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import os
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import re
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import fitz
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import torch
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import pandas as pd
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from sentence_transformers import SentenceTransformer, util
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2', device=device)
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env_ref = ["environment","climate change","carbon emissions","pollution","waste","green energy",
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"renewable resources","sustainability","biodiversity","eco-friendly","net zero",
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"solar energy","wind energy","water conservation"]
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esg_ref = ["environment","social responsibility","governance","sustainability","carbon emissions",
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"green energy","renewable resources","waste management","climate change","pollution control",
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"biodiversity","eco-friendly","net zero","solar energy","wind energy","water conservation",
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"community development","employee welfare","diversity","ethics"]
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action_ref = ["implemented","adopted","reduced emissions","recycled","renewable energy",
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"sustainability project","steps taken to reduce carbon emissions",
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"initiatives to help the environment","measures to prevent greenwashing"]
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claim_ref = ["plans to achieve","committed to","targets","pledges","goal","aims to",
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"intent to reduce","objective to be","aims for sustainability","pledged to achieve",
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"will reduce carbon","expect to reach net zero","plans to be carbon neutral by",
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"commitment to net zero by","goal to be eco friendly by","target year for sustainability",
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"striving to be net zero","intends to adopt renewable energy","aiming for eco-friendly operations"]
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def extract_text(pdf_path):
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text = ""
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with fitz.open(pdf_path) as doc:
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for page in doc:
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text += page.get_text()
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return text
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def split_sentences(text):
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return re.split(r'(?<=[.!?])\s+', text)
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def semantic_matches(sentences, reference, threshold=0.55, batch_size=64):
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ref_emb = model.encode(reference, convert_to_tensor=True)
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matches = []
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for i in range(0, len(sentences), batch_size):
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batch = sentences[i:i+batch_size]
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sent_emb = model.encode(batch, convert_to_tensor=True)
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sim_matrix = util.cos_sim(sent_emb, ref_emb)
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for j, sim_scores in enumerate(sim_matrix):
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if sim_scores.max().item() >= threshold:
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matches.append(batch[j].strip())
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return matches if matches else ["NA"]
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def run_pipeline(pdf_folder):
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data = []
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pdf_files = [f for f in os.listdir(pdf_folder) if f.lower().endswith(".pdf")]
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for pdf in pdf_files:
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company_name = os.path.splitext(pdf)[0]
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pdf_path = os.path.join(pdf_folder, pdf)
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text = extract_text(pdf_path)
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sentences = split_sentences(text)
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total_sentences = len(sentences) if sentences else 1
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env_sentences = semantic_matches(sentences, env_ref)
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esg_sentences = semantic_matches(sentences, esg_ref)
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action_sentences = semantic_matches(sentences, action_ref)
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claim_sentences = semantic_matches(sentences, claim_ref, threshold=0.54)
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env_count = len([s for s in env_sentences if s != "NA"])
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esg_count = len([s for s in esg_sentences if s != "NA"])
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action_count = len([s for s in action_sentences if s != "NA"])
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claim_count = len([s for s in claim_sentences if s != "NA"])
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env_score = (env_count / total_sentences) * 100
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claim_score = (claim_count / total_sentences) * 100
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action_score = (action_count / total_sentences) * 100
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relative_focus = (esg_count / total_sentences) * 100
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net_action = action_score - claim_score
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net_direction = "Positive" if net_action > 0 else "Negative"
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data.append({
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"Company": company_name,
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"Relative Focus Score": round(relative_focus, 2),
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"Environment Score": round(env_score, 2),
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"Claims Score": round(claim_score, 2),
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"Actions Score": round(action_score, 2),
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"Net Action": round(net_action, 2),
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"Direction": net_direction
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})
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return pd.DataFrame(data)
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