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import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from PyPDF2 import PdfReader
import os
# Load the Gemma model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
model = AutoModelForCausalLM.from_pretrained("google/gemma-2-9b-it")
pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
# Helper functions for document processing
def get_pdf_text(pdf_file):
text = ""
pdf_reader = PdfReader(pdf_file)
for page in pdf_reader.pages:
text += page.extract_text()
return text
def load_farming_knowledge_base(pdf_path="ai-farming.pdf"):
if not os.path.exists(pdf_path):
raise FileNotFoundError(f"PDF document '{pdf_path}' not found.")
return get_pdf_text(pdf_path)
# Load knowledge base from the farming PDF
knowledge_base = load_farming_knowledge_base()
# Chatbot response generation
def chatbot_response(user_message):
# Check if the question relates to the knowledge base
if user_message.lower() in knowledge_base.lower():
context = "This information is extracted from the AI Farming Guide:"
input_text = f"{context}\n{user_message}\n"
else:
context = "Answer based on general farming knowledge:"
input_text = f"{context}\n{user_message}\n"
# Generate a response using the Gemma model
response = pipe(input_text, max_length=512, temperature=0.7, top_p=0.9)
return response[0]["generated_text"]
# Gradio UI
with gr.Blocks() as demo:
gr.Markdown("# 🌾 AI Agri Farmer Chat Bot 🌿")
gr.Markdown(
"Welcome to the AI Agri Farmer Chat Bot! 🤖 Ask your farming-related questions, "
"such as crop management, soil health, fertilizers, or pest control. If your "
"question isn't found in the farming guide, the bot will answer based on general knowledge."
)
with gr.Row():
user_input = gr.Textbox(
label="💬 Ask your farming question:",
placeholder="Example: 'What is the best fertilizer for wheat?'",
)
chatbot_output = gr.Textbox(label="🤖 Chat Bot Response:")
example_inputs = gr.Examples(
examples=[
"What is the best fertilizer for rice?",
"How much water does maize need weekly?",
"What crops grow well in clay soil?",
],
inputs=user_input,
)
def respond(input_text):
return chatbot_response(input_text)
user_input.submit(respond, inputs=user_input, outputs=chatbot_output)
gr.Markdown("### 📄 About the Knowledge Base")
gr.Markdown(
"The chatbot uses information from the AI Farming Guide (`ai-farming.pdf`) as its primary source. "
"For topics not covered, it falls back on general farming knowledge."
)
# Launch the Gradio app
demo.launch()
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