Update summarise_bot.py

summarise_bot.py

@@ -1,385 +1,385 @@
-# =========================
-# IMPORTS
-# =========================
-from langgraph.graph import StateGraph, START, END
-from typing import TypedDict
-from langchain_core.messages import HumanMessage
-from langchain_openai import ChatOpenAI
-from langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_core.tools import tool
-import json
-from dotenv import load_dotenv
-
-load_dotenv()
-
-
-# =========================
-# TAVILY TOOL
-# =========================
-@tool
-def tavily_search(query: str) -> dict:
-    """
-    Perform a real-time web search using Tavily.
-    """
-    try:
-        search = TavilySearchResults(max_results=2)
-        results = search.run(query)
-        return {"query": query, "results": results}
-    except Exception as e:
-        return {"error": str(e)}
-    
-
-# =========================
-# LLM
-# =========================
-llm = ChatOpenAI(
-    model="gpt-4.1-nano",
-    temperature=0.4,
-    streaming=True
-)
-
-
-# =========================
-# STATE
-# =========================
-class MovieState(TypedDict, total=False):
-    title: str
-    overview: str
-    web_context: str
-    key_plot_points: str
-    iconic_moments: str
-    themes: str
-    interesting_facts: str
-    songs: str
-    trailer: str
-    summary: str
-
-
-# =========================
-# NODE: FETCH WEB CONTEXT
-# =========================
-def fetch_web_context(state: MovieState):
-    title = state["title"]
-
-    query = f"""
-    Find reliable and up-to-date information about the movie "{title}".
-
-    Focus on:
-    - Official trailers (studio or verified YouTube channels)
-    - Soundtrack / songs (Spotify, Apple Music, IMDb soundtrack)
-    - Verified trivia or interesting facts
-    - Release details and reception (optional)
-
-    Prefer sources like:
-    - IMDb
-    - Wikipedia
-    - Official studio websites
-    - Verified YouTube channels
-    - Major entertainment publications
-
-    Avoid:
-    - Fan theories
-    - Reviews without factual info
-    - Opinion-heavy blogs
-    """
-
-    web = tavily_search.run(query)
-
-    return {
-        "web_context": str(web)
-    }
-
-# =========================
-# HELPER PROMPT RUNNER
-# =========================
-def run_llm(prompt: str) -> str:
-    return llm.invoke(prompt).content
-
-
-# =========================
-# ANALYSIS NODES
-# =========================
-def find_key_points(state: MovieState):
-    prompt = f"""
-You are a professional movie analyst.
-
-Movie title: {state['title']}
-
-Overview:
-{state['overview']}
-
-Verified web context (may include reviews, trivia, or plot confirmations):
-{state['web_context']}
-
-Task:
-Extract the MOST IMPORTANT plot points that define the story.
-
-Guidelines:
-- Focus on STORY EVENTS, not themes or opinions
-- Keep it chronological
-- Avoid unnecessary details or long explanations
-- Do NOT invent scenes not supported by the overview or web context
-
-Output format (strict):
-- Bullet list
-- 5–7 plot points max
-- Each point: 1 concise sentence
-"""
-    return {"key_plot_points": run_llm(prompt)}
-
-
-def find_iconic_moments(state: MovieState):
-    prompt = f"""
-You are a film analyst identifying ICONIC moments.
-
-Movie title: {state['title']}
-
-Overview:
-{state['overview']}
-
-Verified web context (reviews, trivia, cultural references):
-{state['web_context']}
-
-Task:
-Identify the most ICONIC moments from the movie.
-
-Definition of iconic:
-- Scenes that audiences remember most
-- Moments often referenced in reviews, memes, or pop culture
-- Visually, emotionally, or narratively standout scenes
-
-Guidelines:
-- Do NOT summarize the full plot
-- Avoid repeating basic plot points
-- Focus on memorable SCENES or MOMENTS
-- Base choices on common recognition (not personal opinion)
-
-Output format (strict):
-- Numbered list
-- 4–6 iconic moments
-- Each item:
-  • Scene title (short)
-  • One-sentence explanation of why it’s iconic
-"""
-    return {"iconic_moments": run_llm(prompt)}
-
-def find_themes(state: MovieState):
-    prompt = f"""
-You are a movie analyst focusing on THEMES.
