Update app_backup.py

app_backup.py

@@ -0,0 +1,57 @@
+import gradio as gr
+
+from haystack.components.generators import HuggingFaceTGIGenerator
+
+generator = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1")
+generator.warm_up()
+
+from haystack.components.fetchers.link_content import LinkContentFetcher
+from haystack.components.converters import HTMLToDocument
+from haystack.components.preprocessors import DocumentSplitter
+from haystack.components.rankers import TransformersSimilarityRanker
+from haystack.components.generators import GPTGenerator
+from haystack.components.builders.prompt_builder import PromptBuilder
+from haystack import Pipeline
+
+fetcher = LinkContentFetcher()
+converter = HTMLToDocument()
+document_splitter = DocumentSplitter(split_by="word", split_length=50)
+similarity_ranker = TransformersSimilarityRanker(top_k=3)
+
+prompt_template = """
+According to these documents:
+{% for doc in documents %}
+  {{ doc.content }}
+{% endfor %}
+Answer the given question: {{question}}
+Answer:
+"""
+prompt_builder = PromptBuilder(template=prompt_template)
+
+pipeline = Pipeline()
+pipeline.add_component("fetcher", fetcher)
+pipeline.add_component("converter", converter)
+pipeline.add_component("splitter", document_splitter)
+pipeline.add_component("ranker", similarity_ranker)
+pipeline.add_component("prompt_builder", prompt_builder)
+pipeline.add_component("llm", generator)
+
+pipeline.connect("fetcher.streams", "converter.sources")
+pipeline.connect("converter.documents", "splitter.documents")
+pipeline.connect("splitter.documents", "ranker.documents")
+pipeline.connect("ranker.documents", "prompt_builder.documents")
+pipeline.connect("prompt_builder.prompt", "llm.prompt")
+
+def respond(prompt, use_rag):
+    if use_rag:
+        result = pipeline.run({"prompt_builder": {"question": prompt},
+                   "ranker": {"query": prompt},
+                   "fetcher": {"urls": ["https://haystack.deepset.ai/blog/introducing-haystack-2-beta-and-advent"]},
+                    "llm":{"generation_kwargs": {"max_new_tokens": 350}}})
+        return result['llm']['replies'][0]
+    else:
+        result = generator.run(prompt, generation_kwargs={"max_new_tokens": 350})
+    return result["replies"][0]
+
+iface = gr.Interface(fn=respond, inputs=["text", "checkbox"], outputs="text")
+iface.launch()