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.gitattributes

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.gitignore

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+.env
+.venv/

.gradioignore

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+.env
+.venv/

README.md

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 ---
-title: Carnatic Music Ai
-emoji: 🦀
-colorFrom: indigo
-colorTo: purple
+title: carnatic-music-ai
+python_version: 3.12
+emoji: 🚀
+colorFrom: purple
+colorTo: pink
 sdk: gradio
 sdk_version: 5.35.0
 app_file: app.py

app.py

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+import json
+import os
+import gradio as gr
+from dotenv import load_dotenv
+from openai import OpenAI
+import operator
+from typing import Callable, Iterable, List, Mapping, Any
+
+# ----------------------------------------------------------------------
+# Shared operator registry and resolver
+# ----------------------------------------------------------------------
+_OPS: dict[str, Callable[[Any, Any], bool]] = {
+    "==": operator.eq,
+    "!=": operator.ne,
+    "<":  operator.lt,
+    "<=": operator.le,
+    ">":  operator.gt,
+    ">=": operator.ge,
+    "in": lambda lhs, rhs: lhs in rhs,
+    "contains": lambda lhs, rhs: (
+        all(item in lhs for item in rhs)
+        if isinstance(rhs, (list, tuple, set, frozenset))
+        else rhs in lhs
+    ),
+}
+
+def _resolve(op: str | Callable[[Any, Any], bool]) -> Callable[[Any, Any], bool]:
+    if callable(op):
+        return op
+    try:
+        return _OPS[op]
+    except KeyError:
+        raise ValueError(f"Unsupported operator: {op!r}") from None
+
+# ----------------------------------------------------------------------
+# Helper: fetch a value no matter where it is nested
+# ----------------------------------------------------------------------
+def _get_nested_value(obj: Any, field: str) -> Any:
+    """
+    • Supports dotted paths like "signature.aarohanam".
+    • If no dots are given, searches *recursively* for a key
+      with that name and returns the first match.
+    • Returns [] (empty list) when the key can’t be found
+      so that “contains” still works safely.
+    """
+    # 1)  dotted‑path lookup  ------------------------------------------
+    if "." in field:
+        for part in field.split("."):
+            if isinstance(obj, Mapping) and part in obj:
+                obj = obj[part]
+            else:
+                return []          # path breaks ⇒ treat as “nothing here”
+        return obj
+
+    # 2)  recursive scan (simple key)  ---------------------------------
+    if isinstance(obj, Mapping):
+        if field in obj:
+            return obj[field]
+        for v in obj.values():
+            got = _get_nested_value(v, field)
+            if got != []:
+                return got
+    elif isinstance(obj, (list, tuple, set, frozenset)):
+        for v in obj:
+            got = _get_nested_value(v, field)
+            if got != []:
+                return got
+    return []                       # not found anywhere
+
+# ----------------------------------------------------------------------
+# 1‑filter version (unchanged, for reference)
+# ----------------------------------------------------------------------
+def query_collection(collectionName: str,
+                     field: str | None = None,
+                     op: str | Callable[[Any, Any], bool] | None = None,
+                     value: Any | None = None):
+    collection: Iterable[Mapping[str, Any]] = read_file_as_json_array(
+        collectionName, f"data/{collectionName}.json")
+
+    if not op:
+        return collection[:20]
+
+    cmp = _resolve(op)
+    return [
+        item
+        for item in collection
+        if cmp(_get_nested_value(item, field), value)
+    ]
+
+
+# ----------------------------------------------------------------------
+# NEW: multi‑filter counter
+# ----------------------------------------------------------------------
+def count_documents(collectionName: str,
+                    filters: List[Mapping[str, Any] | None] = None) -> int:
+    """
+    *filters* is a list/tuple of dict‑like objects with keys:
+        • field  – str
+        • op     – str key in _OPS **or** a custom 2‑arg callable
+        • value  – value to compare against
+
+    All filters are AND‑ed together (an item must satisfy every filter).
+    """
+    collection: Iterable[Mapping[str, Any]] = read_file_as_json_array(
+        collectionName, f"data/{collectionName}.json")
+
+    # Pre‑compute each filter as (field, cmp‑function, value)
+    compiled = [(f["field"], _resolve(f["op"]), f["value"])
+                for f in filters] if filters else []
+
+    def matches(item):
+        return all(cmp(item[field] if field in item else None, value) for field, cmp, value in compiled)
+
+    return sum(1 for item in collection if matches(item))
+
+
+def read_file_as_json_array(label, fileName):
+    with open(fileName, "r", encoding="utf-8") as f:
+        some_str = f.read()
+
+    json_array = json.loads(some_str)
+    print(f"Number of {label} loaded = ")
+    print(len(json_array))
+    return json_array
+
+
+def get_audio_update(swaram):
+    print("play_swaram called for " + swaram)
+    results = query_collection("swarams", "name", "==", swaram.upper())
+
+    if len(results) > 0:
+        swaramInfo = results[0]
+        print(swaramInfo)
+        audioFilePath = f"data/audio/swarams/c_sw_{swaramInfo['fileName']}.mp3"
+        audio_update = gr.update(value=audioFilePath, visible=True)
+        # return f"Playing swaram {swaram} using audio file {audioFilePath}"
+        return audio_update
+    else:
+        audio_update = gr.update(visible=False)
+        # return f"Unknown swaram {swaram}"
+        return audio_update
+
+
+def play_swaram(swaram):
+    # play the swaram using audio library
+    return get_audio_update(swaram)
+
+
+load_dotenv(override=True)
+
+tool_specs = [
+    {
+        "type": "function",
+        "function": {
+            "name": "query_collection",
+            "description": "Run a structured filter over Firestore",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "collectionName":   {"type": "string"},
