Update app.py

app.py

@@ -1,64 +1,168 @@
+# main.py
+import os
 import gradio as gr
-from huggingface_hub import InferenceClient
+import tempfile
+from dotenv import load_dotenv
+from modules.document_processor import process_document
+from modules.model_handler import get_model_response, get_qa_response
+from modules.config import MODEL_MAPPING, DEFAULT_PARAMETERS
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Load environment variables
+load_dotenv()
 
+def setup_api_key(api_key=None):
+    """Set up the HuggingFace API key from input or environment variables."""
+    if api_key and api_key.startswith('hf_'):
+        os.environ['HF_TOKEN'] = api_key
+        return True, "API key set successfully! ✅"
+    elif os.getenv('HF_TOKEN') and os.getenv('HF_TOKEN').startswith('hf_'):
+        return True, "API key already available! ✅"
+    else:
+        return False, "Please enter a valid HuggingFace API key. ⚠️"
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
+def create_chat_interface():
+    """Create the main chat interface for the application."""
+    with gr.Blocks(title="💬 Small Language Models - POC") as demo:
+        # Application header
+        gr.Markdown("# 💬 Small Language Models - POC")
+        gr.Markdown("This chatbot uses various Language Models such as Llama 3.2, Gemma 2, Gemma 3, Phi 3.5, DeepSeek-V3, and DeepSeek-R1.")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                # Sidebar configuration
+                with gr.Group():
+                    api_key_input = gr.Textbox(
+                        label="HuggingFace API Token", 
+                        placeholder="Enter your HF API token (hf_...)", 
+                        type="password"
+                    )
+                    api_key_status = gr.Markdown("Please enter your API key.")
+                    api_key_button = gr.Button("Set API Key")
+                
+                with gr.Group():
+                    gr.Markdown("## Models and Parameters")
+                    model_dropdown = gr.Dropdown(
+                        choices=list(MODEL_MAPPING.keys()),
+                        label="Select Model",
+                        value=list(MODEL_MAPPING.keys())[0]
+                    )
+                    
+                    temperature_slider = gr.Slider(
+                        label="Temperature", 
+                        minimum=0.01, 
+                        maximum=1.0, 
+                        value=DEFAULT_PARAMETERS["temperature"],
+                        step=0.01
+                    )
+                    
+                    top_p_slider = gr.Slider(
+                        label="Top P", 
+                        minimum=0.01, 
+                        maximum=1.0, 
+                        value=DEFAULT_PARAMETERS["top_p"],
+                        step=0.01
+                    )
+                    
+                    max_length_slider = gr.Slider(
+                        label="Max Length", 
+                        minimum=20, 
+                        maximum=2040, 
+                        value=DEFAULT_PARAMETERS["max_length"],
+                        step=5
+                    )
+                    
+                    clear_button = gr.Button("Clear Chat History")
+                
+                with gr.Group():
+                    gr.Markdown("## Document Upload")
+                    file_upload = gr.File(
+                        label="Upload Document (PDF, DOCX, TXT)",
+                        file_types=["pdf", "docx", "txt"]
+                    )
+                    upload_status = gr.Markdown("")
 
+            with gr.Column(scale=2):
+                # Chat interface
+                chatbot = gr.Chatbot(
+                    label="Conversation",
+                    height=500,
+                    bubble_full_width=False
+                )
+                msg = gr.Textbox(
+                    label="Enter your message",
+                    placeholder="Type your message here...",
+                    show_label=False
+                )
+                
+                # State variables to track conversation and document processing
+                conversation_state = gr.State([])
+                document_store = gr.State(None)
+                api_key_state = gr.State(False)
+        
+        # Set up event handlers
+        api_key_button.click(
+            setup_api_key, 
+            inputs=[api_key_input],
+            outputs=[api_key_state, api_key_status]
+        )
+        
+        file_upload.upload(
+            process_document,
+            inputs=[file_upload, api_key_state],
+            outputs=[document_store, upload_status]
+        )
+        
+        # Function to handle chat messages
+        def respond(message, conversation, model_name, temp, top_p, max_len, doc_store, api_ready):
+            if not api_ready:
+                return conversation, conversation, "Please set a valid API key first. ⚠️"
+            
+            if not message.strip():
+                return conversation, conversation, upload_status.value
+            
+            # Update conversation with user message
+            conversation.append([message, None])
+            yield conversation, conversation, upload_status.value
+            
+            # Generate response based on whether document is uploaded
+            if doc_store is not None:
+                response = get_qa_response(
+                    message, 
+                    model_name, 
+                    doc_store, 
+                    {"temperature": temp, "top_p": top_p, "max_length": max_len}
+                )
+            else:
+                response = get_model_response(
+                    message, 
+                    conversation, 
+                    model_name, 
+                    {"temperature": temp, "top_p": top_p, "max_length": max_len}
+                )
+            
+            # Update conversation with assistant response
+            conversation[-1][1] = response
+            yield conversation, conversation, upload_status.value
+        
+        # Function to clear chat history
+        def clear_history():
+            return [], gr.update(value="Chat history cleared.")
+        
+        # Connect events
+        msg.submit(
+            respond,
+            [msg, conversation_state, model_dropdown, temperature_slider, top_p_slider, max_length_slider, document_store, api_key_state],
+            [chatbot, conversation_state, upload_status]
+        )
+        
+        clear_button.click(
+            clear_history,
+            outputs=[conversation_state, upload_status]
+        )
+        
+    return demo
 
 if __name__ == "__main__":
-    demo.launch()
+    # Create and launch the application
+    app = create_chat_interface()
+    app.launch(share=False)