-
-Movie title: {state['title']}
-
-Overview:
-{state['overview']}
-
-Verified web context (critical analysis, reviews, commentary):
-{state['web_context']}
-
-Task:
-Identify the CORE THEMES explored in the movie.
-
-Guidelines:
-- Themes should be CONCEPTS (not plot points or morals)
-- Avoid vague words like "life" or "journey" unless specific
-- Base themes on story events and critical interpretation
-- Do NOT over-explain
-
-Output format (strict):
-- Bullet list
-- 3–5 themes only
-- Each theme format:
-  **Theme name** – one concise explanatory sentence
-"""
-    return {"themes": run_llm(prompt)}
-
-def find_interesting_facts(state: MovieState):
-    prompt = f"""
-You are a movie researcher collecting VERIFIED trivia.
-
-Movie title: {state['title']}
-
-Overview:
-{state['overview']}
-
-Verified web context (interviews, trivia, production notes, reviews):
-{state['web_context']}
-
-Task:
-Extract interesting and lesser-known facts about the movie.
-
-Guidelines:
-- Facts must be BASED on the web context or widely known sources
-- Avoid speculation or unverified claims
-- Focus on production, casting, behind-the-scenes, or reception
-- Do NOT repeat plot points
-
-Output format (strict):
-- Bullet list
-- 4–6 facts
-- Each fact:
-  • One concise sentence
-  • Clearly factual (no opinions)
-"""
-    return {"interesting_facts": run_llm(prompt)}
-
-def find_songs(state: MovieState):
-    prompt = f"""
-You are extracting OFFICIAL soundtrack information.
-
-Movie title: {state['title']}
-
-Verified web context (soundtrack listings, music platforms, official sources):
-{state['web_context']}
-
-Task:
-Identify the official soundtrack songs associated with this movie.
-
-Rules:
-- Include ONLY officially released songs (not background score unless famous)
-- Prefer reliable sources (Spotify, YouTube, Apple Music, IMDb soundtrack)
-- Do NOT guess or invent songs
-- Do NOT add explanations or extra text
-
-Output format (STRICT — follow exactly):
-- One song per line
-- Each line format:
-  [song name, official link]
-
-If no reliable song information is found:
-- Return an empty list: []
-"""
-    return {"songs": run_llm(prompt)}
-
-
-def find_trailer(state: MovieState):
-    prompt = f"""
-You are retrieving OFFICIAL movie trailer information.
-
-Movie title: {state['title']}
-
-Verified web context (official YouTube channels, studio pages, IMDb, Wikipedia):
-{state['web_context']}
-
-Task:
-Find official trailer links for this movie.
-
-Rules:
-- ONLY official trailers (no fan edits, reactions, reviews)
-- Prefer studio or verified YouTube channels
-- Do NOT invent or approximate links
-- Do NOT include commentary or descriptions
-
-Output format (STRICT — follow exactly):
-- One trailer per line
-- Each line format:
-  [trailer name, official link]
-
-If no official trailer is found:
-- Return an empty list: []
-"""
-    return {"trailer": run_llm(prompt)}
-
-
-# =========================
-# FINAL SUMMARY
-# =========================
-def generate_summary(state: MovieState):
-    prompt = f"""
-You are generating a FINAL movie summary for a frontend application.
-
-Movie title: {state['title']}
-
-Use ONLY the information provided below.
-Do NOT add new facts.
-Do NOT use markdown.
-Do NOT include extra text.
-
-INPUT DATA
----------
-
-KEY PLOT POINTS:
-{state['key_plot_points']}
-
-ICONIC MOMENTS:
-{state['iconic_moments']}
-
-THEMES:
-{state['themes']}
-
-INTERESTING FACTS:
-{state['interesting_facts']}
-
-SONGS:
-{state['songs']}
-
-TRAILERS:
-{state['trailer']}
-
----------
-
-TASK:
-Return a VALID JSON object that follows this schema EXACTLY.