+                    "field":        {"type": "string"},
+                    "op":           {"type": "string", "enum": ["==", "<", "<=", ">", ">="]},
+                    "value":        {"type": "string"}
+                },
+                "required": ["collection"]
+            }
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "vector_search",
+            "description": "Semantic search over a collection using embeddings",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "collectionName": {"type": "string"},
+                    "query":      {"type": "string"},
+                    "top_k":      {"type": "integer", "default": 5}
+                },
+                "required": ["collectionName", "query"]
+            }
+        },
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "count_documents",
+            "description": "Return how many documents satisfy optional filters",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "collectionName": {"type": "string",
+                                       "description": "Firestore collection name"},
+                    "filters": {
+                        "type": "array",
+                        "items": {
+                            "type": "object",
+                            "properties": {
+                                "field": {"type": "string"},
+                                "op":    {"type": "string",
+                                          "enum": ["==", "<", "<=", ">", ">=", "!="]},
+                                "value": {}
+                            },
+                            "required": ["field", "op", "value"]
+                        },
+                        "description": "Optional list of field comparisons"
+                    }
+                },
+                "required": ["collectionName"]
+            }
+        }
+    },
+    {
+        "type": "function",
+        "function": {
+            "name": "play_swaram",
+            "description": "Play a swaram",
+            "parameters": {
+                "type": "object",
+                "properties": {
+                    "swaram":   {"type": "string"},
+                },
+                "required": ["swaram"]
+            }
+        },
+    },
+
+]
+
+
+openai = OpenAI()
+system_prompt = f"""You are an encyclopedia of carnatic music. Users will query you asking questions about various aspects of carnatic music.
+    if the user asks about ragams or ragas, use the ragams collection from the database instead of ragas. If you already fetched the data earlier, don't again query from the database.
+    If the user asks for a count, total, number of X, etc call count_documents. 
+    if user asks for ragams containing some swarams X,Y,Z in aarohanam or avarohanam, use operator contains and pass an array of swarams to it as value.
+    Using ONLY the tools query_collection, count_documents and vector_search as your datasources, please answer the questions. If you don't know the answer to a question, please say so.
+    """
+
+
+def handle_tool_calls(tool_calls):
+    results = []
+    for tool_call in tool_calls:
+        tool_name = tool_call.function.name
+        tool = globals().get(tool_name)
+        arguments = json.loads(tool_call.function.arguments)
+        print(arguments)
+        result = tool(**arguments)
+        results.append(
+            {"role": "tool", "tool_call_id": tool_call.id, "content": json.dumps(result)})
+    return results
+
+
+def chat(message, history):
+    chat_history = history + [{"role": "user", "content": message}]
+    # print("history = " + json.dumps(history))
+    messages = [{"role": "system", "content": system_prompt}]
+
+    # for user_msg, assistant_msg in history:
+    #     if user_msg is not None:
+    #         messages.append({"role": "user", "content": user_msg})
+    #     if assistant_msg is not None:
+    #         messages.append({"role": "assistant", "content": assistant_msg})
+
+    messages += history
+
+    messages.append({"role": "user", "content": message})
+
+    print("messages = " + json.dumps(messages))
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini", messages=messages, tools=tool_specs, tool_choice="auto")
+
+    response_message = response.choices[0].message
+    if response.choices[0].finish_reason == "tool_calls":
+        tool_calls = response.choices[0].message.tool_calls
+        tool_responses = handle_tool_calls(tool_calls)
+        messages.append(response_message)
+        messages.extend(tool_responses)
+        followup_response = openai.chat.completions.create(
+            model="gpt-4o-mini", messages=messages, tools=tool_specs, tool_choice="auto")
+        chat_history.append(
+            {"role": "assistant", "content": followup_response.choices[0].message.content})
+        play_audio_tool = [
+            tool_call for tool_call in tool_calls if tool_call.function.name == "play_swaram"]
+        # print(play_audio_tool)
+        if (len(play_audio_tool) > 0):
+            args = json.loads(play_audio_tool[0].function.arguments)
+            return "", chat_history, get_audio_update(**args)
+        else:
+            return "", chat_history, gr.update(visible=False)
+    else:
+        print("message.content = " + message)
+        print("response.choices[0].message.content" +
+              response.choices[0].message.content)
+        chat_history.append({"role": "assistant",
+                            "content": response.choices[0].message.content})
+        return "", chat_history, gr.update(visible=False)
+
+
+# demo = gr.ChatInterface(chat, type="messages")
+
+# demo.launch()
+
+# ----------------------------------
+# Interface
+# ----------------------------------
+with gr.Blocks(title="Chat + Player") as demo:
+    chatbot = gr.Chatbot(height=400, type="messages")
+    # hidden until needed
+    audio_out = gr.Audio(interactive=False, visible=False, autoplay=True)
+    textbox = gr.Textbox(placeholder="Type here and hit Enter")
+
+    # wire textbox -> respond()
+    textbox.submit(
+        chat,
+        inputs=[textbox, chatbot],
+        outputs=[textbox, chatbot, audio_out],
+    )
+
+demo.launch()

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