Update app.py

app.py

@@ -12,30 +12,12 @@ import base64
 # Load environment variables
 load_dotenv()
 
-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
-]
+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",
+}
 
 embed_models = [
     "BAAI/bge-small-en-v1.5",  # 33.4M
@@ -45,49 +27,48 @@ embed_models = [
 ]
 
 # 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
+selected_llm_model_name = list(llm_models.keys())[0]  # Default to the first model in the dictionary
+
 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
-    '.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)
+    '.pdf': parser,
+    '.docx': parser,
+    '.txt': parser,
+    '.csv': parser,
+    '.xlsx': parser,
+    '.pptx': parser,
+    '.html': parser,
+    '.jpg': parser,
+    '.jpeg': parser,
+    '.png': parser,
+    '.webp': parser,
+    '.svg': parser,
+
+
+
+
+
+
+
+
+
+
+
+
 }
 
 
 # File processing function
 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 for {file_path}")
+
         filename = os.path.basename(file_path)
         return f"Ready to give response on {filename}"
     except Exception as e:
@@ -97,81 +78,81 @@ def load_files(file_path: str, embed_model_name: str):
 # 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}"
+    selected_llm_model_name = next(key for key, value in llm_models.items() if value == selected_model)
 
 
-# Respond function that uses the globally set selected model
+
+# Respond function
+
 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
+            contextWindow=8192,
+            maxTokens=1024,
+            temperature=0.3,
+            topP=0.9,
+            frequencyPenalty=0.5,
+            presencePenalty=0.5,
             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=gr.themes.Soft(font=[gr.themes.GoogleFont("Roboto Mono")]), css='footer {visibility: hidden}') as demo:
-    gr.Markdown("# DocBot📄🤖")
+with gr.Blocks(theme='Hev832/Applio', css='footer {visibility: hidden}') as demo:
+    gr.Markdown("")
     with gr.Tabs():
-        with gr.TabItem("Intro"):
+        with gr.TabItem("Introduction"):
             gr.Markdown(md.description)
 
-        with gr.TabItem("DocBot"):
-            with gr.Accordion("=== IMPORTANT: READ ME FIRST ===", open=False):
+        with gr.TabItem("Chatbot"):
+            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)
+                    file_input = gr.File(file_count="single", type='filepath', label="Upload document")
+                    embed_model_dropdown = gr.Dropdown(embed_models, label="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)
+                    llm_model_dropdown = gr.Dropdown(list(llm_models.values()), label="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)
+                        theme="soft",
+                        textbox=gr.Textbox(placeholder="Ask me any 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()