streamlit_app.py

import os
import time
import string
import hashlib
import traceback
from glob import glob
from pathlib import Path
from difflib import SequenceMatcher
import json

import yt_dlp
import ffmpeg
import streamlit as st
from dotenv import load_dotenv

load_dotenv()

# Optional phi integration
try:
    from phi.agent import Agent
    from phi.model.google import Gemini
    from phi.tools.duckduckgo import DuckDuckGo
    HAS_PHI = True
except Exception:
    Agent = Gemini = DuckDuckGo = None
    HAS_PHI = False

# Legacy google.generativeai SDK
try:
    import google.generativeai as genai
    from google.generativeai import upload_file, get_file, responses  # type: ignore
    HAS_GENAI = True
except Exception:
    genai = None
    upload_file = get_file = responses = None
    HAS_GENAI = False

st.set_page_config(page_title="Generate the story of videos", layout="wide")
DATA_DIR = Path("./data")
DATA_DIR.mkdir(exist_ok=True)

MODEL_OPTIONS = [
    "gemini-2.5-flash",
    "gemini-2.5-flash-lite",
    "gemini-2.0-flash",
    "gemini-2.0-flash-lite",
    "custom",
]
DEFAULT_MODEL = "gemini-2.0-flash-lite"
DEFAULT_PROMPT = (
    "Watch the video and provide a detailed behavioral report focusing on human actions, interactions, posture, movement, and apparent intent. "
    "Keep language professional. Include a list of observations for notable events."
)

st.session_state.setdefault("videos", "")
st.session_state.setdefault("loop_video", False)
st.session_state.setdefault("uploaded_file", None)
st.session_state.setdefault("processed_file", None)
st.session_state.setdefault("busy", False)
st.session_state.setdefault("last_loaded_path", "")
st.session_state.setdefault("analysis_out", "")
st.session_state.setdefault("last_error", "")
st.session_state.setdefault("file_hash", None)
st.session_state.setdefault("api_key", os.getenv("GOOGLE_API_KEY", ""))
st.session_state.setdefault("last_model", "")
st.session_state.setdefault("upload_progress", {"uploaded": 0, "total": 0})
st.session_state.setdefault("last_url_value", "")
st.session_state.setdefault("processing_timeout", 900)
st.session_state.setdefault("generation_timeout", 300)
st.session_state.setdefault("compress_threshold_mb", 200)

def sanitize_filename(path_str: str):
    name = Path(path_str).name
    return name.lower().translate(str.maketrans("", "", string.punctuation)).replace(" ", "_")

def file_sha256(path: str, block_size: int = 65536) -> str:
    h = hashlib.sha256()
    with open(path, "rb") as f:
        for chunk in iter(lambda: f.read(block_size), b""):
            h.update(chunk)
    return h.hexdigest()

def convert_video_to_mp4(video_path: str) -> str:
    target_path = str(Path(video_path).with_suffix(".mp4"))
    if os.path.exists(target_path):
        return target_path
    ffmpeg.input(video_path).output(target_path).run(overwrite_output=True, quiet=True)
    try:
        os.remove(video_path)
    except Exception:
        pass
    return target_path

def compress_video(input_path: str, target_path: str, crf: int = 28, preset: str = "fast"):
    try:
        ffmpeg.input(input_path).output(
            target_path, vcodec="libx264", crf=crf, preset=preset
        ).run(overwrite_output=True, quiet=True)
        return target_path
    except Exception:
        return input_path

def download_video_ytdlp(url: str, save_dir: str, video_password: str = None) -> str:
    if not url:
        raise ValueError("No URL provided")
    outtmpl = str(Path(save_dir) / "%(id)s.%(ext)s")
    ydl_opts = {"outtmpl": outtmpl, "format": "best"}
    if video_password:
        ydl_opts["videopassword"] = video_password
    with yt_dlp.YoutubeDL(ydl_opts) as ydl:
        info = ydl.extract_info(url, download=True)
    video_id = info.get("id") if isinstance(info, dict) else None
    if video_id:
        matches = glob(os.path.join(save_dir, f"{video_id}.*"))
    else:
        all_files = glob(os.path.join(save_dir, "*"))
        matches = sorted(all_files, key=os.path.getmtime, reverse=True)[:1] if all_files else []
    if not matches:
        raise FileNotFoundError("Downloaded video not found")
    return convert_video_to_mp4(matches[0])