-
-JSON SCHEMA (STRICT):
-{{
-  "overview": "2–3 sentence high-level movie overview",
-  "key_moments": ["moment 1", "moment 2", "moment 3"],
-  "themes": ["theme 1", "theme 2"],
-  "notable_facts": ["fact 1", "fact 2"],
-  "soundtrack_highlights": ["song name 1", "song name 2"],
-  "official_trailer": "trailer name"
-}}
-"""
-    return {"summary": run_llm(prompt)}
-
-
-
-# =========================
-# GRAPH
-# =========================
-graph = StateGraph(MovieState)
-
-graph.add_node("fetch_web_context", fetch_web_context)
-graph.add_node("find_key_points", find_key_points)
-graph.add_node("find_iconic_moments", find_iconic_moments)
-graph.add_node("find_themes", find_themes)
-graph.add_node("find_interesting_facts", find_interesting_facts)
-graph.add_node("find_songs", find_songs)
-graph.add_node("find_trailer", find_trailer)
-graph.add_node("generate_summary", generate_summary)
-
-graph.add_edge(START, "fetch_web_context")
-
-graph.add_edge("fetch_web_context", "find_key_points")
-graph.add_edge("fetch_web_context", "find_iconic_moments")
-graph.add_edge("fetch_web_context", "find_themes")
-graph.add_edge("fetch_web_context", "find_interesting_facts")
-graph.add_edge("fetch_web_context", "find_songs")
-graph.add_edge("fetch_web_context", "find_trailer")
-
-graph.add_edge("find_key_points", "generate_summary")
-graph.add_edge("find_iconic_moments", "generate_summary")
-graph.add_edge("find_themes", "generate_summary")
-graph.add_edge("find_interesting_facts", "generate_summary")
-graph.add_edge("find_songs", "generate_summary")
-graph.add_edge("find_trailer", "generate_summary")
-
-graph.add_edge("generate_summary", END)
-
-workflow = graph.compile()
-
-def summarise_movie(title: str, overview: str):
-    result = workflow.invoke({
-        "title": title,
-        "overview": overview
-    })
-
-    raw_summary = result["summary"]
-
-    try:
-        return json.loads(raw_summary)
-    except json.JSONDecodeError:
-        raise ValueError("LLM returned invalid JSON")
-
-
+# =========================
+# IMPORTS
+# =========================
+from langgraph.graph import StateGraph, START, END
+from typing import TypedDict
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain_core.tools import tool
+import json
+from dotenv import load_dotenv
+
+load_dotenv()
+
+
+# =========================
+# TAVILY TOOL
+# =========================
+@tool
+def tavily_search(query: str) -> dict:
+    """
+    Perform a real-time web search using Tavily.
+    """
+    try:
+        search = TavilySearchResults(max_results=2)
+        results = search.run(query)
+        return {"query": query, "results": results}
+    except Exception as e:
+        return {"error": str(e)}
+    
+
+# =========================
+# LLM
+# =========================
+llm = ChatOpenAI(
+    model="gpt-4.1-nano",
+    temperature=0.4,
+    streaming=True
+)
+
+
+# =========================
+# STATE
+# =========================
+class MovieState(TypedDict, total=False):
+    title: str
+    overview: str
+    web_context: str
+    key_plot_points: str
+    iconic_moments: str
+    themes: str
+    interesting_facts: str
+    songs: str
+    trailer: str
+    summary: str
+
+
+# =========================
+# NODE: FETCH WEB CONTEXT
+# =========================
+def fetch_web_context(state: MovieState):
+    title = state["title"]
+
+    query = f"""
+    Find reliable and up-to-date information about the movie "{title}".
+
+    Focus on:
+    - Official trailers (studio or verified YouTube channels)
+    - Soundtrack / songs (Spotify, Apple Music, IMDb soundtrack)
+    - Verified trivia or interesting facts
+    - Release details and reception (optional)
+
+    Prefer sources like:
+    - IMDb
+    - Wikipedia
+    - Official studio websites
+    - Verified YouTube channels
+    - Major entertainment publications
+
+    Avoid:
+    - Fan theories
+    - Reviews without factual info
+    - Opinion-heavy blogs
+    """
+
+    web = tavily_search.run(query)
+
+    return {
+        "web_context": str(web)
+    }
+
+# =========================
+# HELPER PROMPT RUNNER
+# =========================
+def run_llm(prompt: str) -> str:
+    return llm.invoke(prompt).content
+
+
+# =========================
+# ANALYSIS NODES
+# =========================
+def find_key_points(state: MovieState):
+    prompt = f"""
+You are a professional movie analyst.
