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import gradio as gr
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
from gtts import gTTS
import os
# Load the AgriQBot model from Hugging Face using the transformers library
tokenizer = AutoTokenizer.from_pretrained("mrSoul7766/AgriQBot")
model = AutoModelForSeq2SeqLM.from_pretrained("mrSoul7766/AgriQBot")
def respond(
message,
history=None, # Set history default to None
max_tokens=512,
temperature=0.7,
top_p=0.95,
):
"""
Respond to user queries using the AgriQBot model.
Args:
- message: User query (string).
- history: List of previous (user, assistant) message pairs (default is None).
- max_tokens: Maximum number of tokens in the response.
- temperature: Controls randomness in response.
- top_p: Controls diversity of the response.
Returns:
- Response string as the chatbot's answer.
"""
if history is None:
history = [] # Initialize history to an empty list if None
messages = [{"role": "system", "content": "You are a friendly farming assistant. Answer the user's questions related to farming."}]
# Construct the conversation history
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
# Append the current user message
messages.append({"role": "user", "content": message})
# Tokenize the input and generate the response
inputs = tokenizer(message, return_tensors="pt", padding=True, truncation=True)
outputs = model.generate(**inputs, max_length=max_tokens, temperature=temperature, top_p=top_p)
# Decode the response and return it
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
def text_to_voice(response):
"""
Convert the response text to speech using Google Text-to-Speech.
Args:
- response: Text response from the model to be converted to speech.
"""
tts = gTTS(text=response, lang='en')
tts.save("response.mp3")
os.system("start response.mp3") # Use 'open' for macOS, 'xdg-open' for Linux
# Build the Gradio Interface
demo = gr.Interface(
fn=respond,
inputs=[
gr.Textbox(label="Enter your question about farming:"),
],
outputs=[
gr.Textbox(label="Chatbot Response"),
gr.Audio(value="response.mp3", label="Audio Response")
],
title="Farming Assistant Chatbot",
description="Ask questions about farming, crop management, pest control, soil conditions, and best agricultural practices."
)
# Launch the interface
if __name__ == "__main__":
demo.launch()
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