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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,
)
# ✅ Extract only the response
cleaned_response = response.split("### Response:")[-1].strip()
history.append((message, cleaned_response)) # ✅ Update history with the new message and response
save_interaction(message, cleaned_response, feedback) # ✅ Save the interaction and feedback
yield cleaned_response # ✅ 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() |