+
+Movie title: {state['title']}
+
+Overview:
+{state['overview']}
+
+Verified web context (may include reviews, trivia, or plot confirmations):
+{state['web_context']}
+
+Task:
+Extract the MOST IMPORTANT plot points that define the story.
+
+Guidelines:
+- Focus on STORY EVENTS, not themes or opinions
+- Keep it chronological
+- Avoid unnecessary details or long explanations
+- Do NOT invent scenes not supported by the overview or web context
+
+Output format (strict):
+- Bullet list
+- 5–7 plot points max
+- Each point: 1 concise sentence
+"""
+    return {"key_plot_points": run_llm(prompt)}
+
+
+def find_iconic_moments(state: MovieState):
+    prompt = f"""
+You are a film analyst identifying ICONIC moments.
+
+Movie title: {state['title']}
+
+Overview:
+{state['overview']}
+
+Verified web context (reviews, trivia, cultural references):
+{state['web_context']}
+
+Task:
+Identify the most ICONIC moments from the movie.
+
+Definition of iconic:
+- Scenes that audiences remember most
+- Moments often referenced in reviews, memes, or pop culture
+- Visually, emotionally, or narratively standout scenes
+
+Guidelines:
+- Do NOT summarize the full plot
+- Avoid repeating basic plot points
+- Focus on memorable SCENES or MOMENTS
+- Base choices on common recognition (not personal opinion)
+
+Output format (strict):
+- Numbered list
+- 4–6 iconic moments
+- Each item:
+  • Scene title (short)
+  • One-sentence explanation of why it’s iconic
+"""
+    return {"iconic_moments": run_llm(prompt)}
+
+def find_themes(state: MovieState):
+    prompt = f"""
+You are a movie analyst focusing on THEMES.
+
+Movie title: {state['title']}
+
+Overview:
+{state['overview']}
+
+Verified web context (critical analysis, reviews, commentary):
+{state['web_context']}
+
+Task:
+Identify the CORE THEMES explored in the movie.
+
+Guidelines:
+- Themes should be CONCEPTS (not plot points or morals)
+- Avoid vague words like "life" or "journey" unless specific
+- Base themes on story events and critical interpretation
+- Do NOT over-explain
+
+Output format (strict):
+- Bullet list
+- 3–5 themes only
+- Each theme format:
+  **Theme name** – one concise explanatory sentence
+"""
+    return {"themes": run_llm(prompt)}
+
+def find_interesting_facts(state: MovieState):
+    prompt = f"""
+You are a movie researcher collecting VERIFIED trivia.
+
+Movie title: {state['title']}
+
+Overview:
+{state['overview']}
+
+Verified web context (interviews, trivia, production notes, reviews):
+{state['web_context']}
+
+Task:
+Extract interesting and lesser-known facts about the movie.
+
+Guidelines:
+- Facts must be BASED on the web context or widely known sources
+- Avoid speculation or unverified claims
+- Focus on production, casting, behind-the-scenes, or reception
+- Do NOT repeat plot points
+
+Output format (strict):
+- Bullet list
+- 4–6 facts
+- Each fact:
+  • One concise sentence
+  • Clearly factual (no opinions)
+"""
+    return {"interesting_facts": run_llm(prompt)}
+
+def find_songs(state: MovieState):
+    prompt = f"""
+You are extracting OFFICIAL soundtrack information.
+
+Movie title: {state['title']}
+
+Verified web context (soundtrack listings, music platforms, official sources):
+{state['web_context']}
+
+Task:
+Identify the official soundtrack songs associated with this movie.
+
+Rules:
+- Include ONLY officially released songs (not background score unless famous)
+- Prefer reliable sources (Spotify, YouTube, Apple Music, IMDb soundtrack)
+- Do NOT guess or invent songs
+- Do NOT add explanations or extra text
+
+Output format (STRICT — follow exactly):
+- One song per line
+- Each line format:
+  [song name, official link]
+
+If no reliable song information is found:
+- Return an empty list: []
+"""
+    return {"songs": run_llm(prompt)}
+
+
+def find_trailer(state: MovieState):
+    prompt = f"""
+You are retrieving OFFICIAL movie trailer information.
