Create app.py

app.py

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+# Import necessary libraries
+import gradio as gr
+from haystack.document_stores import InMemoryDocumentStore
+from haystack.nodes import DensePassageRetriever, FARMReader
+from haystack.pipelines import ExtractiveQAPipeline
+
+# 1. Initialize Document Store
+document_store = InMemoryDocumentStore(embedding_dim=768)
+
+# 2. Add Documents
+documents = [
+    {"content": "Haystack is an open-source NLP framework for search.", "meta": {"source": "Introduction"}},
+    {"content": "You can use Hugging Face models in Haystack pipelines.", "meta": {"source": "Hugging Face"}},
+    {"content": "The DensePassageRetriever is a key component of Haystack.", "meta": {"source": "Retrievers"}}
+]
+document_store.write_documents(documents)
+
+# 3. Set up Retriever
+retriever = DensePassageRetriever(
+    document_store=document_store,
+    query_embedding_model="facebook/dpr-question_encoder-single-nq-base",
+    passage_embedding_model="facebook/dpr-ctx_encoder-single-nq-base"
+)
+document_store.update_embeddings(retriever)
+
+# 4. Set up Reader
+reader = FARMReader(model_name_or_path="deepset/roberta-base-squad2", use_gpu=False)
+
+# 5. Create QA Pipeline
+qa_pipeline = ExtractiveQAPipeline(reader=reader, retriever=retriever)
+
+# 6. Define Prediction Function
+def ask_question(query):
+    results = qa_pipeline.run(query=query, params={"Retriever": {"top_k": 3}, "Reader": {"top_k": 1}})
+    if results["answers"]:
+        answer = results["answers"][0].answer
+        context = results["answers"][0].context
+        source = results["answers"][0].meta.get("source", "Unknown Source")
+        return f"**Answer:** {answer}\n\n**Context:** {context}\n\n**Source:** {source}"
+    else:
+        return "No relevant answer found. Please refine your question."
+
+# 7. Set up Gradio Interface
+interface = gr.Interface(
+    fn=ask_question,
+    inputs=gr.Textbox(lines=2, label="Ask a Question"),
+    outputs="text",
+    title="AI Search with Haystack",
+    description="Ask any question about the content in the document set."
+)
+
+# 8. Launch Application
+if __name__ == "__main__":
+    interface.launch()