Create main.py

main.py

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+import gradio as gr  # to create the web UI for the application
+from openai import OpenAI  # to interact with LM Studio models
+import re  # for text manipulation
+
+# ANSI escape code for colors
+RESET_COLOR = '\033[0m'
+NEON_GREEN = '\033[92m'
+
+client = OpenAI(base_url="http://localhost:1836/v1", api_key="lm-studio")
+
+# Initialize an empty list to store conversation history
+conversation_history = []
+
+def format_response_text(text):
+    """
+    Formats the response text for improved readability.
+    :param text: The raw response text.
+    :return: Formatted text.
+    """
+    # New paragraphs after each period, question mark, or exclamation point
+    text = re.sub(r'(?<=[.!?])\s+(?=[A-Z])', '\n\n', text)
+    
+    # Properly indent bullet points and numbered lists
+    text = re.sub(r'(\n)?(\s*)?([•\-*]|\d+\.)\s+', r'\n    \3 ', text)
+    
+    return text
+
+def mistral_streamed_interaction(user_input, conversation_history):
+    """
+    Interacts with the mistral model via LM Studio, maintaining conversation context.
+    :param user_input: String, user's input.
+    :param conversation_history: List, the conversation history.
+    :return: Tuple, containing the response and updated conversation history.
+    """
+    # Add user's input to conversation history
+    conversation_history.append({"role": "user", "content": user_input})
+
+    streamed_completion = client.chat.completions.create(
+        model="TheBloke/dolphin-2.2.1-mistral-7B-GGUF/dolphin-2.2.1-mistral-7b.Q4_K_S.gguf",
+        messages=conversation_history,
+        stream=True  # Enable streaming
+    )
+
+    full_response = ""
+    line_buffer = ""
+
+    for chunk in streamed_completion:
+        delta_content = chunk.choices[0].delta.content
+        if delta_content:
+            line_buffer += delta_content
+            if '\n' in line_buffer:
+                lines = line_buffer.split('\n')
+                full_response += '\n'.join(lines[:-1])
+                line_buffer = lines[-1]
+
+    if line_buffer:
+        full_response += line_buffer
+
+    full_response = format_response_text(full_response)
+
+    # Add model's response to conversation history
+    conversation_history.append({"role": "system", "content": full_response})
+
+    return full_response, conversation_history
+
+def clear_conversation_history():
+    """
+    Clears the conversation history.
+    """
+    global conversation_history
+    conversation_history = []
+    print("Conversation history cleared.")
+
+
+def gradio_interface_interaction(user_input):
+    """
+    This function acts as the bridge between the Gradio interface and the chat logic.
+    It processes the user input via the existing chat logic and returns the response.
+    
+    :param user_input: User input from the Gradio interface.
+    :return: Response text to be displayed in the Gradio interface.
+    """
+    # Call the existing chat interaction function with the global conversation history
+    response, _ = mistral_streamed_interaction(user_input, conversation_history)
+    return response
+
+
+# Modify the Gradio interface to use the new interaction function
+iface = gr.Interface(
+    fn=gradio_interface_interaction,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your prompt here"),
+    outputs=gr.Textbox(),
+)
+
+# Launch the Gradio interface
+iface.launch()
+