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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 | |