updated space to allow prompt to be passed down, as well as general updates

app.py

@@ -3,9 +3,10 @@
 #do autonomous llamagents
 
 from llama_index.core.tools import FunctionTool
-from llama_index.llms.openai import OpenAI
+from llama_index.llms.openai import OpenAI as LlamaOpenAI
 from dotenv import load_dotenv
-#from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.llms.google_genai import GoogleGenAI
 from llama_index.core.agent.workflow import AgentWorkflow, FunctionAgent, ReActAgent #can also import ReActAgent or FunctionAgent from this 
 from llama_index.core.tools import FunctionTool
 from llama_index.core.workflow import Context
@@ -25,7 +26,7 @@ import tiktoken
 import requests
 import json
 import gradio as gr
-
+from openai import OpenAI 
 
 
 #from llama_index.llms.google_gemini import GoogleGenAI
@@ -34,13 +35,32 @@ import gradio as gr
 load_dotenv()
 
 #llm = OpenAI(model="gpt-4o-mini")
+import google.generativeai as genai
+
+#genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
+
+#llmGeminiPro = GoogleGenAI(model="gemini-2.5-pro")
+#print("llmGeminiPro loaded!")
 
-llm = OpenAI(
+#llmGeminiFlash = GoogleGenAI(model="gemini-2.5-flash")
+#print("llmGeminiFlash loaded!")
+
+llm = LlamaOpenAI(
     model="gpt-4o-mini",  # or "gpt-3.5-turbo"
     api_key=os.getenv('OPENAI_API_KEY'),  # You can also set this via the OPENAI_API_KEY environment variable
     streaming=True    
 )
 
+llmHigher = LlamaOpenAI(
+    model="o3",
+    api_key=os.getenv('OPENAI_API_KEY'),
+    streaming=True
+)
+
+client = OpenAI(
+    api_key=os.getenv('OPENAI_API_KEY'),
+)
+
 openai.api_key = os.getenv("OPENAI_API_KEY")
 #use gemini
 
@@ -108,7 +128,8 @@ async def find_artist_id_for_artist(ctx: Context, artist_name: str) -> int:
 
     
     """
-    current_state = await ctx.get('state')
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of find_artist_id_for_artist is: {current_state}")
  
     access_token = get_chartmetric_access_token_cached()
 
@@ -140,8 +161,10 @@ async def find_artist_id_for_artist(ctx: Context, artist_name: str) -> int:
         if "working_notes" not in current_state:
             current_state["working_notes"] = {}
 
-        current_state["working_notes"][artist_name] = artist_id
-        await ctx.set("state", current_state)  # 🟢 Save the updated state
+        current_state["working_notes"][f"artist_id_for_{artist_name}"] = artist_id
+        await ctx.store.set("state", current_state)  # 🟢 Save the updated state
+        print(f"🧠 Updated working_notes in find_artist_id_for_artist: {json.dumps(current_state['working_notes'], indent=2)}")
+
 
         return artist_id
 
@@ -163,7 +186,8 @@ async def get_similar_artists(ctx: Context, artist_id: int) -> dict:
     Notes:
     - Results are stored in working memory under "similar_artists".
     """
-    current_state = await ctx.get('state')
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of get_similar_artists is: {current_state}")
 
     access_token = get_chartmetric_access_token_cached()  # Assuming this is defined elsewhere
     print("access_token for get_similar_artists api call obatined!")
@@ -188,7 +212,7 @@ async def get_similar_artists(ctx: Context, artist_id: int) -> dict:
             current_state["working_notes"] = {}
         
         current_state["working_notes"]["similar_artists"] = similar_artists
-        await ctx.set('state', current_state)
+        await ctx.store.set('state', current_state)
 
         return similar_artists
 
@@ -210,7 +234,8 @@ async def get_youtube_audience_data(ctx: Context, artist_id: str) -> dict:
     Notes:
     - Results are saved in working memory.
     """
-    current_state = await ctx.get('state')
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of get_youtube_audience_data is: {current_state}")
     
     access_token = get_chartmetric_access_token_cached()
 
@@ -264,7 +289,7 @@ async def get_youtube_audience_data(ctx: Context, artist_id: str) -> dict:
     youtube_audience_stats = dict_to_return
     print(f"youtube_audience_stats are: {youtube_audience_stats}")
     current_state["working_notes"][f"youtube_audience_data for artist {artist_id}"] = youtube_audience_stats
-    await ctx.set('state', current_state)
+    await ctx.store.set('state', current_state)
 
     return { f"youtube_audience_data for artist {artist_id}": youtube_audience_stats}
 
@@ -282,12 +307,13 @@ async def get_tiktok_audience_data(ctx: Context, artist_id: str) -> dict:
     - artist_id (str): The Chartmetric artist ID.
 
     Returns:
-    - dict: Instagram audience breakdown.
+    - dict: TikTok audience breakdown.
 
     Notes:
     - Results are saved in working memory.
     """
-    current_state = await ctx.get('state')
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of get_tiktok_audience_data is: {current_state}")
 
     access_token = get_chartmetric_access_token_cached()
 
@@ -333,9 +359,9 @@ async def get_tiktok_audience_data(ctx: Context, artist_id: str) -> dict:
         current_state["working_notes"] = {}
     
     tiktok_audience_stats = dict_to_return
-    print(f"tiktok_audience_data are: {tiktok_audience_stats}")
+    #print(f"tiktok_audience_data are: {tiktok_audience_stats}")
     current_state["working_notes"][f"tiktok_audience_data for artist {artist_id}"] = tiktok_audience_stats
-    await ctx.set('state', current_state)
+    await ctx.store.set('state', current_state)
 
     return { f"tiktok_audience_data for artist {artist_id}": tiktok_audience_stats}
 
@@ -363,7 +389,8 @@ async def get_instagram_audience_data(ctx: Context, artist_id: str) -> dict:
     #perhaps just have it get access_token inside here
     #access_token = get_chartmetric_access_token_with_refresh()
 
-    current_state = await ctx.get('state')
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of get_instagram_audience_stats is: {current_state}")
 
     access_token = get_chartmetric_access_token_cached()
 
@@ -382,8 +409,8 @@ async def get_instagram_audience_data(ctx: Context, artist_id: str) -> dict:
         raise Exception(f"API request failed: {response.status_code} {response.reason}")
 
     data = response.json()
-    print(f"data from api call is: {data}")
-    print("Info from platform Instagram is:", data.get("obj"))
+    #print(f"data from api call is: {data}")
+    #print("Info from platform Instagram is:", data.get("obj"))
     
 
     if "working_notes" not in current_state:
@@ -391,12 +418,104 @@ async def get_instagram_audience_data(ctx: Context, artist_id: str) -> dict:
     
     instagram_audience_stats = data.get('obj', {})
     current_state["working_notes"][f"instagram_audience_data for artist {artist_id}"] = instagram_audience_stats
-    await ctx.set('state', current_state)
+    await ctx.store.set('state', current_state)
 
     return { f"instagram_audience_data for artist {artist_id}": instagram_audience_stats}
 
 
 