def file_name_or_id(file_obj):
    if file_obj is None:
        return None
    if isinstance(file_obj, dict):
        return file_obj.get("name") or file_obj.get("id")
    return getattr(file_obj, "name", None) or getattr(file_obj, "id", None) or getattr(file_obj, "fileId", None)

def get_effective_api_key():
    return st.session_state.get("api_key") or os.getenv("GOOGLE_API_KEY")

def configure_genai_if_needed():
    key = get_effective_api_key()
    if not key:
        return False
    try:
        genai.configure(api_key=key)
    except Exception:
        pass
    return True

_agent = None
def maybe_create_agent(model_id: str):
    global _agent
    key = get_effective_api_key()
    if not (HAS_PHI and HAS_GENAI and key):
        _agent = None
        return None
    if _agent and st.session_state.get("last_model") == model_id:
        return _agent
    try:
        genai.configure(api_key=key)
        _agent = Agent(name="Video AI summarizer", model=Gemini(id=model_id), tools=[DuckDuckGo()], markdown=True)
        st.session_state["last_model"] = model_id
    except Exception:
        _agent = None
    return _agent

def clear_all_video_state():
    st.session_state.pop("uploaded_file", None)
    st.session_state.pop("processed_file", None)
    st.session_state["videos"] = ""
    st.session_state["last_loaded_path"] = ""
    st.session_state["analysis_out"] = ""
    st.session_state["last_error"] = ""
    st.session_state["file_hash"] = None
    for f in glob(str(DATA_DIR / "*")):
        try:
            os.remove(f)
        except Exception:
            pass

current_url = st.session_state.get("url", "")
if current_url != st.session_state.get("last_url_value"):
    clear_all_video_state()
    st.session_state["last_url_value"] = current_url

st.sidebar.header("Video Input")
st.sidebar.text_input("Video URL", key="url", placeholder="https://")

settings_exp = st.sidebar.expander("Settings", expanded=False)
model_choice = settings_exp.selectbox("Select model", options=MODEL_OPTIONS, index=MODEL_OPTIONS.index(DEFAULT_MODEL) if DEFAULT_MODEL in MODEL_OPTIONS else 0)
if model_choice == "custom":
    model_input = settings_exp.text_input("Custom model id", value=DEFAULT_MODEL, key="model_input")
    model_selected = model_input.strip() or DEFAULT_MODEL
else:
    st.session_state["model_input"] = model_choice
    model_selected = model_choice

settings_exp.text_input("Google API Key", key="api_key", value=os.getenv("GOOGLE_API_KEY", ""), type="password")
analysis_prompt = settings_exp.text_area("Analysis prompt", value=DEFAULT_PROMPT, height=140)
settings_exp.text_input("Video Password (if needed)", key="video-password", placeholder="password", type="password")

settings_exp.number_input(
    "Processing timeout (s)", min_value=60, max_value=3600,
    value=st.session_state.get("processing_timeout", 900), step=30,
    key="processing_timeout",
)
settings_exp.number_input(
    "Generation timeout (s)", min_value=30, max_value=1800,
    value=st.session_state.get("generation_timeout", 300), step=10,
    key="generation_timeout",
)
settings_exp.number_input(
    "Optional compression threshold (MB)", min_value=10, max_value=2000,
    value=st.session_state.get("compress_threshold_mb", 200), step=10,
    key="compress_threshold_mb",
)

key_source = "session" if st.session_state.get("api_key") else ".env" if os.getenv("GOOGLE_API_KEY") else "none"
settings_exp.caption(f"Using API key from: **{key_source}**")
if not get_effective_api_key():
    settings_exp.warning("No Google API key provided; upload/generation disabled.", icon="⚠️")

safety_settings = [
    {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "OFF"},
    {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "OFF"},
    {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "OFF"},
    {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "OFF"},
]

