From d46dedbbfaabb624128c08cd9ea5ee4bbc3b1a3e Mon Sep 17 00:00:00 2001
From: suoko <gabriele.vidali@gmail.com>
Date: Sat, 28 Oct 2023 08:59:30 +0200
Subject: [PATCH] Update main.py with gradio.chatinterface

---
 examples/langchain-docscanner/main.py | 27 +++++++++++++++++++++------
 1 file changed, 21 insertions(+), 6 deletions(-)

diff --git a/examples/langchain-docscanner/main.py b/examples/langchain-docscanner/main.py
index 6cb152cc..919233ae 100644
--- a/examples/langchain-docscanner/main.py
+++ b/examples/langchain-docscanner/main.py
@@ -29,34 +29,49 @@ for file in os.listdir(docsUrl):
         pdf_path = docsUrl + "/" + file
         loader = PyPDFLoader(pdf_path)
         documents.extend(loader.load())
+        print("Found " + pdf_path)
     elif file.endswith('.docx') or file.endswith('.doc'):
         doc_path = docsUrl + "/" + file
         loader = Docx2txtLoader(doc_path)
         documents.extend(loader.load())
+        print("Found " + doc_path)
     elif file.endswith('.txt') or file.endswith('.kt') or file.endswith('.json'):
         text_path = docsUrl + "/" + file
         loader = TextLoader(text_path)
         documents.extend(loader.load())
+        print("Found " + text_path)        
     elif file.endswith('.html') or file.endswith('.htm'):
-        text_path = docsUrl + "/" + file
-        loader = UnstructuredHTMLLoader(text_path)
+        htm_path = docsUrl + "/" + file
+        loader = UnstructuredHTMLLoader(htm_path)
         documents.extend(loader.load())
+        print("Found " + htm_path)        
 
-text_splitter = CharacterTextSplitter(chunk_size=3500, chunk_overlap=20)
+
+text_splitter = CharacterTextSplitter(chunk_size=2000, chunk_overlap=20)
 all_splits = text_splitter.split_documents(documents)
 
 from langchain.embeddings import GPT4AllEmbeddings
 from langchain.vectorstores import Chroma
 vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
 
-def greet(question):
+
+
+
+
+
+def AI_response(question, history):
     docs = vectorstore.similarity_search(question)
     len(docs)
     qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
-    reply=qachain({"query": question})
+    #reply=qachain()    
+    #reply=str(qachain({"query": question}))
+    reply=str(qachain.run(question))
     return reply
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
+
+
+demo = gr.ChatInterface(AI_response, title="Put your files in folder" + docsUrl)
 
 if __name__ == "__main__":
     demo.launch(server_name="0.0.0.0", server_port=7860)
+