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import gradio as gr | |
from huggingface_hub import InferenceClient | |
from textblob import TextBlob | |
import json | |
import os | |
client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf") | |
# Directory to store interactions and feedback | |
DATA_DIR = "data" | |
INTERACTIONS_FILE = os.path.join(DATA_DIR, "interactions.json") | |
# Ensure the data directory exists | |
os.makedirs(DATA_DIR, exist_ok=True) | |
def format_alpaca_prompt(user_input, system_prompt, history): | |
"""Formats input in Alpaca/LLaMA style""" | |
history_str = "\n".join([f"### Instruction:\n{h[0]}\n### Response:\n{h[1]}" for h in history]) | |
prompt = f"""{system_prompt} | |
{history_str} | |
### Instruction: | |
{user_input} | |
### Response: | |
""" | |
return prompt | |
def analyze_sentiment(message): | |
"""Analyze the sentiment of the user's message""" | |
blob = TextBlob(message) | |
sentiment = blob.sentiment.polarity | |
return sentiment | |
def save_interaction(user_input, chatbot_response, feedback=None): | |
"""Save the interaction and feedback to a file""" | |
interaction = { | |
"user_input": user_input, | |
"chatbot_response": chatbot_response, | |
"feedback": feedback, | |
"timestamp": "2025-02-25 04:00:30" | |
} | |
if os.path.exists(INTERACTIONS_FILE): | |
with open(INTERACTIONS_FILE, "r") as file: | |
interactions = json.load(file) | |
else: | |
interactions = [] | |
interactions.append(interaction) | |
with open(INTERACTIONS_FILE, "w") as file: | |
json.dump(interactions, file, indent=4) | |
def respond(message, history, system_message, max_tokens, temperature, top_p, feedback=None): | |
sentiment = analyze_sentiment(message) | |
# Adjust system message based on sentiment | |
if sentiment < -0.2: | |
system_message = "You are a sympathetic Chatbot." | |
elif sentiment > 0.2: | |
system_message = "You are an enthusiastic Chatbot." | |
else: | |
system_message = "You are a friendly Chatbot." | |
formatted_prompt = format_alpaca_prompt(message, system_message, history) | |
response = client.text_generation( | |
formatted_prompt, | |
max_new_tokens=max_tokens, | |
temperature=temperature, | |
top_p=top_p, | |
) | |
# β Extract only the response | |
cleaned_response = response.split("### Response:")[-1].strip() | |
history.append((message, cleaned_response)) # β Update history with the new message and response | |
save_interaction(message, cleaned_response, feedback) # β Save the interaction and feedback | |
yield cleaned_response # β Output only the answer | |
def collect_feedback(response, feedback): | |
"""Collect user feedback on the chatbot's response""" | |
save_interaction(response, feedback=feedback) | |
feedback_interface = gr.Interface( | |
fn=collect_feedback, | |
inputs=[ | |
gr.Textbox(label="Response"), | |
gr.Radio(choices=["Good", "Bad"], label="Feedback"), | |
], | |
outputs="text", | |
title="Feedback Interface" | |
) | |
demo = gr.ChatInterface( | |
respond, | |
additional_inputs=[ | |
gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
gr.Slider(minimum=1, maximum=250, value=128, step=1, label="Max new tokens"), | |
gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperature"), | |
gr.Slider(minimum=0.1, maximum=1.0, value=0.99, step=0.01, label="Top-p (nucleus sampling)"), | |
gr.Radio(choices=["Good", "Bad"], label="Feedback", optional=True), | |
], | |
) | |
if __name__ == "__main__": | |
demo.launch() | |
feedback_interface.launch() |