+
+Movie title: {state['title']}
+
+Verified web context (official YouTube channels, studio pages, IMDb, Wikipedia):
+{state['web_context']}
+
+Task:
+Find official trailer links for this movie.
+
+Rules:
+- ONLY official trailers (no fan edits, reactions, reviews)
+- Prefer studio or verified YouTube channels
+- Do NOT invent or approximate links
+- Do NOT include commentary or descriptions
+
+Output format (STRICT — follow exactly):
+- One trailer per line
+- Each line format:
+  [trailer name, official link]
+
+If no official trailer is found:
+- Return an empty list: []
+"""
+    return {"trailer": run_llm(prompt)}
+
+
+# =========================
+# FINAL SUMMARY
+# =========================
+def generate_summary(state: MovieState):
+    prompt = f"""
+You are generating a FINAL movie summary for a frontend application.
+
+Movie title: {state['title']}
+
+Use ONLY the information provided below.
+Do NOT add new facts.
+Do NOT use markdown.
+Do NOT include extra text.
+
+INPUT DATA
+---------
+
+KEY PLOT POINTS:
+{state['key_plot_points']}
+
+ICONIC MOMENTS:
+{state['iconic_moments']}
+
+THEMES:
+{state['themes']}
+
+INTERESTING FACTS:
+{state['interesting_facts']}
+
+SONGS:
+{state['songs']}
+
+TRAILERS:
+{state['trailer']}
+
+---------
+
+TASK:
+Return a VALID JSON object that follows this schema EXACTLY.
+
+JSON SCHEMA (STRICT):
+{{
+  "overview": "4–7 sentence high-level movie overview",
+  "key_moments": ["moment 1", "moment 2", "moment 3", "moment 4"],
+  "themes": ["theme 1", "theme 2", "theme 3"],
+  "notable_facts": ["fact 1", "fact 2", "fact 3"],
+  "soundtrack_highlights": ["song name 1", "song name 2"],
+  "official_trailer": "trailer name"
+}}
+"""
+    return {"summary": run_llm(prompt)}
+
+
+
+# =========================
+# GRAPH
+# =========================
+graph = StateGraph(MovieState)
+
+graph.add_node("fetch_web_context", fetch_web_context)
+graph.add_node("find_key_points", find_key_points)
+graph.add_node("find_iconic_moments", find_iconic_moments)
+graph.add_node("find_themes", find_themes)
+graph.add_node("find_interesting_facts", find_interesting_facts)
+graph.add_node("find_songs", find_songs)
+graph.add_node("find_trailer", find_trailer)
+graph.add_node("generate_summary", generate_summary)
+
+graph.add_edge(START, "fetch_web_context")
+
+graph.add_edge("fetch_web_context", "find_key_points")
+graph.add_edge("fetch_web_context", "find_iconic_moments")
+graph.add_edge("fetch_web_context", "find_themes")
+graph.add_edge("fetch_web_context", "find_interesting_facts")
+graph.add_edge("fetch_web_context", "find_songs")
+graph.add_edge("fetch_web_context", "find_trailer")
+
+graph.add_edge("find_key_points", "generate_summary")
+graph.add_edge("find_iconic_moments", "generate_summary")
+graph.add_edge("find_themes", "generate_summary")
+graph.add_edge("find_interesting_facts", "generate_summary")
+graph.add_edge("find_songs", "generate_summary")
+graph.add_edge("find_trailer", "generate_summary")
+
+graph.add_edge("generate_summary", END)
+
+workflow = graph.compile()
+
+def summarise_movie(title: str, overview: str):
+    result = workflow.invoke({
+        "title": title,
+        "overview": overview
+    })
+
+    raw_summary = result["summary"]
+
+    try:
+        return json.loads(raw_summary)
+    except json.JSONDecodeError:
+        raise ValueError("LLM returned invalid JSON")
+
+
 #print(summarise_movie("Jumanji", "Four teenagers are sucked into a magical video game..."))