Create app.py

app.py

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+from pymed import PubMed
+from typing import List
+from haystack import component
+from haystack import Document
+from haystack.components.generators import HuggingFaceTGIGenerator
+from dotenv import load_dotenv
+import os 
+from haystack import Pipeline
+from haystack.components.builders.prompt_builder import PromptBuilder
+import gradio as gr
+import time
+
+# load_dotenv()
+
+# os.environ['HUGGINGFACE_API_KEY'] = os.getenv('HUGGINGFACE_API_KEY')
+
+
+pubmed = PubMed(tool="Haystack2.0Prototype", email="dummyemail@gmail.com")
+
+def documentize(article):
+  return Document(content=article.abstract, meta={'title': article.title, 'keywords': article.keywords})
+
+@component
+class PubMedFetcher():
+
+  @component.output_types(articles=List[Document])
+  def run(self, queries: list[str]):
+    cleaned_queries = queries[0].strip().split('\n')
+
+    articles = []
+    try:
+      for query in cleaned_queries:
+        response = pubmed.query(query, max_results = 1)
+        documents = [documentize(article) for article in response]
+        articles.extend(documents)
+    except Exception as e:
+        print(e)
+        print(f"Couldn't fetch articles for queries: {queries}" )
+    results = {'articles': articles}
+    return results
+
+keyword_llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1")
+keyword_llm.warm_up()
+
+llm = HuggingFaceTGIGenerator("mistralai/Mixtral-8x7B-Instruct-v0.1")
+llm.warm_up()
+
+
+keyword_prompt_template = """
+Your task is to convert the following question into 3 keywords that can be used to find relevant medical research papers on PubMed.
+Here is an examples:
+question: "What are the latest treatments for major depressive disorder?"
+keywords:
+Antidepressive Agents
+Depressive Disorder, Major
+Treatment-Resistant depression
+---
+question: {{ question }}
+keywords:
+"""
+
+prompt_template = """
+Answer the question truthfully based on the given documents.
+If the documents don't contain an answer, use your existing knowledge base.
+q: {{ question }}
+Articles:
+{% for article in articles %}
+  {{article.content}}
+  keywords: {{article.meta['keywords']}}
+  title: {{article.meta['title']}}
+{% endfor %}
+"""
+
+keyword_prompt_builder = PromptBuilder(template=keyword_prompt_template)
+
+prompt_builder = PromptBuilder(template=prompt_template)
+fetcher = PubMedFetcher()
+
+pipe = Pipeline()
+
+pipe.add_component("keyword_prompt_builder", keyword_prompt_builder)
+pipe.add_component("keyword_llm", keyword_llm)
+pipe.add_component("pubmed_fetcher", fetcher)
+pipe.add_component("prompt_builder", prompt_builder)
+pipe.add_component("llm", llm)
+
+pipe.connect("keyword_prompt_builder.prompt", "keyword_llm.prompt")
+pipe.connect("keyword_llm.replies", "pubmed_fetcher.queries")
+
+pipe.connect("pubmed_fetcher.articles", "prompt_builder.articles")
+pipe.connect("prompt_builder.prompt", "llm.prompt")
+
+def ask(question):
+  output = pipe.run(data={"keyword_prompt_builder":{"question":question},
+                          "prompt_builder":{"question": question},
+                          "llm":{"generation_kwargs": {"max_new_tokens": 500}}})
+  print(question)
+  print(output['llm']['replies'][0])
+  return output['llm']['replies'][0]
+
+# result = ask("How are mRNA vaccines being used for cancer treatment?")
+
+# print(result)
+
+iface = gr.Interface(fn=ask, inputs=gr.Textbox(
+    value="How are mRNA vaccines being used for cancer treatment?"), 
+        outputs="markdown",  
+        title="LLM Augmented Q&A over PubMed Search Engine",
+        description="Ask a question about BioMedical and get an answer from a friendly AI assistant.",
+        examples=[["How are mRNA vaccines being used for cancer treatment?"], 
+                ["Suggest me some Case Studies related to Pneumonia."],
+                ["Tell me about HIV AIDS."],["Suggest some case studies related to Auto Immune Disorders."],
+                ["How to treat a COVID infected Patient?"]],
+    theme=gr.themes.Soft(),
+    allow_flagging="never",)
+
+iface.launch(debug=True)