Added Question Answering System

.gitignore

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+.Chroma
+*.ipynb
+*.pyc

main.py

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+import logging
+import os
+
+import gradio as gr
+from langchain import OpenAI
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import PyPDFLoader
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.vectorstores import Chroma
+
+documents = []
+qa = None
+
+
+def get_file(file):
+    try:
+        global documents
+        data = PyPDFLoader(file.name)
+        documents = data.load_and_split(CharacterTextSplitter(chunk_size=2000, chunk_overlap=0))
+    except Exception as e:
+        logging.error(e, exc_info=True)
+        return "Failed to upload."
+    return "File Uploaded."
+
+
+def model_configuration(model_name, api_key=None, hug_model=None, hug_token=None):
+    try:
+        embeddings, llm = None, None
+        if not documents:
+            return gr.update(value="Please upload correct PDF!", visible=True)
+        global qa
+        if model_name == "OpenAI":
+            os.environ["OPENAI_API_KEY"] = os.getenv("OPENAI_API_KEY", api_key)
+            embeddings = OpenAIEmbeddings()
+            llm = OpenAI(temperature=1)
+        elif model_name == "HuggingFace":
+            return gr.update(value="Hugging Face is not yet supported!", visible=True)
+            os.environ["HUGGINGFACEHUB_API_TOKEN"] = os.getenv("", hug_token)
+            # embeddings = HuggingFaceEmbeddings(model_name=hug_model, model_kwargs={'device': 'cpu'})
+            # llm = HuggingFaceHub(repo_id=hug_model)
+
+        if embeddings:
+            db = Chroma.from_documents(documents, embeddings)
+            retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 2})
+            qa = ConversationalRetrievalChain.from_llm(llm, chain_type="map_reduce", retriever=retriever,
+                                                       return_source_documents=True, verbose=False)
+    except Exception as e:
+        logging.error(e, exc_info=True)
+        return gr.update(value="Error occurred!", visible=True)
+    return gr.update(value="Model Built", visible=True)
+
+
+def response(msg, chat_history):
+    global qa
+    result = qa({"question": msg, "chat_history": map(tuple, chat_history)})
+    final_resp = result.get("answer", "").strip()
+    chat_history.append((msg, final_resp))
+    return "", chat_history
+
+
+with gr.Blocks() as demo:
+    with gr.Tab("PDF Ingestion") as pdf_input:
+        file = None
+        with gr.Column() as r1:
+            file = gr.File(file_types=[".pdf"])
+            op_txt = gr.Label(value="", label="")
+            fl_btn = gr.Button("Upload & Ingest 🚀")
+            fl_btn.click(get_file, inputs=[file], outputs=op_txt)
+
+    with gr.Tab("Select Model") as model:
+        model_name = gr.Dropdown(
+            ["NA", "OpenAI", "HuggingFace"],
+            show_label=True,
+            label="Model Name",
+            multiselect=False,
+            value="NA"
+        )
+        with gr.Column(visible=False) as openai_config:
+            api_key = gr.Textbox(value="", label="OPENAI API KEY", placeholder="sk-...", visible=True, interactive=True)
+
+        with gr.Column(visible=False) as huggy_config:
+            hug_model = gr.Dropdown(["distilbert-base-uncased"],
+                                    value="distilbert-base-uncased", multiselect=False)
+            hug_token = gr.Textbox(value="", placeholder="hf-...", interactive=True)
+
+
+        def show_configuration(model_name):
+            if model_name == "OpenAI":
+                return {
+                    openai_config: gr.update(visible=True),
+                    huggy_config: gr.update(visible=False)
+                }
+            elif model_name == "HuggingFace":
+                return {
+                    openai_config: gr.update(visible=False),
+                    huggy_config: gr.update(visible=True)
+                }
+            return {
+                openai_config: gr.update(visible=False),
+                huggy_config: gr.update(visible=False)
+            }
+
+
+        model_name.change(show_configuration, inputs=[model_name], outputs=[openai_config, huggy_config])
+        model_updated = gr.Label("", visible=False)
+        btn = gr.Button("Configure Model 🤖")
+        btn.click(model_configuration, inputs=[model_name, api_key, hug_model, hug_token], outputs=model_updated)
+
+    with gr.Tab("Q&A") as qna:
+        with gr.Column() as r:
+            chatbot = gr.Chatbot(show_label=True)
+            msg = gr.Textbox(placeholder="Ask Something")
+            clear = gr.Button("Clear")
+            msg.submit(response, [msg, chatbot], [msg, chatbot])
+            clear.click(lambda: None, None, chatbot, queue=False)
+
+if __name__ == "__main__":
+    demo.launch()