def upload_video_sdk(filepath: str):
    key = get_effective_api_key()
    if not key:
        raise RuntimeError("No API key provided")
    if not HAS_GENAI or upload_file is None:
        raise RuntimeError("google.generativeai SDK not available; cannot upload")
    genai.configure(api_key=key)
    return upload_file(filepath)

def wait_for_processed(file_obj, timeout: int = None):
    if timeout is None:
        timeout = st.session_state.get("processing_timeout", 900)
    if not HAS_GENAI or get_file is None:
        return file_obj
    start = time.time()
    name = file_name_or_id(file_obj)
    if not name:
        return file_obj
    backoff = 1.0
    while True:
        try:
            obj = get_file(name)
        except Exception as e:
            if time.time() - start > timeout:
                raise TimeoutError(f"Failed to fetch file status before timeout: {e}")
            time.sleep(backoff)
            backoff = min(backoff * 2, 8.0)
            continue

        state = getattr(obj, "state", None)
        if not state or getattr(state, "name", None) != "PROCESSING":
            return obj

        if time.time() - start > timeout:
            raise TimeoutError(f"File processing timed out after {int(time.time() - start)}s")
        time.sleep(backoff)
        backoff = min(backoff * 2, 8.0)

def remove_prompt_echo(prompt: str, text: str, check_len: int = 600, ratio_threshold: float = 0.68):
    if not prompt or not text:
        return text
    a = " ".join(prompt.strip().lower().split())
    b_full = text.strip()
    b = " ".join(b_full[:check_len].lower().split())
    ratio = SequenceMatcher(None, a, b).ratio()
    if ratio >= ratio_threshold:
        cut = min(len(b_full), max(int(len(prompt) * 0.9), len(a)))
        new_text = b_full[cut:].lstrip(" \n:-")
        if len(new_text) >= 3:
            return new_text
    placeholders = ["enter analysis", "enter your analysis", "enter analysis here", "please enter analysis"]
    low = b_full.strip().lower()
    for ph in placeholders:
        if low.startswith(ph):
            return b_full[len(ph):].lstrip(" \n:-")
    return text

def compress_video_if_large(local_path: str, threshold_mb: int = 200):
    try:
        file_size_mb = os.path.getsize(local_path) / (1024 * 1024)
    except Exception as e:
        st.session_state["last_error"] = f"Failed to stat file before compression: {e}"
        return local_path, False

    if file_size_mb <= threshold_mb:
        return local_path, False

    compressed_path = str(Path(local_path).with_name(Path(local_path).stem + "_compressed.mp4"))
    try:
        result = compress_video(local_path, compressed_path, crf=28, preset="fast")
        if result and os.path.exists(result):
            return result, True
        return local_path, False
    except Exception as e:
        st.session_state["last_error"] = f"Video compression failed: {e}\n{traceback.format_exc()}"
        return local_path, False

def generate_via_responses_api(prompt_text: str, processed, model_used: str, max_tokens: int = 1024, timeout: int = 300):
    key = get_effective_api_key()
    if not key:
        raise RuntimeError("No API key provided")
    if not HAS_GENAI or responses is None:
        raise RuntimeError("Responses API not available; install google-generativeai SDK.")
    genai.configure(api_key=key)
    fname = file_name_or_id(processed)
    if not fname:
        raise RuntimeError("Uploaded file missing name/id")

    system_msg = {"role": "system", "content": prompt_text}
    user_msg = {"role": "user", "content": "Please summarize the attached video."}
    req = {
        "model": model_used,
        "input": [system_msg, user_msg],
        "files": [fname],
        "max_output_tokens": max_tokens,
        "temperature": 0.2,
    }
    resp = responses.create(**req)
    # extract text robustly
    out = ""
    try:
        if isinstance(resp, dict):
            candidates = resp.get("candidates") or resp.get("output") or []
            if isinstance(candidates, list) and candidates:
                c = candidates[0]
                if isinstance(c, dict):
                    out = c.get("content") or c.get("text") or ""
                else:
                    out = str(c)
            else:
                out = resp.get("outputText") or resp.get("content") or resp.get("text") or ""
        else:
            out = getattr(resp, "output_text", "") or getattr(resp, "text", "") or ""
    except Exception:
        out = str(resp)
    return out or ""