+async def get_charts(ctx: Context, artist_id: int, chart_type: str) -> dict:
+    """
+    Retrieve chart data for a given artist using Chartmetric API.
+
+    Parameters:
+    - artist_id (str): The Chartmetric artist ID.
+    - chart_type: The platform chart and sub-choice. Choose one from:
+        [
+            "spotify_viral_daily", "spotify_viral_weekly", "spotify_top_daily", "spotify_top_weekly",
+            "applemusic_top", "applemusic_daily", "applemusic_albums",
+            "itunes_top", "itunes_albums",
+            "shazam", "beatport",
+            "youtube", "youtube_tracks", "youtube_videos", "youtube_trends",
+            "amazon"
+        ]
+
+    Returns:
+    - dict: Chart entries containing album name, rank, and peak info.
+
+    Notes:
+    - Results are saved in working memory.
+    """
+
+    valid_chart_types = [
+    "spotify_viral_daily", "spotify_viral_weekly", "spotify_top_daily", "spotify_top_weekly",
+    "applemusic_top", "applemusic_daily", "applemusic_albums",
+    "itunes_top", "itunes_albums", "shazam", "beatport",
+    "youtube", "youtube_tracks", "youtube_videos", "youtube_trends", "amazon"
+    ]
+    
+    if chart_type not in valid_chart_types:
+        raise ValueError(f"Invalid chart_type '{chart_type}'. Must be one of: {valid_chart_types}")
+
+    current_state = await ctx.store.get('state')
+    print(f"value of current_state on load inside of get_chart is: {current_state}")
+
+    #https://api.chartmetric.com/api/artist/:id/:type/charts
+
+    access_token = get_chartmetric_access_token_cached()
+
+
+    print("🚀 Called get_charts with artist_id:", artist_id)
+    print("🚀 Called get_charts with access_token:", access_token)
+
+    ##shoukd make dates of the chart dynamic later
+    ##need to give chart options in function description clearly
+
+    url = f"https://api.chartmetric.com/api/artist/{artist_id}/{chart_type}/charts?since=2025-03-01&until=2025-07-04"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+
+    response = requests.get(url, headers=headers)
+
+    if not response.ok:
+        print(f"❌ Request failed with status {response.status_code}: {response.text}")
+        return {}
+        
+
+    data = response.json()
+    #print(f"data from get_charts is: {data}")
+    print("🚀 data call to get_charts successfully made!")
+
+    dataObj = data.get('obj',{})
+    #print(f"dataObj is {dataObj}")
+    dataObjEntries = dataObj.get('data',{})
+    dataObjEntries2 = dataObjEntries.get('entries',{})
+    #print(f"dataObjEntries2 is {dataObjEntries2}")
+    
+    relevant_details = []
+    for entry in dataObjEntries2:
+        print(f"entry is: {entry}")
+        stuffToSave = { "album": entry["name"], "pre-rank": entry["pre_rank"], "peak": entry["peak_rank"], "peak_day": entry["peak_date"], "rank": entry["rank"] }
+        print(f"stuff to save is: {stuffToSave}")
+        relevant_details.append(stuffToSave)
+    
+    print(f"value of relevant_dtails is: {relevant_details}")
+
+    if "working_notes" not in current_state:
+        current_state["working_notes"] = {}
+    
+    if f"charts_data for {artist_id}" not in current_state["working_notes"]:
+        current_state["working_notes"][f"charts_data for {artist_id}"] = {}
+    
+    current_state["working_notes"][f"charts_data for {artist_id}"][chart_type] = relevant_details
+    await ctx.store.set('state', current_state)
+
+    return {
+    "artist_id": artist_id,
+    "chart_data": relevant_details
+}
+
 
 
 
@@ -413,15 +532,31 @@ async def get_instagram_audience_data(ctx: Context, artist_id: str) -> dict:
 #find_artist_id_for_artist_tool = FunctionTool(fn=find_artist_id_for_artist)
 #get_instagram_audience_stats_tool = FunctionTool(fn=get_instagram_audience_stats)
 #get_similar_artists_tool = FunctionTool(fn=get_similar_artists)
+
 manager_agent = ReActAgent(
     name="ManagerAgent",
     description="Manager agent decides which other agents to use, and is decision maker",
-    system_prompt=("You are the manager agent, you decide which other agents to use and hand-off to."
-    "You decide when the answer is ready to be returned to the user"
-    ),
-    can_handoff_to=["SocialMediaDataAgent", "SimilarityAgent"]
+    system_prompt=(
+    "You are the manager agent. You do not collect data yourself. You delegate tasks to other agents.\n\n"
+    "Your responsibilities are:\n"
+    "- Receive the user’s question\n"
+    "- Decide whether StreamingChartAgent or SocialMediaDataAgent or SimilarityAgent (or two or all) should handle the request\n"
+    "+ If the question is about social media audience data (TikTok, Instagram, YouTube), use SocialMediaDataAgent."
+"+ If the question is about chart positions, chart history, or streaming rankings, use StreamingChartAgent."
+    "- Wait for their responses and evaluate whether the question has been sufficiently answered\n"
+),
+    llm=llm,
+    can_handoff_to=["SocialMediaDataAgent", "SimilarityAgent", "StreamingChartAgent"]
 )
 
+streaming_chart_agent = ReActAgent(
+    name="StreamingChartAgent",
+    description="agent to retrieve streaming chart data for the artist being researched",
+    system_prompt=("You are a research agent that retrieves streaming chart information about an artist"),
+    llm=llm,
+    tools=[get_charts, find_artist_id_for_artist],
+    can_handoff_to=["ManagerAgent", "SimilarityAgent", "SocialMediaDataAgent"]
+)
 
 
 social_media_data_agent = ReActAgent(#try with Function Agents first, change to ReAct agents if needed/performance is poor.
@@ -437,9 +572,18 @@ social_media_data_agent = ReActAgent(#try with Function Agents first, change to
     "- Your tools are only for Instagram and TikTok and Youtube data.\n"
 )
 ,
-    llm=llm,
+    llm=llmHigher,
     tools=[get_instagram_audience_data, find_artist_id_for_artist, get_tiktok_audience_data, get_youtube_audience_data],
-    can_handoff_to=["ManagerAgent", "SimilarityAgent"]#allow it to handoff to all other agents
+    can_handoff_to=["ManagerAgent", "SimilarityAgent", "StreamingChartAgent"]#allow it to handoff to all other agents
+)
+
+streaming_chart_agent = ReActAgent(
+    name="StreamingChartAgent",
+    description="agent to retrieve streaming chart data for the artist being researched",
+    system_prompt=("You are a research agent that retrieves streaming chart information about an artist"),
+    llm=llm,
+    tools=[get_charts, find_artist_id_for_artist],
+    can_handoff_to=["ManagerAgent", "SimilarityAgent", "SocialMediaDataAgent"]
 )
 
 similarity_agent = ReActAgent(
@@ -450,7 +594,7 @@ similarity_agent = ReActAgent(
     ),
     llm=llm,
     tools=[get_similar_artists, find_artist_id_for_artist],
-    can_handoff_to=["ManagerAgent", "SocialMediaDataAgent"]
+    can_handoff_to=["ManagerAgent", "SocialMediaDataAgent", "StreamingChartAgent"]
 )
 
 
@@ -459,7 +603,7 @@ similarity_agent = ReActAgent(
 
 
 
