Create app.py

app.py

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+import gradio as gr
+from langchain.document_loaders import OnlinePDFLoader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.llms import HuggingFaceHub
+from langchain.embeddings import HuggingFaceHubEmbeddings
+from langchain.vectorstores import Chroma
+from langchain.chains import RetrievalQA
+
+def loading_pdf(): return 'Loading...'
+
+def pdf_changes(pdf_doc, repo_id):
+    loader = OnlinePDFLoader(pdf_doc.name)
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=2096, chunk_overlap=0)
+    texts = text_splitter.split_documents(documents)
+    embeddings = HuggingFaceHubEmbeddings()
+    db = Chroma.from_documents(texts, embeddings)
+    retriever = db.as_retriever()
+    llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={'temperature': 0.5, 'max_new_tokens': 2096})
+    global qa 
+    qa = RetrievalQA.from_chain_type(llm=llm, chain_type='stuff', retriever=retriever, return_source_documents=True)
+    return "Ready"
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, ''
+
+def bot(history):
+    response = infer(history[-1][0])
+    history[-1][1] = response['result']
+    return history
+
+def infer(question):
+    query = question
+    result = qa({'query': query})
+    return result
+
+css="""
+#col-container {max-width: 700px; margin-left: auto; margin-right: auto;}
+"""
+
+title = """
+    <h1>Chat with PDF</h1>
+"""
+
+with gr.Blocks(css=css, theme='Taithrah/Minimal') as demo:
+    with gr.Column(elem_id='col-container'):
+        gr.HTML(title)
+
+    with gr.Column():
+        pdf_doc = gr.File(label='Upload a PDF', file_types=['.pdf'])
+        repo_id = gr.Dropdown(label='LLM', 
+                              choices=[
+                                  'mistralai/Mistral-7B-Instruct-v0.1', 
+                                  'HuggingFaceH4/zephyr-7b-beta', 
+                                  'meta-llama/Llama-2-7b-chat-hf', 
+                                  '01-ai/Yi-6B-200K'
+                                  'cognitivecomputations/dolphin-2.5-mixtral-8x7b'
+                              ],
+                             value='mistralai/Mistral-7B-Instruct-v0.1')
+        with gr.Row():
+            langchain_status = gr.Textbox(label='Status', placeholder='', interactive=False)
+            load_pdf = gr.Button('Load PDF to LangChain')
+
+        chatbot = gr.Chatbot([], elem_id='chatbot')#.style(height=350)
+        question = gr.Textbox(label='Question', placeholder='Type your query')
+        submit_btn = gr.Button('Send')
+
+    repo_id.change(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
+    load_pdf.click(pdf_changes, inputs=[pdf_doc, repo_id], outputs=[langchain_status], queue=False)
+    question.submit(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
+    submit_btn.click(add_text, [chatbot, question], [chatbot, question]).then(bot, chatbot, chatbot)
+
+demo.launch()