Create app.py

app.py

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+import gradio as gr
+import os
+import time
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.nodes import EmbeddingRetriever
+import pandas as pd
+
+
+def load_qa_model():
+  document_store = InMemoryDocumentStore()
+  retriever = EmbeddingRetriever(
+    document_store=document_store,
+    embedding_model="sentence-transformers/all-MiniLM-L6-v2",
+    use_gpu=False,
+    scale_score=False,
+  )
+  # Get dataframe with columns "question", "answer" and some custom metadata
+  df = pd.read_csv('/content/social-faq.csv', on_bad_lines='skip', delimiter=';')
+  # Minimal cleaning
+  df.fillna(value="", inplace=True)
+  df["question"] = df["question"].apply(lambda x: x.strip())
+
+  questions = list(df["question"].values)
+  df["embedding"] = retriever.embed_queries(queries=questions).tolist()
+  df = df.rename(columns={"question": "content"})
+
+  # Convert Dataframe to list of dicts and index them in our DocumentStore
+  docs_to_index = df.to_dict(orient="records")
+  document_store.write_documents(docs_to_index)
+
+  return retriever
+
+def add_text(history, text):
+    history = history + [(text, None)]
+    return history, gr.Textbox(value="", interactive=False)
+
+
+def add_file(history, file):
+    history = history + [((file.name,), None)]
+    return history
+
+
+def bot(history):
+    print(history)
+    # response = "**That's cool!**"
+    history[-1][1] = ""
+
+    global retriever
+    response = get_answers(retriever, history[0][0])
+
+    for character in response:
+      history[-1][1] += character
+      time.sleep(0.01)
+      yield history
+
+
+
+def get_answers(retriever, query):
+  from haystack.pipelines import FAQPipeline
+
+  pipe = FAQPipeline(retriever=retriever)
+
+  from haystack.utils import print_answers
+
+  # Run any question and change top_k to see more or less answers
+  prediction = pipe.run(query=query, params={"Retriever": {"top_k": 1}})
+
+  answers = prediction['answers']
+
+  if answers:
+    return answers[0].answer
+  else:
+    return "I don't have an answer to that question"
+
+
+
+
+retriever = load_qa_model()
+
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(
+        [],
+        elem_id="chatbot",
+        bubble_full_width=False,
+        # avatar_images=(None, "/content/avatar.png"),
+    )
+
+    with gr.Row():
+        txt = gr.Textbox(
+            scale=4,
+            show_label=False,
+            placeholder="Enter text and press enter",
+            container=False,
+        )
+        inputRecord = gr.Audio(label="Record a question", source="microphone", type="filepath")
+        audioOutput = gr.Audio(label="Listen the answer", interactive=False)
+
+    txt_msg = txt.submit(add_text, [chatbot, txt], [chatbot, txt], queue=False).then(
+        bot, chatbot, chatbot
+    )
+    txt_msg.then(lambda: gr.Textbox(interactive=True), None, [txt], queue=False)
+
+demo.queue()
+demo.launch()