-async def main(chosen_artist):
+async def main(chosen_artist, prompt):
     #response = await workflow.run(user_msg="What is Bertie Blackman's Chartmetric artist ID?"
 #, ctx=ctx) python llamaOaAgent.py
     #chosen_artist = "Kenan Doğulu"
@@ -473,11 +617,14 @@ async def main(chosen_artist):
     f"Who are the most similar artists to {chosen_artist}? Based on sonic qualities and existing audience data, who are his closest peers?",
     f"Who is listening to artists similar to {chosen_artist}? What does the audience profile of {chosen_artist}'s peer artists look like, and where does it overlap with his?",
     f"Where can {chosen_artist} find new listeners? Which specific playlists (on Spotify, Apple Music, etc.) are crucial for reaching the fans of these similar artists?",
-    f"Who should be {chosen_artist}'s audience? Based on all the available data, what does the ideal 'extended audience' look like that we should be targeting?"
+    f"Who should be {chosen_artist}'s audience? Based on all the available data, what does the ideal 'extended audience' look like that we should be targeting?",
+    f"what time of year do {chosen_artist}'s albums perform the best in the charts?"
 ]
     overall_answers = ""
     overall_answers2 = {}
 
+    all_states = {}
+
     for (index, user_msg) in enumerate(questions):
         
         print(f"starting questions {index + 1}")
@@ -486,7 +633,7 @@ async def main(chosen_artist):
 
         #create/re-create workflow with new question as user_msg
         workflow = AgentWorkflow(
-        agents=[similarity_agent, social_media_data_agent, manager_agent],
+        agents=[similarity_agent, social_media_data_agent, manager_agent, streaming_chart_agent],
         root_agent=manager_agent.name,
         initial_state={"working_notes": {}, "user question": user_msg, "users language": "English"}
         )
@@ -494,6 +641,9 @@ async def main(chosen_artist):
         # run the workflow with context
         ctx = Context(workflow)
 
+
+
+
         handler = workflow.run(user_msg=user_msg, ctx=ctx)
         current_agent = None
         current_tool_calls = ""
@@ -551,7 +701,18 @@ async def main(chosen_artist):
                     print(f"🔨 Calling Tool: {event.tool_name}")
                     print(f"  With arguments: {event.tool_kwargs}")
         
-        print(f"overall_answers is: {overall_answers}")
+        state = await ctx.store.get("state")
+        all_states[f"Q{index+1}"] = {
+            "question": user_msg,
+            "state": state
+            }
+        
+    print(f"overall_answers is: {overall_answers}")
+
+    #final_state = await ctx.store.get("state")
+        
+    with open(f"ctx_memory_all_answers.json", "w") as f:
+        json.dump(all_states, f, indent=2)
     
     #can then keep just the last thought of each question index
 
@@ -572,10 +733,10 @@ async def main(chosen_artist):
     total_tokens2 = count_tokens(flattened)
     print(f"Total tokens of overall_answers2: {total_tokens2}")
 
-    with open(f"overall_answers.txt","w", encoding="utf-8") as file:
+    with open(f"overall_answersGemini.txt","w", encoding="utf-8") as file:
         file.write(overall_answers)
     
-    with open(f"overall_answers2.txt","w", encoding="utf-8") as file:
+    with open(f"overall_answers2Gemini.txt","w", encoding="utf-8") as file:
         json.dump(overall_answers2, file, ensure_ascii=False, indent=2)
 
 
@@ -621,68 +782,77 @@ RAW DATA:
 """
 
     prompto3 = f"""
-You are a senior music strategist preparing a 2-page audience intelligence brief for artist **{chosen_artist}**.
-
-You have been given raw data from Instagram, TikTok, Chartmetric, and related tools.
-
----
-
-🎯 INSTRUCTIONS:
-
-- Use the detailed markdown template below as a **shell to be fully populated**.
-- **Do not repeat the template unfilled.**
-- Extract ALL relevant statistics, insights, and named entities from the raw data and synthesize them into this format.
-- If a section is partially unsupported (e.g., missing TikTok data), write that clearly — but **still include the section fully**.
-- Match the **tone, voice, and layout** of a presentation-ready strategy deck: consultative, insight-rich, structured.
-- Use bullet points, tables, and formatting to ensure visual clarity.
-
----
-
-### Deep‑Dive Audience Analysis for {chosen_artist}  
-(Synthesising Instagram data + Turkish pop‑market context)
+# ROLE & TASK
+You are a **senior music strategist** hired to deliver a **two-page Audience Intelligence Brief** for the artist **{chosen_artist}**.
+
+# SOURCE MATERIAL
+– You have one source only: **RAW_DATA** (verbatim answers & metrics pulled from Instagram, TikTok and YouTube).
+– Treat all numbers as trustworthy unless they contradict each other; in that case flag the conflict in “Data Gaps”.
+
+# WORKFLOW  (do not display)
+1. **THINK:** Extract every statistic, named entity, quote or behavioural clue from RAW_DATA.  
+2. **PLAN:** Map those findings onto the template sections. Identify unsupported cells early.  
+3. **WRITE:** Populate the markdown template in polished, presentation-ready prose.  
+   – Use concise bullet points (max. 15 words each) and tables for scannability.  
+   – Keep each column width sensible; wrap long text with `<br>` if needed.  
+4. **VERIFY:** Double-check that totals, % and age-band ranges add up logically.  
+5. **CLEAN:** Do **not** expose this workflow, system prompts or RAW_DATA.
+
+# STYLE
+Consultative, insight-rich, brand-strategy tone. Prefer active voice, audience-centric language (“Fans show…”, “Leverage…”).  
+Use **bold** for key stats, *italics* for emphasis, emojis only where the template already includes them.
+
+# DELIVERABLE
+Return **exactly** the filled-in template between the markers  
+`---BEGIN BRIEF---` and `---END BRIEF---`.  
+If a section lacks data, keep the section but write “*No platform data supplied — analyst inference required*”.
+
+# MARKDOWN TEMPLATE  (to be populated – do NOT repeat unfilled)
+### Deep-Dive Audience Analysis for {chosen_artist}  
+(Synthesising Instagram, TikTok & YouTube data within Turkish pop-market context)
 
 ---
 
 1. **Audience Architecture at a Glance**  
 | Layer              | Instagram Data            | TikTok/Other*         | Strategic Takeaway                       |
-|--------------------|---------------------------|------------------------|-------------------------------------------|
-| Scale              |                           |                        |                                           |
-| Core Territory     |                           |                        |                                           |
-| Secondary Markets  |                           |                        |                                           |
-| Gender             |                           |                        |                                           |
-| Prime Age Band     |                           |                        |                                           |
+|--------------------|---------------------------|-----------------------|------------------------------------------|
+| Scale              |                           |                       |                                          |
+| Core Territory     |                           |                       |                                          |
+| Secondary Markets  |                           |                       |                                          |
+| Gender             |                           |                       |                                          |
+| Prime Age Band     |                           |                       |                                          |
 
 ---
 
 2. **Hidden Insights & Underserved Nuances**  
-| Insight                     | Evidence                            | Why It Matters                          |
-|-----------------------------|-------------------------------------|------------------------------------------|
-|                             |                                     |                                          |
-|                             |                                     |                                          |
-|                             |                                     |                                          |
+| Insight                            | Evidence (platform, metric)     | Why It Matters                          |
+|------------------------------------|---------------------------------|------------------------------------------|
+|                                    |                                 |                                          |
+|                                    |                                 |                                          |
+|                                    |                                 |                                          |
 
