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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: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| """ | |
| Respond to user queries using the AgriQBot model. | |
| Args: | |
| - message: User query (string). | |
| - history: List of previous (user, assistant) message pairs. | |
| - system_message: System-level instructions for the assistant. | |
| - 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. | |
| """ | |
| messages = [{"role": "system", "content": system_message}] | |
| # 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(value="You are a friendly farming assistant. Answer the user's questions related to farming.", label="System Message"), | |
| gr.Textbox(label="Enter your question about farming:"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max New Tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"), | |
| ], | |
| 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() | |