be populated by uploaded files)
-embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+    # 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)
+}
 
-# Global variable to store documents loaded from user-uploaded files
-vector_index = None
 
 # File processing function
-def load_files(file_path: str):
+def load_files(file_path: str, embed_model_name: str):
     try:
         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 {file_path}")
+        print(f"Parsing done for {file_path}")
         filename = os.path.basename(file_path)
-        return f"Ready to give response on give {filename}"
+        return f"Ready to give response on {filename}"
     except Exception as e:
-        return f"An error occurred {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 = 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:
+        # Initialize the LLM with the selected model
+        llm = HuggingFaceInferenceAPI(
+            model_name=selected_llm_model_name,
+            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)
-        # output = ""
-        # for chr in bot_message:
-        #     output += chr
-        #     yield output
-        print(f"{datetime.now()}::message=>{str(bot_message)}")
-        return 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 e == "'NoneType' object has no attribute 'as_query_engine'":
-            return "upload file"
-        return f"an error occurred {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() as main:
-    with gr.Row():
-        with gr.Column(scale=1):
-            file_input = gr.File(file_count="single", type='filepath')
-            with gr.Column():
-                clear = gr.ClearButton()
-                btn = gr.Button("Submit", variant='primary')
-            output = gr.Text(label='Vector Index')
-        with gr.Column(scale=2):
-             gr.ChatInterface(fn=respond,
-                              chatbot=gr.Chatbot(height=500),
-                              textbox=gr.Textbox(placeholder="Ask me a yes or no question", container=False, scale=7),
-                              examples=["summarize the document"]
-                             )
-
-    # Action on button click to process file and load into index
-    btn.click(fn=load_files, inputs=file_input, outputs=output)
-    clear.click(lambda: [None]*2, outputs=[file_input, output])
-
-
-# Launch the demo with public link option
+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("Intro"):
+            gr.Markdown(md.description)
+
+        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="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(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 with a public link option
 if __name__ == "__main__":
-    main.launch(share=True)
+    demo.launch(share=True)