create app.py

app.py

@@ -0,0 +1,103 @@
+from langchain.llms import OpenAI
+from langchain.chains.qa_with_sources import load_qa_with_sources_chain
+from langchain.docstore.document import Document
+import requests
+import pathlib
+import subprocess
+import tempfile
+import os
+import gradio as gr
+
+# using a vector space for our search
+from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.vectorstores.faiss import FAISS
+from langchain.text_splitter import CharacterTextSplitter
+
+#To get markdowns from github fo Gradio (/or your) repo
+def get_github_docs(repo_owner, repo_name):
+    with tempfile.TemporaryDirectory() as d:
+        subprocess.check_call(
+            f"git clone https://github.com/{repo_owner}/{repo_name}.git .",
+            cwd=d,
+            shell=True,
+        )
+        git_sha = (
+            subprocess.check_output("git rev-parse HEAD", shell=True, cwd=d)
+            .decode("utf-8")
+            .strip()
+        )
+        repo_path = pathlib.Path(d)
+        markdown_files = list(repo_path.rglob("*.md")) + list(
+            repo_path.rglob("*.mdx")
+        )
+        for markdown_file in markdown_files:
+            try:
+                with open(markdown_file, "r") as f:
+                    relative_path = markdown_file.relative_to(repo_path)
+                    github_url = f"https://github.com/{repo_owner}/{repo_name}/blob/{git_sha}/{relative_path}"
+                    yield Document(page_content=f.read(), metadata={"source": github_url})
+            except FileNotFoundError:
+                print(f"Could not open file: {markdown_file}")
+
+#sources = get_github_docs("gradio-app", "gradio")
+#source_chunks = []
+#splitter = CharacterTextSplitter(separator=" ", chunk_size=1024, chunk_overlap=0)
+
+#for source in sources:
+#    for chunk in splitter.split_text(source.page_content):
+#        source_chunks.append(Document(page_content=chunk, metadata=source.metadata))
+        
+#search_index = FAISS.from_documents(source_chunks, OpenAIEmbeddings()) #(source_chunks, OpenAIEmbeddings()) # <------
+#chain = load_qa_with_sources_chain(OpenAI(temperature=0))  ## <<---------
+
+                
+#loading FAISS search index from disk
+with open("search_index.pickle", "rb") as f:
+    search_index = pickle.load(f)
+
+
+def print_answer(question, openai):   #openai_embeddings
+    #search_index = get_search_index()
+    chain = load_qa_with_sources_chain(openai) #(OpenAI(temperature=0)) 
+    response = (
+        chain(
+            {
+                "input_documents": search_index.similarity_search(question, k=4),
+                "question": question,
+            },
+            return_only_outputs=True,
+        )["output_text"]
+    )
+    #print(response)
+    if len(r.split('\n')[-1].split())>2:
+        response = response.split('\n')[0] + ', '.join([' <a href="' + response.split('\n')[-1].split()[i] + '" target="_blank"><u>Click Link' + str(i) + '</u></a>' for i in range(1,len(response.split('\n')[-1].split()))])
+    else: 
+        response = response.split('\n')[0] + ' <a href="' + response.split('\n')[-1].split() + '" target="_blank"><u>Click Link</u></a>'
+    return response
+
+
+
+def chat(message, history, openai_api_key):
+    #openai_embeddings = OpenAIEmbeddings(openai_api_key=openai_api_key)
+    openai = OpenAI(temperature=0, openai_api_key=openai_api_key )
+    #os.environ["OPENAI_API_KEY"] = openai_api_key
+    history = history or []
+    message = message.lower()
+    response = print_answer(message, openai)   #openai_embeddings
+    history.append((message, response))
+    return history, history
+
+#chatbot = gr.Chatbot().style(color_map=("green", "orange"))
+
+with gr.Blocks() as demo:
+  gr.Markdown("""<h1><centre>LangChain - powered - Gradio-Helper-Bot </h1></centre>
+  """)
+  with gr.Row():
+    question = gr.Textbox(label = 'Type in your questions about Gradio here', placeholder = 'What is the role of "every" argument in a component')
+    openai_api_key = gr.Textbox()
+  state = gr.State()
+  chatbot = gr.Chatbot()
+  question.submit(chat, [question, state, openai_api_key], [chatbot, state])
+
+if __name__ == "__main__":
+    demo.launch()