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import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import gradio as gr
from transformers import pipeline
# Load the CSV file and preprocess it
def load_csv_and_preprocess(csv_file):
df = pd.read_csv(csv_file)
df = df.dropna().head(100000)
column_names = list(df.columns)
df['combined'] = df.apply(lambda x: "Title: " + '; '.join(x[column_names].astype(str)), axis=1)
df['combined'] = df['combined'].str.strip()
vectorizer = TfidfVectorizer()
embeddings = vectorizer.fit_transform(df['combined'])
return df, vectorizer, embeddings
# Initialize the summarization pipeline outside of the chatbot_response function
summarizer = pipeline("summarization")
# Perform semantic search for a given query
def semantic_search(df, vectorizer, embeddings, query):
search_vector = vectorizer.transform([query])
similarities = cosine_similarity(search_vector, embeddings).flatten()
df['similarities'] = similarities
result = df.sort_values('similarities', ascending=False).head(3)
return result['combined'].tolist()
# Define the chatbot response function with summarization
def chatbot_response(query, history):
if not query.strip():
return "", history
search_results = semantic_search(df, vectorizer, embeddings, query)
# Summarize the search results
summary = summarizer("\n".join(search_results), max_length=130, min_length=30, do_sample=False)[0]['summary_text']
# Format the summarized response and update the chat history
history = f"{history}User: {query}\nBot: {summary}\n\n"
return "", history # Clear the input box after each message, update history
# Load CSV and preprocess on server startup
csv_file_path = "Dronealexa.csv" # Update this to your CSV file path
df, vectorizer, embeddings = load_csv_and_preprocess(csv_file_path)
# Define a function to handle feedback
def handle_feedback(feedback, response, history_box):
# Simple logic to prepend feedback to the user's query
# This could be replaced with more sophisticated logic or ML model updating
response = f"Based on your feedback ('{feedback}'): {response}"
history = history_box + "\nBot: " + response + "\n"
return "", history # Update the history with the feedback-aware response
# Gradio Blocks Interface
with gr.Blocks() as blocks_app:
gr.Markdown("<h1 style='text-align: center;'>Explore Science & Technology with Chatbot</h1>")
history_box = gr.Textbox(label="", value="", interactive=False, lines=20)
with gr.Row():
query_input = gr.Textbox(show_label=False, placeholder="Type your message here...", lines=1)
with gr.Row():
send_button = gr.Button("Send")
send_button.click(
fn=chatbot_response,
inputs=[query_input, history_box],
outputs=[query_input, history_box]
)
feedback_input = gr.Textbox(show_label=False, placeholder="Type your feedback here...", lines=1)
feedback_button = gr.Button("Submit Feedback")
feedback_button.click(
fn=handle_feedback,
inputs=[feedback_input, history_box, history_box],
outputs=[query_input, history_box]
)
blocks_app.launch(share=True) |