Create app.py

app.py

@@ -0,0 +1,41 @@
+import gradio as gr
+from haystack import Pipeline
+from haystack.components.fetchers import LinkContentFetcher
+from haystack.components.converters import HTMLToDocument
+from haystack.components.builders import PromptBuilder
+from haystack.components.generators import OpenAIGenerator
+from haystack.utils import Secret
+
+def budget_chatbot(query):
+    fetcher = LinkContentFetcher()
+    converter = HTMLToDocument()
+    prompt_template = """
+    According to the contents of this website:
+    {% for document in documents %}
+      {{document.content}}
+    {% endfor %}
+    Answer the given question: {{query}}
+    Answer:
+    """
+    prompt_builder = PromptBuilder(template=prompt_template)
+    llm = OpenAIGenerator(
+        api_key=Secret.from_env_var("MONSTER_API_KEY"),
+        api_base_url="https://llm.monsterapi.ai/v1/",
+        model="microsoft/Phi-3-mini-4k-instruct",
+        generation_kwargs = {"max_tokens": 256}
+    )
+    pipeline = Pipeline()
+    pipeline.add_component("fetcher", fetcher)
+    pipeline.add_component("converter", converter)
+    pipeline.add_component("prompt", prompt_builder)
+    pipeline.add_component("llm", llm)
+
+    pipeline.connect("fetcher.streams", "converter.sources")
+    pipeline.connect("converter.documents", "prompt.documents")
+    pipeline.connect("prompt.prompt", "llm.prompt")
+
+    result = pipeline.run({"fetcher": {"urls": ["https://example.com/indian-2024-budget"]},
+                          "prompt": {"query": query}})
+    return result["llm"]["replies"][0]
+
+gr.Interface(fn=budget_chatbot, inputs="text", outputs="text", title="Indian 2024 Budget Chatbot").launch()