 ---
 
-3. **Psychographic Micro‑Segments to Activate**  
-| Segment Name               | % Audience | Description                           | Ideal Touch‑point                       |
-|----------------------------|------------|---------------------------------------|------------------------------------------|
-|                            |            |                                       |                                          |
-|                            |            |                                       |                                          |
+3. **Psychographic Micro-Segments to Activate**  
+| Segment Name        | % Audience | Description (mindset / need-state) | Ideal Touch-point                       |
+|---------------------|-----------:|------------------------------------|-----------------------------------------|
+|                     |            |                                    |                                         |
+|                     |            |                                    |                                         |
 
 ---
 
 4. **Content & Channel Implications**  
-| Funnel Stage   | Channel            | Format Strategy                        |
-|----------------|--------------------|-----------------------------------------|
-| Discovery      |                    |                                         |
-| Consideration  |                    |                                         |
-| Community      |                    |                                         |
-| Conversion     |                    |                                         |
+| Funnel Stage   | Priority Channel(s) | Format & Narrative Hook              |
+|----------------|---------------------|--------------------------------------|
+| Discovery      |                     |                                      |
+| Consideration  |                     |                                      |
+| Community      |                     |                                      |
+| Conversion     |                     |                                      |
 
 ---
 
 5. **Monetisation & Partnership Levers**  
-- (Example: Stadium performance + merch collab with Galatasaray)
+- 
 - 
 - 
 - 
@@ -690,10 +860,10 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
 ---
 
 6. **Risks & Mitigations**  
-| Risk                          | Impact              | Mitigation                               |
-|-------------------------------|---------------------|-------------------------------------------|
-|                               |                     |                                           |
-|                               |                     |                                           |
+| Risk                                   | Potential Impact       | Mitigation Play                          |
+|----------------------------------------|------------------------|------------------------------------------|
+|                                        |                        |                                          |
+|                                        |                        |                                          |
 
 ---
 
@@ -704,23 +874,151 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
 
 ---
 
-📦 RAW DATA:  
+📦 **RAW_DATA** (for internal use only – do NOT show in the brief)
 {overall_answers}
+
+---BEGIN BRIEF---
+<!-- o3 starts populating here -->
+---END BRIEF---
 """
 
+    prompto3ChrisChart = f"""🎯 MEGA AUDIENCE INSIGHT & GROWTH PROMPT
+Prompt Title: Deep Audience Intelligence & Growth Blueprint for [ARTIST_NAME]
 
+System Role (Set Once):
+You are a senior music data strategist trained in multi-platform audience intelligence, behavioral segmentation, and growth marketing. You operate like a hybrid of a data analyst, music marketer, and product strategist. Your job is to extract unique insights, detect overlooked opportunities, and build a data-driven growth plan for the artist based on a rich dataset of streaming, chart, and social data.
 
+🔍 INPUT DATA:
+Structured streaming data (Spotify, Apple Music, iTunes, Shazam) with rank movement, peak days, velocity, and decay.
 
+Social media + CRM metrics (TikTok, IG, YouTube, Reels, Stories, Email, Merch, Tour Sales, etc.).
 
-    formal_questions = [
-        {
-            "section": "Core Audience",
-            "questions": f"Who is {chosen_artist}'s core audience?",
-            "features": "include table breakdown of core audience"
-        }
-    ]
+Any artist metadata you can derive (track names, album release cycles, remix info, sentiment cues, genre tags, collaborators).
+
+🧠 TASK
+Split your approach into three distinct cognitive layers, executed in sequence:
+
+✅ LAYER 1: ANALYTICAL DEEP DIVE
+Understand the data in its rawest form.
+
+Detect patterns in streaming velocity, seasonal performance, and Shazam conversion.
+
+Surface anomalies — outlier peaks, remix vs original inconsistencies, platform skews.
+
+Build segmentations across:
+
+Demographics (inferred via geo and platform)
+
+Behavioral (engagement, replay rate, completion, skip/save behavior)
+
+Content type affinity (e.g., club mix vs acoustic vs emotional lyrics)
+
+Identify:
+
+Top 3–5 most influential formats (content, platform, track type)
+
+2–3 examples of platform crossover lags (e.g., Shazam peak → Spotify delay)
+
+Fanbase decay curves (where and when attention drops off)
+
+🧠 LAYER 2: STRATEGIC REASONING
+Generate hypotheses and opportunity clusters.
+
+Audience Gaps:
+
+Where is the artist underperforming?
+
+What similar audiences (adjacent genres, demos, cities) are reachable?
+
+Cluster Fans into Personas based on behavior + geo:
+Example labels:
+
+“Shazam-driven club-goers in Southern Europe”
+
+“Loyal iTunes buyers over 40 in Central Asia”
+
+“Spotify Weekly repeaters with remix preference in Berlin”
+
+For each persona cluster, answer:
+
+What drives their behavior?
+
+Where can we find more like them?
+
+Which platform(s) matter most?
+
+Propose 3–4 testable hypotheses about:
+
+Timing strategies
+
+Collaboration types
+
+Format performance
 
-    ##cut down Thought input, so only last one returned with Answer from
+Messaging tones (e.g. romantic, nostalgic, rebellious)
+
+🚀 LAYER 3: GROWTH & CAMPAIGN STRATEGY
+Turn intelligence into a tactical plan.
+
+Recommend:
+
+3 platform strategies, tailored to audience types (e.g. TikTok + Reels = Hook virality vs Apple = intimacy/purchase)
+
+3 content types likely to resonate with segments (e.g. stripped vocals for Gen Z on IG vs remix packs for DJs)
+
+2 partnership ideas — either influencer-led, playlist curators, or collab artists with overlapping fanbases
+
+Suggest distribution timing:
+
+What day, week, and month clusters have historically driven best results?
+
+Layer this with social engagement cycles.
+
+Design 1 bold, data-informed “Big Bet” campaign:
+
+Could be a geo-targeted drop, genre mashup collab, remix competition, or a multi-platform narrative series.
+
+🧪 OUTPUT FORMAT:
+markdown
+Copy
+Edit
+# Artist Audience Intelligence & Growth Blueprint: [Artist Name]
+
+## 1. Overview
+Short summary of overall patterns, growth arcs, and platform behaviors.
+
+## 2. Key Segments
+- Persona 1: “...” → Description, platforms, geo, behavior
+- Persona 2: ...
+- Persona 3: ...
+
+## 3. Strategic Observations
+- Opportunity gaps
+- Surprising over/under performance
+- Hypotheses
+
+## 4. Marketing Recommendations
+### A. Platform Strategy
+[List of 3, each with logic and examples]
+
+### B. Content Types to Emphasize
+[List of 3, with reasoning per segment]
+
+### C. Influencer/Partnership Strategy
+[2 ideas with audience alignment logic]
+
+## 5. Big Bet Growth Campaign
+Title + concept + rationale
+
+Your data is {overall_answers}
+"""
+
+
+
+
+
+    final_prompt = prompt + f"This report is about artist {chosen_artist}" + f"your sole data source is: {overall_answers}"
+    ##that should ensure that whatever prompt is entered, the correct artist and data source is still passed down.
 
