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import os
import streamlit as st
import google.generativeai as genai
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import pandas as pd
from db import get_mongo_client
# Configure Gemini API key
GEMINI_API_KEY = os.getenv("gemini_api")
if GEMINI_API_KEY:
genai.configure(api_key=GEMINI_API_KEY)
else:
st.error("⚠️ Google API key is missing! Set it in Hugging Face Secrets.")
# Load pre-trained sentiment analysis model
MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
sentiment_pipeline = pipeline("sentiment-analysis", model=model, tokenizer=tokenizer)
except Exception as e:
st.error(f"❌ Error loading sentiment model: {e}")
# Load Topic Extraction Model
try:
topic_pipeline = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
except Exception as e:
st.error(f"❌ Error loading topic extraction model: {e}")
# Predefined topic labels for classification
TOPIC_LABELS = [
"Technology", "Politics", "Business", "Sports", "Entertainment",
"Health", "Science", "Education", "Finance", "Travel", "Food"
]
# Function to analyze sentiment using the pre-trained model
def analyze_sentiment(text):
try:
sentiment_result = sentiment_pipeline(text)[0]
label = sentiment_result['label']
score = sentiment_result['score']
sentiment_mapping = {
"LABEL_0": "Negative",
"LABEL_1": "Neutral",
"LABEL_2": "Positive"
}
return sentiment_mapping.get(label, "Unknown"), score
except Exception as e:
return f"Error analyzing sentiment: {e}", None
# Function to extract topic using zero-shot classification
def extract_topic(text):
try:
topic_result = topic_pipeline(text, TOPIC_LABELS)
top_topic = topic_result["labels"][0]
confidence = topic_result["scores"][0]
return top_topic, confidence
except Exception as e:
return f"Error extracting topic: {e}", None
# Function to determine if the user's query is about the dataset
def is_dataset_query(text):
keywords = ["dataset", "data", "historical", "csv", "stored"]
text_lower = text.lower()
for keyword in keywords:
if keyword in text_lower:
return True
return False
# Function to retrieve insights from the dataset stored in MongoDB
def get_dataset_insights():
try:
collection = get_mongo_client()
data = list(collection.find({}, {"_id": 0}))
if not data:
return "The dataset in MongoDB is empty."
df = pd.DataFrame(data)
# Map the sentiment labels from sentiment140.csv: 0 -> Negative, 2 -> Neutral, 4 -> Positive.
sentiment_mapping = {0: "Negative", 2: "Neutral", 4: "Positive"}
if "target" in df.columns:
df['sentiment_label'] = df['target'].apply(lambda x: sentiment_mapping.get(int(x), "Unknown"))
summary = df['sentiment_label'].value_counts().to_dict()
summary_str = ", ".join([f"{k}: {v}" for k, v in summary.items()])
return f"The dataset sentiment distribution is: {summary_str}."
else:
return "The dataset does not have a 'target' field."
except Exception as e:
return f"Error retrieving dataset insights: {e}"
# Function to generate AI response along with sentiment and topic analysis
def chatbot_response(user_prompt):
if not user_prompt:
return None, None, None, None, None
# Check if the query is about the dataset
if is_dataset_query(user_prompt):
dataset_insights = get_dataset_insights()
sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
topic_label, topic_confidence = extract_topic(user_prompt)
return dataset_insights, sentiment_label, sentiment_confidence, topic_label, topic_confidence
else:
try:
# Generate AI response using Gemini
model_gen = genai.GenerativeModel("gemini-1.5-pro")
ai_response = model_gen.generate_content(user_prompt)
sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
topic_label, topic_confidence = extract_topic(user_prompt)
return ai_response.text, sentiment_label, sentiment_confidence, topic_label, topic_confidence
except Exception as e:
return f"❌ Error: {e}", None, None, None, None
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