Create chain_summary.py

chain_summary.py

@@ -0,0 +1,40 @@
+# chain_summary.py
+import json
+from typing import Dict
+from langchain import PromptTemplate, LLMChain
+from models import chat_model
+
+final_prompt_template = PromptTemplate(
+    input_variables=["report", "problems", "recommendation"],
+    template=(
+        "Based on the following information:\n"
+        "Report:\n{report}\n\n"
+        "Problem Severity Percentages:\n{problems}\n\n"
+        "Recommended Packages:\n{recommendation}\n\n"
+        "Generate a short summary suitable for video narration that synthesizes this information."
+    )
+)
+final_chain = LLMChain(llm=chat_model, prompt=final_prompt_template)
+
+def generate_final_summary(report: str, problems: Dict[str, float], recommendation: str) -> str:
+    summary = final_chain.run(
+        report=report,
+        problems=json.dumps(problems),
+        recommendation=recommendation
+    )
+    return summary.strip()
+
+shorten_prompt_template = PromptTemplate(
+    input_variables=["final_summary"],
+    template=(
+        "Shorten the following summary to make it concise and engaging for video narration. "
+        "Ensure all key points remain intact:\n\n"
+        "{final_summary}\n\n"
+        "Shortened Summary:"
+    )
+)
+shorten_chain = LLMChain(llm=chat_model, prompt=shorten_prompt_template)
+
+def shorten_summary(final_summary: str) -> str:
+    shortened = shorten_chain.run(final_summary=final_summary)
+    return shortened.strip()