 
     #count tokens anyway, for later usage:
@@ -729,8 +1027,6 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
 
     max_tokens = 16384 - total_tokens - 200
 
-    response = openai.chat.completions.create(
-    model="gpt-3.5-turbo-0125",  # or "o3-pro" if enabled
     messages=[
         {
             "role": "system",
@@ -738,15 +1034,51 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
         },
         {
             "role": "user",
-            "content": prompto3
+            "content": final_prompt
+        }
+    ]
+
+    response = client.responses.create(
+    model="o3",
+    input=[
+        {
+            "role": "developer",
+            "content": [
+                {
+                    "type": "input_text",
+                    "text": (
+                        "You are a precise music industry data analyst. "
+                        "Be structured, factual, and preserve all stats given."
+                    )
+                }
+            ]
+        },
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "input_text",
+                    "text": prompto3
+                }
+            ]
         }
     ],
-    temperature=0.2,  # low temp helps avoid paraphrasing
-    max_tokens=4096   # adjust based on your expected output size
-)
+    text={
+        "format": {
+            "type": "text"
+        }
+    },
+    reasoning={
+        "effort": "medium",
+        "summary": "auto"
+    },
+    tools=[],
+    store=True
+    )
+
     
-    print(response.choices[0].message.content)
-    two_pager_document = response.choices[0].message.content
+    print(response.output_text)
+    two_pager_document = response.output_text
 
     #add generation of two_pager_part2
 
@@ -754,7 +1086,7 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
     #for question in formal_questions:
     print(f"overall_answer2 is {overall_answers2}")
 
-    with open(f"{chosen_artist}o32.txt","w", encoding="utf-8") as file:
+    with open(f"{chosen_artist}.txt","w", encoding="utf-8") as file:
         file.write(two_pager_document)
     
     return two_pager_document
@@ -762,7 +1094,7 @@ You have been given raw data from Instagram, TikTok, Chartmetric, and related to
 
 demo = gr.Interface(
     fn=main,
-    inputs="text",
+    inputs=["text", "text"], #one for artist_name, other for prompt
     outputs="text",
     title="artist report generator",
     description="generate report for artist"

