create template app.py

template/app.py

@@ -0,0 +1,80 @@
+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
+import pickle
+
+# 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
+                
+#loading FAISS search index from disk
+with open("search_index.pickle", "rb") as f:
+    search_index = pickle.load(f)
+
+#Get GPT3 response using Langchain
+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"]
+    )
+    if len(response.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()[-1] + '" 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
+
+
+with gr.Blocks() as demo:
+  gr.HTML("""<div style="text-align: center; max-width: 700px; margin: 0 auto;">
+        <div
+        style="
+            display: inline-flex;
+            align-items: center;
+            gap: 0.8rem;
+            font-size: 1.75rem;
+        "
+        >
+        <h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
+            $RepoName QandA - LangChain Bot
+        </h1>
+        </div>
+        <p style="margin-bottom: 10px; font-size: 94%">
+        Hi, I'm a Q and A $RepoName expert bot, start by typing in your OpenAI API key, questions/issues you are facing in your $RepoName implementations and then press enter.<br>
+        <a href="https://huggingface.co/spaces/ysharma/InstructPix2Pix_Chatbot?duplicate=true"><img src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>Duplicate Space with GPU Upgrade for fast Inference & no queue<br> 
+        Built using <a href="https://langchain.readthedocs.io/en/latest/" target="_blank">LangChain</a> and <a href="https://github.com/gradio-app/gradio" target="_blank">Gradio Github repo</a>
+        </p>
+    </div>""")  
+  with gr.Row():
+    question = gr.Textbox(label = 'Type in your questions about Gradio here and press Enter!', placeholder = 'What is the role of "every" argument in a component')
+    openai_api_key = gr.Textbox(type='password', label="Enter your OpenAI API key here")
+  state = gr.State()
+  chatbot = gr.Chatbot()
+  question.submit(chat, [question, state, openai_api_key], [chatbot, state])
+
+if __name__ == "__main__":
+    demo.launch()

template/app_simple.py

@@ -1,15 +0,0 @@
-import os
-import gradio as gr
-
-API_KEY=os.environ.get('HUGGING_FACE_HUB_TOKEN', None)
-
-article = """---
-This space was created using [SD Space Creator](https://huggingface.co/spaces/anzorq/sd-space-creator)."""
-
-gr.Interface.load(
-    name="models/$model_id",
-    title="""$title""",
-    description="""$description""",
-    article=article,
-    api_key=API_KEY,
-    ).queue(concurrency_count=20).launch()