fix gradio and folder name

examples/langchain-docscanner/main.py

@@ -1,86 +0,0 @@
-import gradio as gr
-
-import sys
-import os
-from collections.abc import Iterable
-
-from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader, UnstructuredHTMLLoader
-
-from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
-
-from langchain.chains import RetrievalQA
-
-from langchain.llms import Ollama
-
-from langchain.vectorstores import FAISS, Chroma
-
-from langchain.embeddings import GPT4AllEmbeddings, CacheBackedEmbeddings
-
-from langchain.storage import LocalFileStore#, RedisStore, UpstashRedisStore, InMemoryStore
-
-ollama = Ollama(base_url='http://localhost:11434',
-#model="codellama")
-#model="starcoder")
-model="llama2")
-
-docsUrl = "/home/user/dev/docs"
-
-documents = []
-for file in os.listdir(docsUrl):
-
-    if file.endswith(".pdf"):
-        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'):
-        htm_path = docsUrl + "/" + file
-        loader = UnstructuredHTMLLoader(htm_path)
-        documents.extend(loader.load())
-        print("Found " + htm_path)        
-
-
-text_splitter = CharacterTextSplitter(chunk_size=32, chunk_overlap=32)
-all_splits = text_splitter.split_documents(documents)
-
-
-
-#fs = LocalFileStore("/home/gabriele/dev/cache/")
-
-#underlying_embeddings = GPT4AllEmbeddings()
-#cached_embedder = CacheBackedEmbeddings.from_bytes_store(
-#    underlying_embeddings, fs, namespace=underlying_embeddings.model
-#)
-
-
-
-vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings(embeddings_chunk_size=1000))
-#vectorstore = FAISS.from_documents(documents=all_splits, embedding=cached_embedder)
-
-
-def AI_response(question, history):
-    docs = vectorstore.similarity_search(question)
-    len(docs)
-    qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
-    #reply=qachain()    
-    #reply=str(qachain({"query": question}))
-    reply=str(qachain.run(question))
-    return reply
-
-
-
-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)
-

examples/langchain-python-langchain-webui/main.py

@@ -0,0 +1,80 @@
+#! /usr/bin/python3.10
+
+import gradio as gr
+import sys
+import os
+import subprocess
+from collections.abc import Iterable
+from langchain.document_loaders import PyPDFLoader, Docx2txtLoader, TextLoader, UnstructuredHTMLLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
+from langchain.chains import RetrievalQA
+from langchain.llms import Ollama
+from langchain.vectorstores import FAISS, Chroma
+from langchain.embeddings import GPT4AllEmbeddings, CacheBackedEmbeddings
+from langchain.storage import LocalFileStore#, RedisStore, UpstashRedisStore, InMemoryStore
+
+docsUrl = "/home/user/dev/docs"
+ollamaModel="llama2"
+
+def get_ollama_names():
+    output = subprocess.check_output(["ollama", "list"])
+    lines = output.decode("utf-8").splitlines()
+    names = {}
+    for line in lines[1:]:
+        name = line.split()[0].split(':')[0]
+        names[name] = name
+    return names
+
+names = get_ollama_names()
+
+def greet(name):
+    global ollamaModel
+    ollamaModel=name
+    return f"{name}"
+
+dropdown = gr.Dropdown(label="Models available", choices=names, value="llama2")
+textbox = gr.Textbox(label="You chose")
+
+def AI_response(question, history):
+    ollama = Ollama(base_url='http://localhost:11434', model=ollamaModel)
+    print(ollamaModel)
+    documents = []
+    for file in os.listdir(docsUrl):
+        if file.endswith(".pdf"):
+            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'):
+            htm_path = docsUrl + "/" + file
+            loader = UnstructuredHTMLLoader(htm_path)
+            documents.extend(loader.load())
+            print("Found " + htm_path)        
+
+    text_splitter = CharacterTextSplitter(chunk_size=32, chunk_overlap=32)
+    all_splits = text_splitter.split_documents(documents)
+    vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings(embeddings_chunk_size=1000))
+    docs = vectorstore.similarity_search(question)
+    len(docs)
+    qachain=RetrievalQA.from_chain_type(ollama, retriever=vectorstore.as_retriever())
+    reply=str(qachain.run(question))
+    return reply
+
+
+
+with gr.Blocks() as demo:
+    interface = gr.Interface(fn=greet, inputs=[dropdown], outputs=[textbox], title="Choose a LLM model")
+    chat = gr.ChatInterface(AI_response, title="Put your files in folder " + docsUrl)
+
+demo.launch()
+