appOld.py

@@ -0,0 +1,789 @@
+#try using existing logic, but add ctx/memory that llamindex allows
+
+#do autonomous llamagents
+
+from llama_index.core.tools import FunctionTool
+from llama_index.llms.openai import OpenAI
+from dotenv import load_dotenv
+#from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.core.agent.workflow import AgentWorkflow, FunctionAgent, ReActAgent #can also import ReActAgent or FunctionAgent from this 
+from llama_index.core.tools import FunctionTool
+from llama_index.core.workflow import Context
+import os
+from functools import lru_cache
+import asyncio
+import requests
+from llama_index.core.agent.workflow import (
+    AgentInput,
+    AgentOutput,
+    ToolCall,
+    ToolCallResult,
+    AgentStream,
+)
+import openai
+import tiktoken
+import requests
+import json
+import gradio as gr
+
+
+
+#from llama_index.llms.google_gemini import GoogleGenAI
+#from google.genai import types
+
+load_dotenv()
+
+#llm = OpenAI(model="gpt-4o-mini")
+
+llm = OpenAI(
+    model="gpt-4o-mini",  # or "gpt-3.5-turbo"
+    api_key=os.getenv('OPENAI_API_KEY'),  # You can also set this via the OPENAI_API_KEY environment variable
+    streaming=True    
+)
+
+openai.api_key = os.getenv("OPENAI_API_KEY")
+#use gemini
+
+#set api_key in .env for gemini
+#llmGemini = GoogleGenAI(model="gemini-2.5-pro")
+
+#can use search as AI
+#google_search_tool = types.Tool(
+    #google_search=types.GoogleSearch()
+#)#should be able to pass as tool?
+
+
+
+@lru_cache(maxsize=1)
+def get_chartmetric_access_token_cached() -> str | None:
+    print("🔑 Fetching new Chartmetric token")
+    return get_chartmetric_access_token_with_refresh()
+
+#@function_tool
+def get_chartmetric_access_token_with_refresh() -> str or None:
+    """
+    Retrieves an access token from Chartmetric. You need to use this before you can use any other function involving chartmetric
+    
+    """
+    #current_state = await ctx.get('state')
+
+
+    refresh_token = 'izPNc1uMM7A13dvWGs0Gij3rfMTKV0K24ADFfcHviaOPWxc35ZsNuYqlQNb5BVyG'
+
+    endpoint = 'https://api.chartmetric.com/api/token'
+    headers = {
+        'Content-Type': 'application/json'
+    }
+    payload = {
+        'refreshtoken': refresh_token
+    }
+
+    try:
+        response = requests.post(endpoint, headers=headers, json=payload)
+        if not response.ok:
+            raise Exception(f"Token request failed: {response.status_code} {response.reason}")
+        
+        data = response.json()
+        print("Access token retrieved:", data.get('token'),{})
+
+       #if "working_notes" not in current_state:
+            #current_state["working_notes"] = {}
+        
+        access_token = data.get('token')# This is your bearer token for future API calls
+        #current_state["working_notes"]["access_token"] = access_token
+
+        #await ctx.set("state", current_state)
+        return access_token
+
+    except Exception as e:
+        print("Error retrieving Chartmetric access token:", str(e))
+        return None
+
+
+
+#@function_tool
+async def find_artist_id_for_artist(ctx: Context, artist_name: str) -> int:
+    """
+    Retrieves artist_id for the artist you want to search on the chartmetric system .
+
+    
+    """
+    current_state = await ctx.get('state')
+ 
+    access_token = get_chartmetric_access_token_cached()
+
+    url = f'https://api.chartmetric.com/api/search?q={artist_name}&type=artists'
+
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+    
+    try: 
+        response = requests.get(url, headers=headers)
+        
+        if not response.ok:
+            raise Exception(f"artist_id request failed: {response.status_code} {response.reason}")
+
+        data = response.json()
+        print("Raw response data:", data)
+        
+        # Safely access first matched artist
+        artists = data.get("obj", {}).get("artists", [])
+        
+        if not artists:
+            print(f"No artists found matching '{artist_name}'.")
+            return None
+        
+        artist_id = artists[0].get('id',{})
+
+        # Update state and persist it
+        if "working_notes" not in current_state:
+            current_state["working_notes"] = {}
+
+        current_state["working_notes"][artist_name] = artist_id
+        await ctx.set("state", current_state)  # 🟢 Save the updated state
+
+        return artist_id
+
+    except Exception as e:
+        print("Error retrieving Chartmetric artist_id:", str(e))
+        return None
+
+#@function_tool
+async def get_similar_artists(ctx: Context, artist_id: int) -> dict:
+    """
+    Retrieve a list of similar artists from Chartmetric based on a given artist ID.
+
+    Parameters:
+    - artist_id (int): The Chartmetric artist ID.
+
+    Returns:
+    - dict: A dictionary of similar artists (up to 5).
+
+    Notes:
+    - Results are stored in working memory under "similar_artists".
+    """
+    current_state = await ctx.get('state')
+
+    access_token = get_chartmetric_access_token_cached()  # Assuming this is defined elsewhere
+    print("access_token for get_similar_artists api call obatined!")
+
+    url = f"https://api.chartmetric.com/api/artist/{artist_id}/relatedartists?limit=3"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+
+    try:
+        response = requests.get(url, headers=headers)
+        if not response.ok:
+            raise Exception(f"Related artists request failed: {response.status_code} {response.reason}")
+
+        data = response.json()
+        print("data returned from get_similar_artists is:", data)
+
+        
+        similar_artists = data.get('obj', {})
+
+        if "working_notes" not in current_state:
+            current_state["working_notes"] = {}
+        
+        current_state["working_notes"]["similar_artists"] = similar_artists
+        await ctx.set('state', current_state)
+
+        return similar_artists
+
+    except Exception as e:
+        print("Error retrieving similar artists:", str(e))
+        return None
+
+
+async def get_youtube_audience_data(ctx: Context, artist_id: str) -> dict:
+    """
+    Retrieve Youtube audience data for a given artist, using Chartmetric API.
+
+    Parameters:
+    - artist_id (int): The Chartmetric artist ID.
+
+    Returns:
+    - dict: A dictionary of similar artists (up to 5).
+
+    Notes:
+    - Results are saved in working memory.
+    """
+    current_state = await ctx.get('state')
+    
+    access_token = get_chartmetric_access_token_cached()
+
+
+    print("🚀 Called get_Youtube with artist_id:", artist_id)
+    print("🚀 Called get_Youtube with access_token:", access_token)
+
+
+    url = f"https://api.chartmetric.com/api/artist/{artist_id}/youtube-audience-stats"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+
+    response = requests.get(url, headers=headers)
+
+    if not response.ok:
+        if response.status_code == 404:
+            print(f"⚠️ No YouTube data found for artist {artist_id}")
+            return {}
+        
+
+    data = response.json()
+    print(f"data from get_Youtube is: {data}")
+
+    dataObj = data.get('obj',{})
+
+    print("Info from get_tiktok_audience_data is:", dataObj)
+
+    compressed_notable_followers = []
+    for follower in dataObj["notable_subscribers"]:
+        #pprint(f"follower in dataObj is: {follower}")
+
+        new_data = {}
+        
+        new_data["custom_name"] = follower.get("custom_name", {})
+        new_data["subscribers"] = follower["subscribers"] 
+        new_data["engagements"] = follower["engagements"] 
+
+        compressed_notable_followers.append(new_data)
+    
+
+    dict_to_return = {"top_countries": dataObj["top_countries"], "audience_gender_by_age": dataObj["audience_genders_per_age"], "audience_genders": dataObj["audience_genders"], "top_followers": compressed_notable_followers,
+      "subscribers": dataObj["subscribers"], "avg_likes_per_post": dataObj["avg_likes_per_post"], "avg_commments_per_post": dataObj["avg_commments_per_post"],
+      "engagement_rate": dataObj["engagement_rate"]
+    
+     }
+
+    if "working_notes" not in current_state:
+        current_state["working_notes"] = {}
+    
+    youtube_audience_stats = dict_to_return
+    print(f"youtube_audience_stats are: {youtube_audience_stats}")
+    current_state["working_notes"][f"youtube_audience_data for artist {artist_id}"] = youtube_audience_stats
+    await ctx.set('state', current_state)
+
+    return { f"youtube_audience_data for artist {artist_id}": youtube_audience_stats}
+
+
+
+
+
+
+
+async def get_tiktok_audience_data(ctx: Context, artist_id: str) -> dict:
+    """
+    Retrieve TikTok audience data for a given artist using Chartmetric API.
+
+    Parameters:
+    - artist_id (str): The Chartmetric artist ID.
+
+    Returns:
+    - dict: Instagram audience breakdown.
+
+    Notes:
+    - Results are saved in working memory.
+    """
+    current_state = await ctx.get('state')
+
+    access_token = get_chartmetric_access_token_cached()
+
+
+    print("🚀 Called get_tiktok_audience_data with artist_id:", artist_id)
+    print("🚀 Called get_tiktok_audience_data with access_token:", access_token)
+
+    url = f"https://api.chartmetric.com/api/artist/{artist_id}/tiktok-audience-stats"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+
+    response = requests.get(url, headers=headers)
+
+    if not response.ok:
+        raise Exception(f"API request failed: {response.status_code} {response.reason}")
+
+    data = response.json()
+    #print(f"data from get_tiktok_audience_data is: {data}")
+
+    dataObj = data.get('obj',{})
+
+    #print("Info from get_tiktok_audience_data is:", dataObj)
+
+    compressed_notable_followers = []
+    for follower in dataObj.get("notable_followers", []):
