File size: 2,649 Bytes
2fdfa6a 1bce4f6 2fdfa6a 3740b36 1bce4f6 3740b36 2fdfa6a 3740b36 2fdfa6a 3740b36 87b7758 3740b36 12e22a0 3740b36 2fdfa6a 3740b36 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 |
import os
import json
import gradio as gr
from transformers import pipeline
# Step 1: Load JSON Dataset
def load_dataset(folder_path):
data = []
for filename in os.listdir(folder_path):
if filename.endswith(".json"):
with open(os.path.join(folder_path, filename), "r") as file:
data.extend(json.load(file)) # Assuming each file is a list of entries
return data
# Step 2: Initialize OpenAI or Hugging Face Model
model = pipeline("question-answering", model="deepset/roberta-base-squad2") # Replace with your preferred model
# Step 3: Query Handler
def query_chatbot(query, name, email, contact):
# Load the dataset
dataset = load_dataset("dataset/")
# Retrieve relevant information using a simple RAG technique
responses = []
for entry in dataset:
context = entry.get("content", "") # Extract relevant content from the JSON file
if query.lower() in context.lower():
response = model(question=query, context=context)
responses.append(response["answer"])
# Compile the result
response = (
f"Hello {name}!\n\n"
f"Based on your query: '{query}', here are some relevant insights:\n\n"
+ "\n".join(responses)[:3] # Limit to top 3 responses
)
# Create a profile (Optional enhancement)
profile = {
"name": name,
"email": email,
"contact": contact,
"query": query,
"responses": responses,
}
# Optionally, you can send this profile to an email or save it for further analysis.
return response
# Step 4: Gradio Interface
def chatbot_ui():
with gr.Blocks() as app:
gr.Markdown("# 🎓 Education Consultant Chatbot")
with gr.Row():
with gr.Column():
query_input = gr.Textbox(label="Your Query", placeholder="Ask about courses, visas, or programs...")
name_input = gr.Textbox(label="Name", placeholder="Your Full Name")
email_input = gr.Textbox(label="Email", placeholder="Your Email Address")
contact_input = gr.Textbox(label="Contact (Optional)", placeholder="Your Contact Number")
submit_btn = gr.Button("Submit")
with gr.Column():
output_text = gr.Textbox(label="Chatbot Response")
submit_btn.click(
query_chatbot,
inputs=[query_input, name_input, email_input, contact_input],
outputs=output_text,
)
return app
# Step 5: Launch App
if __name__ == "__main__":
chatbot = chatbot_ui()
chatbot.launch()
|