Create app.py

app.py

@@ -0,0 +1,144 @@
+import streamlit as st
+import pandas as pd
+import numpy as np
+from sentence_transformers import SentenceTransformer
+from transformers import pipeline
+from sklearn.metrics.pairwise import cosine_similarity
+from docx import Document
+import io
+
+class CarbonCreditDocGenerator:
+    def __init__(self):
+        self.sbert_model = SentenceTransformer('all-MiniLM-L6-v2')
+        self.nlg_pipeline = pipeline("text-generation", model="gpt2", max_length=500)
+        
+        # Load your knowledge base here
+        self.knowledge_base = self.load_knowledge_base()
+        
+    def load_knowledge_base(self):
+        # This should load your carbon credit domain knowledge
+        return [
+            "Carbon credits represent the reduction of one metric ton of carbon dioxide emissions.",
+            "Afforestation projects involve planting trees in areas where there were none before.",
+            "The Verified Carbon Standard (VCS) is a widely recognized certification for carbon credits.",
+            "Carbon credit projects must demonstrate additionality, meaning the reductions wouldn't have occurred without the project.",
+            "Monitoring, reporting, and verification (MRV) are crucial components of carbon credit projects.",
+            # Add more knowledge base entries...
+        ]
+        
+    def process_input_data(self, input_text):
+        # In a real scenario, you'd parse the input document more thoroughly
+        lines = input_text.split('\n')
+        data = {}
+        for line in lines:
+            if ':' in line:
+                key, value = line.split(':', 1)
+                data[key.strip()] = value.strip()
+        return data
+    
+    def retrieve_relevant_knowledge(self, query, top_k=3):
+        query_embedding = self.sbert_model.encode([query])[0]
+        knowledge_embeddings = self.sbert_model.encode(self.knowledge_base)
+        
+        similarities = cosine_similarity([query_embedding], knowledge_embeddings)[0]
+        top_indices = np.argsort(similarities)[-top_k:][::-1]
+        
+        return [self.knowledge_base[i] for i in top_indices]
+    
+    def generate_section_content(self, section_title, input_data, max_length=500):
+        query = f"Generate content for the '{section_title}' section of a carbon credit document."
+        relevant_knowledge = self.retrieve_relevant_knowledge(query)
+        
+        context = f"Input data: {input_data}\n\nRelevant knowledge: {' '.join(relevant_knowledge)}"
+        prompt = f"{context}\n\nTask: {query}\n\nContent:"
+        
+        generated_text = self.nlg_pipeline(prompt, max_length=max_length, num_return_sequences=1)[0]['generated_text']
+        
+        # Apply corrective RAG
+        corrected_text = self.apply_corrective_rag(generated_text, input_data, relevant_knowledge)
+        
+        return corrected_text
+    
+    def apply_corrective_rag(self, generated_text, input_data, relevant_knowledge):
+        # This is a simplified version of corrective RAG
+        corrected_text = generated_text
+        
+        # Ensure all input data is represented
+        for key, value in input_data.items():
+            if value.lower() not in corrected_text.lower():
+                corrected_text += f" {key}: {value}."
+        
+        # Ensure relevant knowledge is incorporated
+        for knowledge in relevant_knowledge:
+            if knowledge.lower() not in corrected_text.lower():
+                corrected_text += f" {knowledge}"
+        
+        return corrected_text
+    
+    def create_document(self, input_text):
+        doc = Document()
+        doc.add_heading('Carbon Credit Project Document', 0)
+        
+        input_data = self.process_input_data(input_text)
+        
+        sections = [
+            "Executive Summary",
+            "Certificate Identification",
+            "Emission Reduction Details",
+            "Project Information",
+            "Verification and Certification",
+            "Issuance and Expiration Dates",
+            "Market Type",
+            "Transferability Information",
+            "Legal Framework",
+            "Accountability Measures",
+            "Contact Information"
+        ]
+        
+        for section in sections:
+            doc.add_heading(section, level=1)
+            content = self.generate_section_content(section, input_data)
+            doc.add_paragraph(content)
+        
+        return doc
+    
+    def generate_document(self, input_text):
+        doc = self.create_document(input_text)
+        
+        # Save the document to a BytesIO object
+        doc_io = io.BytesIO()
+        doc.save(doc_io)
+        doc_io.seek(0)
+        
+        return doc_io
+
+# Streamlit app
+def main():
+    st.title("Carbon Credit Document Generator")
+
+    # File uploader
+    uploaded_file = st.file_uploader("Choose a text file", type="txt")
+
+    if uploaded_file is not None:
+        # Read the file
+        input_text = uploaded_file.read().decode("utf-8")
+        st.text_area("Input Data", input_text, height=200)
+
+        if st.button("Generate Document"):
+            generator = CarbonCreditDocGenerator()
+            
+            with st.spinner("Generating document..."):
+                doc_io = generator.generate_document(input_text)
+
+            st.success("Document generated successfully!")
+
+            # Provide download button
+            st.download_button(
+                label="Download Carbon Credit Document",
+                data=doc_io.getvalue(),
+                file_name="carbon_credit_document.docx",
+                mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document"
+            )
+
+if __name__ == "__main__":
+    main()