col1, col2 = st.columns([1, 3])
with col1:
    generate_now = st.button("Generate the story", type="primary", disabled=not bool(get_effective_api_key()))
with col2:
    st.write("")

if st.sidebar.button("Load Video", use_container_width=True):
    try:
        vpw = st.session_state.get("video-password", "")
        path = download_video_ytdlp(st.session_state.get("url", ""), str(DATA_DIR), vpw)
        st.session_state["videos"] = path
        st.session_state["last_loaded_path"] = path
        st.session_state.pop("uploaded_file", None)
        st.session_state.pop("processed_file", None)
        try:
            st.session_state["file_hash"] = file_sha256(path)
        except Exception:
            st.session_state["file_hash"] = None
    except Exception as e:
        st.sidebar.error(f"Failed to load video: {e}")

if st.session_state["videos"]:
    try:
        st.sidebar.video(st.session_state["videos"], loop=st.session_state.get("loop_video", False))
    except Exception:
        st.sidebar.write("Couldn't preview video")

    with st.sidebar.expander("Options", expanded=False):
        loop_checkbox = st.checkbox("Enable Loop", value=st.session_state.get("loop_video", False))
        st.session_state["loop_video"] = loop_checkbox

        if st.button("Clear Video(s)"):
            clear_all_video_state()

        try:
            with open(st.session_state["videos"], "rb") as vf:
                st.download_button("Download Video", data=vf, file_name=sanitize_filename(st.session_state["videos"]), mime="video/mp4", use_container_width=True)
        except Exception:
            st.sidebar.error("Failed to prepare download")

    st.sidebar.write("Title:", Path(st.session_state["videos"]).name)
    try:
        file_size_mb = os.path.getsize(st.session_state["videos"]) / (1024 * 1024)
        st.sidebar.caption(f"File size: {file_size_mb:.1f} MB")
        if file_size_mb > st.session_state.get("compress_threshold_mb", 200):
            st.sidebar.warning(f"Large file detected — it will be compressed automatically before upload (>{st.session_state.get('compress_threshold_mb')} MB).", icon="⚠️")
    except Exception:
        pass

if generate_now and not st.session_state.get("busy"):
    if not st.session_state.get("videos"):
        st.error("No video loaded. Use 'Load Video' in the sidebar.")
    else:
        key_to_use = get_effective_api_key()
        if not key_to_use:
            st.error("Google API key not set.")
        else:
            try:
                st.session_state["busy"] = True
                try:
                    if HAS_GENAI and genai is not None:
                        genai.configure(api_key=key_to_use)
                except Exception:
                    pass

                model_id = (st.session_state.get("model_input") or model_selected or DEFAULT_MODEL).strip()
                if st.session_state.get("last_model") != model_id:
                    st.session_state["last_model"] = ""
                maybe_create_agent(model_id)

                processed = st.session_state.get("processed_file")
                current_path = st.session_state.get("videos")
                try:
                    current_hash = file_sha256(current_path) if current_path and os.path.exists(current_path) else None
                except Exception:
                    current_hash = None

                reupload_needed = True
                if processed and st.session_state.get("last_loaded_path") == current_path and st.session_state.get("file_hash") == current_hash:
                    reupload_needed = False

                if reupload_needed:
                    if not HAS_GENAI:
                        raise RuntimeError("google-generativeai SDK not available; install it.")
                    local_path = current_path
                    upload_path, compressed = compress_video_if_large(local_path, threshold_mb=st.session_state.get("compress_threshold_mb", 200))

                    with st.spinner(f"Uploading video{' (compressed)' if compressed else ''}..."):
                        try:
                            uploaded = upload_video_sdk(upload_path)
                        except Exception as e:
                            st.session_state["last_error"] = f"Upload failed: {e}\n\nTraceback:\n{traceback.format_exc()}"
                            st.error("Upload failed. See Last Error for details.")
                            raise