+        #print(f"follower in dataObj is: {follower}")
+
+        new_data = {}
+        new_data["username"] = follower["username"]
+        new_data["followers"] = follower["followers"] 
+        new_data["engagement"] = follower["engagements"] 
+
+        compressed_notable_followers.append(new_data)
+    
+
+    dict_to_return = {"top_countries": dataObj["top_countries"], "audience_gender_by_age": dataObj["audience_genders_per_age"], "audience_genders": dataObj["audience_genders"], "top_followers": compressed_notable_followers,
+      "followers": dataObj["followers"], "avg_likes_per_post": dataObj["avg_likes_per_post"], "avg_commments_per_post": dataObj["avg_commments_per_post"],
+      "engagement_rate": dataObj["engagement_rate"]
+    
+     }
+    if "working_notes" not in current_state:
+        current_state["working_notes"] = {}
+    
+    tiktok_audience_stats = dict_to_return
+    print(f"tiktok_audience_data are: {tiktok_audience_stats}")
+    current_state["working_notes"][f"tiktok_audience_data for artist {artist_id}"] = tiktok_audience_stats
+    await ctx.set('state', current_state)
+
+    return { f"tiktok_audience_data for artist {artist_id}": tiktok_audience_stats}
+
+    #choose which parts to return
+
+
+
+
+
+
+#@function_tool
+async def get_instagram_audience_data(ctx: Context, artist_id: str) -> dict:
+    """
+    Retrieve Instagram audience statistics for a given artist using Chartmetric.
+
+    Parameters:
+    - artist_id (str): The Chartmetric artist ID.
+
+    Returns:
+    - dict: Instagram audience breakdown.
+
+    Notes:
+    - Results are saved in working memory.
+    """
+    #perhaps just have it get access_token inside here
+    #access_token = get_chartmetric_access_token_with_refresh()
+
+    current_state = await ctx.get('state')
+
+    access_token = get_chartmetric_access_token_cached()
+
+
+    print("🚀 Called get_instagram_audience_stats with artist_id:", artist_id)
+    print("🚀 Called get_instagram_audience_stats with access_token:", access_token)
+    
+    url = f"https://api.chartmetric.com/api/artist/{artist_id}/instagram-audience-stats"
+    headers = {
+        "Authorization": f"Bearer {access_token}"
+    }
+
+    response = requests.get(url, headers=headers)
+
+    if not response.ok:
+        raise Exception(f"API request failed: {response.status_code} {response.reason}")
+
+    data = response.json()
+    print(f"data from api call is: {data}")
+    print("Info from platform Instagram is:", data.get("obj"))
+    
+
+    if "working_notes" not in current_state:
+        current_state["working_notes"] = {}
+    
+    instagram_audience_stats = data.get('obj', {})
+    current_state["working_notes"][f"instagram_audience_data for artist {artist_id}"] = instagram_audience_stats
+    await ctx.set('state', current_state)
+
+    return { f"instagram_audience_data for artist {artist_id}": instagram_audience_stats}
+
+
+
+
+
+
+
+#and that code which allows logging of every step of the memory/thought process
+
+#keep teh cahce of chartmetric api, attached to function that gets api_key, which is inserted into each relevant api
+#find_artist_id_for_artist_tool = FunctionTool.from_function(find_artist_id_for_artist)
+#get_instagram_audience_stats_tool = FunctionTool.from_function(get_instagram_audience_stats)
+#get_similar_artists = FunctionTool.from_function(get_similar_artists)
+
+
+# Wrap your function
+#find_artist_id_for_artist_tool = FunctionTool(fn=find_artist_id_for_artist)
+#get_instagram_audience_stats_tool = FunctionTool(fn=get_instagram_audience_stats)
+#get_similar_artists_tool = FunctionTool(fn=get_similar_artists)
+manager_agent = ReActAgent(
+    name="ManagerAgent",
+    description="Manager agent decides which other agents to use, and is decision maker",
+    system_prompt=("You are the manager agent, you decide which other agents to use and hand-off to."
+    "You decide when the answer is ready to be returned to the user"
+    ),
+    can_handoff_to=["SocialMediaDataAgent", "SimilarityAgent"]
+)
+
+
+
+social_media_data_agent = ReActAgent(#try with Function Agents first, change to ReAct agents if needed/performance is poor.
+    name="SocialMediaDataAgent",
+    description="agent to source data about artists from social media data, using chartmetric api",
+    system_prompt=(
+    "You are a research agent that uses social media data to analyze artist audiences via Chartmetric.\n"
+    "- Always use **both** Instagram and TikTok and Youtube data as your default behavior when analyzing artists.\n"
+    "- Do NOT choose one over the other unless explicitly told to focus on one.\n"
+    "- Always call 'get_instagram_audience_stats' AND 'get_tiktok_audience_data' AND 'get_youtube_audience_data' when gathering audience data.\n"
+    "- Do NOT assume artist names. Only use 'find_artist_id_for_artist' with real artist names provided by the user.\n"
+    "- If the user needs information about similar artists, HAND OFF to the SimilarityAgent — do NOT attempt it yourself.\n"
+    "- Your tools are only for Instagram and TikTok and Youtube data.\n"
+)
+,
+    llm=llm,
+    tools=[get_instagram_audience_data, find_artist_id_for_artist, get_tiktok_audience_data, get_youtube_audience_data],
+    can_handoff_to=["ManagerAgent", "SimilarityAgent"]#allow it to handoff to all other agents
+)
+
+similarity_agent = ReActAgent(
+    name="SimilarityAgent",
+    description="agent to find similar artists to the artist being research, using chartmetric api",
+    system_prompt=("You are a research agent that looks for similar artists to the artist you are researching, in order to understand how the artist can copy the growth of similar artists who are larger."
+    "you can handoff to SocialMediaDataAgent, in order to find information about the followers of similar artists"
+    ),
+    llm=llm,
+    tools=[get_similar_artists, find_artist_id_for_artist],
+    can_handoff_to=["ManagerAgent", "SocialMediaDataAgent"]
+)
+
+
+
+
+
+
+
+async def main(chosen_artist):
+    #response = await workflow.run(user_msg="What is Bertie Blackman's Chartmetric artist ID?"
+#, ctx=ctx) python llamaOaAgent.py
+    #chosen_artist = "Kenan Doğulu"
+
+    questions = [
+    f"Who are {chosen_artist}'s fans and what are their core demographics?",
+    f"Where are {chosen_artist}'s fans located: which countries and cities have the highest concentration of my listeners?",
+    f"What are the broader interests of {chosen_artist}'s fans beyond his music? What TV shows, films, or books do they consume? What are their other lifestyle affinities (e.g., sports, fashion, art, hobbies)?",
+    f"Where do {chosen_artist}'s fans gather online? What specific online communities, like subreddits or forums, are they active in?",
+    f"How can {chosen_artist} best reach his fans? Based on their online behaviour and interests, what are the most effective channels to engage them?",
+    f"Who are the most similar artists to {chosen_artist}? Based on sonic qualities and existing audience data, who are his closest peers?",
+    f"Who is listening to artists similar to {chosen_artist}? What does the audience profile of {chosen_artist}'s peer artists look like, and where does it overlap with his?",
+    f"Where can {chosen_artist} find new listeners? Which specific playlists (on Spotify, Apple Music, etc.) are crucial for reaching the fans of these similar artists?",
+    f"Who should be {chosen_artist}'s audience? Based on all the available data, what does the ideal 'extended audience' look like that we should be targeting?"
+]
+    overall_answers = ""
+    overall_answers2 = {}
+
+    for (index, user_msg) in enumerate(questions):
+        
+        print(f"starting questions {index + 1}")
+
+        overall_answers2[index] = {"Thoughts": "", "Answer": ""}
+
+        #create/re-create workflow with new question as user_msg
+        workflow = AgentWorkflow(
+        agents=[similarity_agent, social_media_data_agent, manager_agent],
+        root_agent=manager_agent.name,
+        initial_state={"working_notes": {}, "user question": user_msg, "users language": "English"}
+        )
+        
+        # run the workflow with context
+        ctx = Context(workflow)
+
+        handler = workflow.run(user_msg=user_msg, ctx=ctx)
+        current_agent = None
+        current_tool_calls = ""
+
+        
+        
+        async for event in handler.stream_events():
+            if (
+                hasattr(event, "current_agent_name")
+                and event.current_agent_name != current_agent
+               ):
+               current_agent = event.current_agent_name
+               print(f"\n{'='*50}")
+               print(f"🤖 Agent: {current_agent}")
+               print(f"{'='*50}\n")
+            
+            elif isinstance(event, AgentOutput):
+                content = event.response.content.strip()
+                print("📤 Output:", content)
+
+        # New logic: extract Thought and Answer from any position
+                clean_answer_combined = ""
+                thought, answer = None, None
+                
+                if "Thought:" in content:
+                    if "Answer:" in content:
+                        thought = content.split("Thought:")[1].split("Answer:")[0].strip()
+                    
+                    else:
+                        thought = content.split("Thought:")[1].strip()
+                        overall_answers2[index]["Thoughts"] += "\n" + thought
+                        clean_answer_combined += f"🧠 Thought: {thought}\n"
+                    
+                if "Answer:" in content:
+                    answer = content.split("Answer:")[-1].strip()
+                    overall_answers2[index]["Answer"] = answer
