Update app.py

app.py

@@ -29,7 +29,7 @@ document_prompt = PromptTemplate(
 )
 prompt = PromptTemplate(
   template=
-  """Write a personalised newsletter for a researcher. The researcher describes his work as follows:"{context}". Base the newsletter on the following articles:\n\n"{text}"\n\nNEWSLETTER:""",
+  """Write a personalised newsletter for a researcher. The researcher describes his work as follows:"{context}". Base the newsletter on the following articles:\n\n"{text}"\n\nNEWSLETTER:\n#Your AI curated newsletter\n""",
   input_variables=["context", "text"])
 
 # llm = FakeListLLM(responses=list(map(str, range(100))))
@@ -37,7 +37,7 @@ REPO_ID = "HuggingFaceH4/starchat-beta"
 llm = HuggingFaceHub(
     repo_id=REPO_ID,
     model_kwargs={
-        "max_new_tokens": 1024,
+        "max_new_tokens": 300,
         "do_sample": True,
         "temperature": 0.8,
         "top_p": 0.9
@@ -68,7 +68,8 @@ def get_data(user_query: str):
     for doc in relevant_docs:
         articles += f"**Title: {doc.metadata['Title']}**\n\nAbstract: {doc.metadata['Summary']}\n\n"
     output = stuff_chain({"input_documents": relevant_docs, "context": user_query})
-    return f"{output['output_text']}\n\n\n\nUsed articles:\n\n{output}"
+    output_text = output["output_text"].split("<|end|>")[0]
+    return f"#Your AI curated newsletter\n{output['output_text']}\n\n\n\nUsed articles:\n\n{articles}"
 
 demo = gr.Interface(
     fn=get_data,