                    try:
                        processed = wait_for_processed(uploaded, timeout=st.session_state.get("processing_timeout", 900))
                    except Exception as e:
                        st.session_state["last_error"] = f"Processing failed/wait timeout: {e}\n\nTraceback:\n{traceback.format_exc()}"
                        st.error("Video processing failed or timed out. See Last Error.")
                        raise

                    st.session_state["uploaded_file"] = uploaded
                    st.session_state["processed_file"] = processed
                    st.session_state["last_loaded_path"] = current_path
                    st.session_state["file_hash"] = current_hash

                prompt_text = (analysis_prompt.strip() or DEFAULT_PROMPT).strip()
                out = ""
                model_used = model_id
                max_tokens = 2048 if "2.5" in model_used else 1024
                est_tokens = max_tokens

                agent = maybe_create_agent(model_used)
                debug_info = {"agent_attempted": False, "agent_ok": False, "agent_error": None, "agent_response_has_text": False}
                if agent:
                    debug_info["agent_attempted"] = True
                    try:
                        with st.spinner("Generating description via Agent..."):
                            if not processed:
                                raise RuntimeError("Processed file missing for agent generation")
                            agent_response = agent.run(prompt_text, videos=[processed], safety_settings=safety_settings)
                            agent_text = getattr(agent_response, "content", None) or getattr(agent_response, "outputText", None) or None
                            if not agent_text:
                                try:
                                    if isinstance(agent_response, dict):
                                        for k in ("content", "outputText", "text", "message"):
                                            if k in agent_response and agent_response[k]:
                                                agent_text = agent_response[k]
                                                break
                                except Exception:
                                    pass
                            if agent_text and str(agent_text).strip():
                                out = str(agent_text).strip()
                                debug_info["agent_ok"] = True
                                debug_info["agent_response_has_text"] = True
                            else:
                                debug_info["agent_ok"] = False
                    except Exception as ae:
                        debug_info["agent_error"] = f"{ae}"
                        debug_info["agent_traceback"] = traceback.format_exc()

                if not out:
                    try:
                        with st.spinner("Generating description via Responses API..."):
                            out = generate_via_responses_api(prompt_text, processed, model_used, max_tokens=max_tokens, timeout=st.session_state.get("generation_timeout", 300))
                    except Exception as e:
                        tb = traceback.format_exc()
                        st.session_state["last_error"] = f"Responses API error: {e}\n\nDebug: {debug_info}\n\nTraceback:\n{tb}"
                        st.error("An error occurred while generating the story. You can try Generate again; the uploaded video will be reused.")
                        out = ""

                if out:
                    out = remove_prompt_echo(prompt_text, out)
                    p = prompt_text
                    if p and out.strip().lower().startswith(p.lower()):
                        out = out.strip()[len(p):].lstrip(" \n:-")
                    placeholders = ["enter analysis", "enter your analysis", "enter analysis here", "please enter analysis"]
                    low = out.strip().lower()
                    for ph in placeholders:
                        if low.startswith(ph):
                            out = out.strip()[len(ph):].lstrip(" \n:-")
                            break
                    out = out.strip()

                st.session_state["analysis_out"] = out
                st.session_state["last_error"] = ""
                st.subheader("Analysis Result")
                st.markdown(out if out else "No analysis returned.")
                st.caption(f"Est. max tokens: {est_tokens}")

            except Exception as e:
                tb = traceback.format_exc()
                st.session_state["last_error"] = f"{e}\n\nDebug: {locals().get('debug_info', {})}\n\nTraceback:\n{tb}"
                st.error("An error occurred while generating the story. You can try Generate again; the uploaded video will be reused.")
            finally:
                st.session_state["busy"] = False

if st.session_state.get("analysis_out"):
    just_loaded_same = (st.session_state.get("last_loaded_path") == st.session_state.get("videos"))
    if not just_loaded_same:
        st.subheader("Analysis Result")
        st.markdown(st.session_state.get("analysis_out"))

if st.session_state.get("last_error"):
    with st.expander("Last Error", expanded=False):
        st.write(st.session_state.get("last_error"))