File size: 2,379 Bytes
e765ee0 e08d1b5 | 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 | import streamlit as st
from transformers import pipeline, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
from sentence_transformers import SentenceTransformer
import faiss, pickle
from PIL import Image
import os
tokenizer = AutoTokenizer.from_pretrained("sentiment_model")
model = AutoModelForSequenceClassification.from_pretrained("sentiment_model")
clf = pipeline("text-classification", model=model, tokenizer=tokenizer, device=-1)
embedder = SentenceTransformer("all-MiniLM-L6-v2", device="cpu")
gen_tok = AutoTokenizer.from_pretrained("google/flan-t5-base")
gen_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
index = faiss.read_index("faiss_index.index")
with open("passages.pkl", "rb") as f:
train_texts = pickle.load(f)
def retrieve_passages(query, k=5):
q_emb = embedder.encode([query])
D, I = index.search(q_emb, k)
return [train_texts[i] for i in I[0]]
def explain_sentiment(text, predicted_label, k=3):
retrieved = retrieve_passages(text, k=k)
retrieved_str = "\n- ".join(retrieved)
prompt = f"""
Text to analyze: "{text}"
Predicted sentiment: {predicted_label}
Retrieved examples:
- {retrieved_str}
Task: Write a clear explanation (1–2 sentences) about why the text is {predicted_label}.
Focus on emotional words, tone, and context. Do NOT just repeat the label.
"""
inputs = gen_tok(prompt, return_tensors="pt", truncation=True, max_length=512)
outputs = gen_model.generate(**inputs, max_length=80)
explanation = gen_tok.decode(outputs[0], skip_special_tokens=True)
return {"retrieved": retrieved, "explanation": explanation}
st.title("Sentiment Analyzer")
image = Image.open("Social-Sentiment-Tracking.png")
st.image(image, width='stretch')
text = st.text_area("Enter text to analyze:")
if st.button("Predict"):
out = clf(text)[0]
pred = out['label']
score = out['score']
if pred == "LABEL_0":
human_pred = 'Negative'
elif pred == "LABEL_1":
human_pred = 'Neutral'
else:
human_pred = 'Positive'
st.write(f"**Prediction:** {human_pred} (confidence {score*100:.2f}%)")
rag_output = explain_sentiment(text, pred)
st.subheader("Retrieved evidence")
for r in rag_output['retrieved']:
st.write("-", r)
st.subheader("Explanation")
st.write(rag_output['explanation']) |