initial commit

app.py

@@ -0,0 +1,68 @@
+# Bring in deps
+import streamlit as st 
+from langchain.llms import LlamaCpp
+from langchain.embeddings import LlamaCppEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.document_loaders import CSVLoader  # Import CSVLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+import pandas as pd
+
+# Customize the layout
+st.set_page_config(page_title="DOCAI", page_icon="🤖", layout="wide", )     
+st.markdown(f"""
+            <style>
+            .stApp {{background-image: url("https://images.unsplash.com/photo-1509537257950-20f875b03669?ixlib=rb-4.0.3&ixid=M3wxMjA3fDB8MHxwaG90by1wYWdlfHx8fGVufDB8fHx8fA%3D%3D&auto=format&fit=crop&w=1469&q=80"); 
+                     background-attachment: fixed;
+                     background-size: cover}}
+         </style>
+         """, unsafe_allow_html=True)
+
+# function for writing uploaded file in temp
+def write_csv_file(content, file_path):
+    try:
+        with open(file_path, 'w') as file:
+            file.write(content)
+        return True
+    except Exception as e:
+        print(f"Error occurred while writing the file: {e}")
+        return False
+
+# set prompt template
+prompt_template = """Use the following pieces of context to answer the question at the end. If you don't know the answer, just say that you don't know, don't try to make up an answer.
+
+{context}
+
+Question: {question}
+Answer:"""
+prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
+
+# initialize the LLM & Embeddings
+llm = LlamaCpp(model_path="./models/llama-7b.ggmlv3.q4_0.bin")
+embeddings = LlamaCppEmbeddings(model_path="models/llama-7b.ggmlv3.q4_0.bin")
+llm_chain = LLMChain(llm=llm, prompt=prompt)
+
+st.title("📄 Document Conversation 🤖")
+uploaded_file = st.file_uploader("Upload a CSV file", type="csv")  # Change type to csv
+
+if uploaded_file is not None:
+    content = uploaded_file.read().decode('utf-8')
+    file_path = "temp/file.csv"
+    write_csv_file(content, file_path)   
+    
+    loader = CSVLoader(file_path)  # Use CSVLoader
+    docs = loader.load()    
+    text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=0)
+    texts = text_splitter.split_documents(docs)
+    db = Chroma.from_documents(texts, embeddings)    
+    st.success("File Loaded Successfully!!")
+    
+    # Query through LLM    
+    question = st.text_input("Ask something from the file", placeholder="Find something similar to: ....this.... in the text?", disabled=not uploaded_file,)    
+    if question:
+        similar_doc = db.similarity_search(question, k=1)
+        context = similar_doc[0].page_content
+        query_llm = LLMChain(llm=llm, prompt=prompt)
+        response = query_llm.run({"context": context, "question": question})        
+        st.write(response)