+                    clean_answer_combined += f"✅ Answer: {answer}\n"
+
+                if clean_answer_combined:
+                    question_header = f"\n### Q{index + 1}: {user_msg}\n"
+                    overall_answers += question_header + clean_answer_combined + "\n"
+                
+                # If either Thought or Answer was captured, append to overall_answers
+       
+                if event.tool_calls:
+                    print(
+                        "🛠️  Planning to use tools:",
+                        [call.tool_name for call in event.tool_calls],
+                        )
+                elif isinstance(event, ToolCallResult):
+                    print(f"🔧 Tool Result ({event.tool_name}):")
+                    print(f"  Arguments: {event.tool_kwargs}")
+                    print(f"  Output: {event.tool_output}")
+                elif isinstance(event, ToolCall):
+                    print(f"🔨 Calling Tool: {event.tool_name}")
+                    print(f"  With arguments: {event.tool_kwargs}")
+        
+        print(f"overall_answers is: {overall_answers}")
+    
+    #can then keep just the last thought of each question index
+
+    def count_tokens(text, model="gpt-4o"):
+        encoding = tiktoken.encoding_for_model(model)
+        return len(encoding.encode(text))
+    
+    total_tokens = count_tokens(overall_answers)
+    print(f"Total tokens of overall_answers: {total_tokens}")
+
+    
+# Build a single string
+    flattened = "\n\n".join(
+        f"Q{idx + 1}: {qa['Thoughts']}\n{qa['Answer']}" 
+        for idx, qa in overall_answers2.items()
+    )
+
+    total_tokens2 = count_tokens(flattened)
+    print(f"Total tokens of overall_answers2: {total_tokens2}")
+
+    with open(f"overall_answers.txt","w", encoding="utf-8") as file:
+        file.write(overall_answers)
+    
+    with open(f"overall_answers2.txt","w", encoding="utf-8") as file:
+        json.dump(overall_answers2, file, ensure_ascii=False, indent=2)
+
+
+
+    #now send overall_answers to LLM
+    prompt2 = f"""You need to assemble a document on the artist {chosen_artist} in the following structure
+    
+    *SECTION TITLE* - Who is {chosen_artist}'s audience?
+    
+    *SUB-SECTION HEADER* - 1. Core Profile
+                           *answer using Q1 content*  
+    
+    *SUB-SECTION HEADER* - 2. What They Love (Beyond the Music)
+                           *answer using Q2 content*
+    
+    *SUB-SECTION HEADER* - 3. Audience Segments Identified
+                           *answer using Q3 content*
+
+    *SUB-SECTION HEADER* - 4. Where They Hang Out Online
+                           *answer using Q4 content*
+    
+    You should assemble this document using the following data: {overall_answers}
+    
+    """
+
+    prompt = f"""
+You are assembling a detailed audience profile for artist {chosen_artist}.
+
+IMPORTANT:
+- You MUST preserve all statistics from the raw data.
+- Do NOT paraphrase away key numbers like city %s, gender splits, or country breakdowns.
+- Present these in structured markdown.
+
+SECTION: Who is {chosen_artist}'s audience?
+
+1. Core Profile (use Q1)
+2. What They Love (use Q2)
+3. Audience Segments Identified (use Q3)
+4. Where They Hang Out Online (use Q4)
+
+RAW DATA:
+{overall_answers}
+"""
+
+    prompto3 = f"""
+You are a senior music strategist preparing a 2-page audience intelligence brief for artist **{chosen_artist}**.
+
+You have been given raw data from Instagram, TikTok, Chartmetric, and related tools.
+
+---
+
+🎯 INSTRUCTIONS:
+
+- Use the detailed markdown template below as a **shell to be fully populated**.
+- **Do not repeat the template unfilled.**
+- Extract ALL relevant statistics, insights, and named entities from the raw data and synthesize them into this format.
+- If a section is partially unsupported (e.g., missing TikTok data), write that clearly — but **still include the section fully**.
+- Match the **tone, voice, and layout** of a presentation-ready strategy deck: consultative, insight-rich, structured.
+- Use bullet points, tables, and formatting to ensure visual clarity.
+
+---
+
+### Deep‑Dive Audience Analysis for {chosen_artist}  
+(Synthesising Instagram data + Turkish pop‑market context)
+
+---
+
+1. **Audience Architecture at a Glance**  
+| Layer              | Instagram Data            | TikTok/Other*         | Strategic Takeaway                       |
+|--------------------|---------------------------|------------------------|-------------------------------------------|
+| Scale              |                           |                        |                                           |
+| Core Territory     |                           |                        |                                           |
+| Secondary Markets  |                           |                        |                                           |
+| Gender             |                           |                        |                                           |
+| Prime Age Band     |                           |                        |                                           |
+
+---
+
+2. **Hidden Insights & Underserved Nuances**  
+| Insight                     | Evidence                            | Why It Matters                          |
+|-----------------------------|-------------------------------------|------------------------------------------|
+|                             |                                     |                                          |
+|                             |                                     |                                          |
+|                             |                                     |                                          |
+
+---
+
+3. **Psychographic Micro‑Segments to Activate**  
+| Segment Name               | % Audience | Description                           | Ideal Touch���point                       |
+|----------------------------|------------|---------------------------------------|------------------------------------------|
+|                            |            |                                       |                                          |
+|                            |            |                                       |                                          |
+
+---
+
+4. **Content & Channel Implications**  
+| Funnel Stage   | Channel            | Format Strategy                        |
+|----------------|--------------------|-----------------------------------------|
+| Discovery      |                    |                                         |
+| Consideration  |                    |                                         |
+| Community      |                    |                                         |
+| Conversion     |                    |                                         |
+
+---
+
+5. **Monetisation & Partnership Levers**  
+- (Example: Stadium performance + merch collab with Galatasaray)
+- 
+- 
+- 
+
+---
+
+6. **Risks & Mitigations**  
+| Risk                          | Impact              | Mitigation                               |
+|-------------------------------|---------------------|-------------------------------------------|
+|                               |                     |                                           |
+|                               |                     |                                           |
+
+---
+
+7. **Data Gaps & Next Steps**  
+- 
+- 
+- 
+
+---
+
+📦 RAW DATA:  
+{overall_answers}
+"""
+
+
+
+
+
+    formal_questions = [
+        {
+            "section": "Core Audience",
+            "questions": f"Who is {chosen_artist}'s core audience?",
+            "features": "include table breakdown of core audience"
+        }
+    ]
+
+    ##cut down Thought input, so only last one returned with Answer from
+
+
+    #count tokens anyway, for later usage:
+    total_tokens = count_tokens(prompto3)
+    print(f"Total tokens of prompt: {total_tokens}")
+
+    max_tokens = 16384 - total_tokens - 200
+
+    response = openai.chat.completions.create(
+    model="gpt-3.5-turbo-0125",  # or "o3-pro" if enabled
+    messages=[
+        {
+            "role": "system",
+            "content": "You are a precise music industry data analyst. Be structured, factual, and preserve all stats given."
+        },
+        {
+            "role": "user",
+            "content": prompto3
+        }
+    ],
+    temperature=0.2,  # low temp helps avoid paraphrasing
+    max_tokens=4096   # adjust based on your expected output size
+)
+    
+    print(response.choices[0].message.content)
+    two_pager_document = response.choices[0].message.content
+
+    #add generation of two_pager_part2
+
+    
+    #for question in formal_questions:
+    print(f"overall_answer2 is {overall_answers2}")
+
+    with open(f"{chosen_artist}o32.txt","w", encoding="utf-8") as file:
+        file.write(two_pager_document)
+    
+    return two_pager_document
+
+
+demo = gr.Interface(
+    fn=main,
+    inputs="text",
+    outputs="text",
+    title="artist report generator",
+    description="generate report for artist"
+)
+
+demo.launch(share=True)
+
+#if __name__ == "__main__":
+    #response = asyncio.run(main())
+    #then pass to llm to assemble formal response to formal questions
+
+# FunctionAgent works for LLMs with a function calling API.
+# ReActAgent works for any LLM.
+
+
+
+#can check logs:
+#async for ev in handler.stream_events():
+    #if isinstance(ev, ToolCallResult):
+        #print("")
+        #print("Called tool: ", ev.tool_name, ev.tool_kwargs, "=>", ev.tool_output)
+    #elif isinstance(ev, AgentStream):  # showing the thought process
+        #print(ev.delta, end="", flush=True)
+