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import os
import streamlit as st
import google.generativeai as genai
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
# 🔑 Fetch API key from Hugging Face Secrets
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.")
# Model for Sentiment Analysis
MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
# Load Sentiment Analysis Model
try:
tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
sentiment_pipeline = pipeline("sentiment-analysis", model=MODEL_NAME, 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
def analyze_sentiment(text):
try:
sentiment_result = sentiment_pipeline(text)[0]
label = sentiment_result['label'] # Extract sentiment label (POSITIVE, NEGATIVE, NEUTRAL)
score = sentiment_result['score'] # Extract confidence score
# Convert labels to readable format
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
def extract_topic(text):
try:
topic_result = topic_pipeline(text, TOPIC_LABELS)
top_topic = topic_result["labels"][0] # Get the highest confidence topic
confidence = topic_result["scores"][0] # Confidence score for the topic
return top_topic, confidence
except Exception as e:
return f"Error extracting topic: {e}", None
# Function to generate AI response, sentiment, and topic
def chatbot_response(user_prompt):
if not user_prompt:
return None, None, None, None, None
try:
# AI Response from Gemini
model = genai.GenerativeModel("gemini-1.5-pro")
ai_response = model.generate_content(user_prompt)
# Sentiment Analysis
sentiment_label, sentiment_confidence = analyze_sentiment(user_prompt)
# Topic Extraction
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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