Update app.py

app.py

@@ -1,3 +1,177 @@
 import gradio as gr
+import json
+from pathlib import Path
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-gr.load("models/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B").launch()
+# Default system prompt for the chat interface
+DEFAULT_SYSTEM_PROMPT = """You are DeepThink, a helpful and knowledgeable AI assistant. You aim to provide accurate, 
+informative, and engaging responses while maintaining a professional and friendly demeanor."""
+
+class ChatInterface:
+    """Main chat interface handler with memory and parameter management"""
+    
+    def __init__(self):
+        """Initialize the chat interface with default settings"""
+        self.model_name = "deepseek-ai/DeepSeek-R1-Distill-Qwen-7B"
+        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
+        self.model = AutoModelForCausalLM.from_pretrained(self.model_name)
+        self.chat_history = []
+        self.system_prompt = DEFAULT_SYSTEM_PROMPT
+        
+    def load_context_from_json(self, file_obj):
+        """Load additional context from a JSON file"""
+        if file_obj is None:
+            return "No file uploaded", self.system_prompt
+        
+        try:
+            content = json.load(file_obj)
+            if "system_prompt" in content:
+                self.system_prompt = content["system_prompt"]
+            return "Context loaded successfully!", self.system_prompt
+        except Exception as e:
+            return f"Error loading context: {str(e)}", self.system_prompt
+
+    def generate_response(self, message, temperature, max_length, top_p, presence_penalty, frequency_penalty):
+        """Generate AI response with given parameters"""
+        # Format the input with system prompt and chat history
+        conversation = f"System: {self.system_prompt}\n\n"
+        for msg in self.chat_history:
+            conversation += f"Human: {msg[0]}\nAssistant: {msg[1]}\n\n"
+        conversation += f"Human: {message}\nAssistant:"
+
+        # Generate response with specified parameters
+        inputs = self.tokenizer(conversation, return_tensors="pt")
+        outputs = self.model.generate(
+            inputs["input_ids"],
+            max_length=max_length,
+            temperature=temperature,
+            top_p=top_p,
+            presence_penalty=presence_penalty,
+            frequency_penalty=frequency_penalty,
+        )
+        response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract assistant's response and update chat history
+        response = response.split("Assistant:")[-1].strip()
+        self.chat_history.append((message, response))
+        
+        return response, self.format_chat_history()
+    
+    def format_chat_history(self):
+        """Format chat history for display"""
+        return [(f"User: {msg[0]}", f"Assistant: {msg[1]}") for msg in self.chat_history]
+    
+    def clear_history(self):
+        """Clear the chat history"""
+        self.chat_history = []
+        return self.format_chat_history()
+
+# Initialize the chat interface
+chat_interface = ChatInterface()
+
+# Create the Gradio interface
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    with gr.Row():
+        with gr.Column(scale=2):
+            # Main chat interface
+            chatbot = gr.Chatbot(
+                label="Chat History",
+                height=600,
+                show_label=True,
+            )
+            
+            with gr.Row():
+                message = gr.Textbox(
+                    label="Your message",
+                    placeholder="Type your message here...",
+                    lines=2
+                )
+                submit_btn = gr.Button("Send", variant="primary")
+        
+        with gr.Column(scale=1):
+            # System settings and parameters
+            with gr.Group(label="System Configuration"):
+                system_prompt = gr.Textbox(
+                    label="System Prompt",
+                    value=DEFAULT_SYSTEM_PROMPT,
+                    lines=4
+                )
+                context_file = gr.File(
+                    label="Upload Context JSON",
+                    file_types=[".json"]
+                )
+                upload_button = gr.Button("Load Context")
+                context_status = gr.Textbox(label="Context Status", interactive=False)
+            
+            with gr.Group(label="Generation Parameters"):
+                temperature = gr.Slider(
+                    minimum=0.1,
+                    maximum=2.0,
+                    value=0.7,
+                    step=0.1,
+                    label="Temperature"
+                )
+                max_length = gr.Slider(
+                    minimum=50,
+                    maximum=2000,
+                    value=500,
+                    step=50,
+                    label="Max Length"
+                )
+                top_p = gr.Slider(
+                    minimum=0.1,
+                    maximum=1.0,
+                    value=0.9,
+                    step=0.1,
+                    label="Top P"
+                )
+                presence_penalty = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.0,
+                    step=0.1,
+                    label="Presence Penalty"
+                )
+                frequency_penalty = gr.Slider(
+                    minimum=0.0,
+                    maximum=2.0,
+                    value=0.0,
+                    step=0.1,
+                    label="Frequency Penalty"
+                )
+            
+            clear_btn = gr.Button("Clear Chat History")
+
+    # Event handlers
+    def submit_message(message, temperature, max_length, top_p, presence_penalty, frequency_penalty):
+        response, history = chat_interface.generate_response(
+            message, temperature, max_length, top_p, presence_penalty, frequency_penalty
+        )
+        return "", history
+
+    submit_btn.click(
+        submit_message,
+        inputs=[message, temperature, max_length, top_p, presence_penalty, frequency_penalty],
+        outputs=[message, chatbot]
+    )
+    
+    message.submit(
+        submit_message,
+        inputs=[message, temperature, max_length, top_p, presence_penalty, frequency_penalty],
+        outputs=[message, chatbot]
+    )
+    
+    clear_btn.click(
+        lambda: (chat_interface.clear_history(), ""),
+        outputs=[chatbot, message]
+    )
+    
+    upload_button.click(
+        chat_interface.load_context_from_json,
+        inputs=[context_file],
+        outputs=[context_status, system_prompt]
+    )
+
+# Launch the interface
+demo.launch()