streamlit_app.py

# streamlit_app.py
import os
import time
import string
import hashlib
from glob import glob
from pathlib import Path
from difflib import SequenceMatcher

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

load_dotenv()

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

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

# Session defaults
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("fast_mode", False)
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", "")

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

# track url changes
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_input = settings_exp.text_input("Gemini Model (short name)", "gemini-2.5-flash-lite", key="model_input")
settings_exp.text_input("Google API Key", key="api_key", value=os.getenv("GOOGLE_API_KEY", ""), type="password")
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."
)
analysis_prompt = settings_exp.text_area("Enter analysis", value=default_prompt, height=140)
settings_exp.text_input("Video Password (if needed)", key="video-password", placeholder="password", type="password")
settings_exp.checkbox("Fast mode (skip compression, smaller model, fewer tokens)", key="fast_mode")

# Show which key is active
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=180):
    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:
        obj = get_file(name)
        state = getattr(obj, "state", None)
        if not state or getattr(state, "name", None) != "PROCESSING":
            return obj
        if time.time() - start > timeout:
            raise TimeoutError("File processing timed out")
        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

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:
    pass

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 > 50 and not st.session_state.get("fast_mode", False):
            st.sidebar.warning("Large file detected — consider enabling Fast mode or compression.", icon="⚠️")
    except Exception:
        pass

# --- Generation flow ---
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 "gemini-2.5-flash-lite").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
                    fast_mode = st.session_state.get("fast_mode", False)
                    upload_path = local_path
                    try:
                        file_size_mb = os.path.getsize(local_path) / (1024 * 1024)
                    except Exception:
                        file_size_mb = 0

                    if not fast_mode and file_size_mb > 50:
                        compressed_path = str(Path(local_path).with_name(Path(local_path).stem + "_compressed.mp4"))
                        try:
                            preset = "veryfast" if fast_mode else "fast"
                            upload_path = compress_video(local_path, compressed_path, crf=28, preset=preset)
                        except Exception:
                            upload_path = local_path

                    with st.spinner("Uploading video..."):
                        uploaded = upload_video_sdk(upload_path)
                        processed = wait_for_processed(uploaded, timeout=180)
                        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 = ""
                if st.session_state.get("fast_mode"):
                    model_used = model_id if model_id else "gemini-2.5-flash-lite"
                    max_tokens = 512
                else:
                    model_used = model_id
                    max_tokens = 1024

                est_tokens = max_tokens
                est_cost_caption = f"Est. max tokens: {est_tokens}"

                agent = maybe_create_agent(model_used)
                if agent:
                    with st.spinner("Generating description via Agent..."):
                        if not processed:
                            raise RuntimeError("Processed file missing for agent generation")
                        response = agent.run(prompt_text, videos=[processed], safety_settings=safety_settings)
                        out = getattr(response, "content", None) or getattr(response, "outputText", None) or str(response)
                else:
                    if not HAS_GENAI or genai is None:
                        raise RuntimeError("Responses API not available; install google.generativeai SDK.")
                    genai.configure(api_key=key_to_use)
                    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."}

                    # Try the modern and legacy signatures; fail clearly if both fail
                    try:
                        response = genai.responses.generate(
                            model=model_used,
                            messages=[system_msg, user_msg],
                            files=[{"name": fname}],
                            safety_settings=safety_settings,
                            max_output_tokens=max_tokens,
                        )
                    except TypeError:
                        response = genai.responses.generate(
                            model=model_used,
                            input=[{"text": prompt_text, "files": [{"name": fname}]}],
                            safety_settings=safety_settings,
                            max_output_tokens=max_tokens,
                        )

                    # Normalize response into iterable items safely
                    outputs = []
                    if response is None:
                        outputs = []
                    else:
                        # response might be object or dict; try known attributes/keys
                        if isinstance(response, dict):
                            # common dict keys
                            if isinstance(response.get("output"), list):
                                outputs = response.get("output") or []
                            elif isinstance(response.get("candidates"), list):
                                outputs = response.get("candidates") or []
                            elif isinstance(response.get("items"), list):
                                outputs = response.get("items") or []
                            elif isinstance(response.get("responses"), list):
                                outputs = response.get("responses") or []
                            else:
                                # fallback: try to find list-valued entries
                                for v in response.values():
                                    if isinstance(v, list):
                                        outputs = v
                                        break
                        else:
                            # try attribute access
                            attr_candidates = []
                            for attr in ("output", "candidates", "items", "responses"):
                                val = getattr(response, attr, None)
                                if isinstance(val, list):
                                    attr_candidates = val
                                    break
                            outputs = attr_candidates or []

                    # Ensure we have a list
                    if not isinstance(outputs, list):
                        outputs = list(outputs) if outputs else []

                    text_pieces = []
                    # Iterate safely through outputs (may be dicts or objects)
                    for item in outputs:
                        if item is None:
                            continue
                        # attempt to extract a 'content' bag
                        contents = None
                        if isinstance(item, dict):
                            contents = item.get("content") or item.get("text") or item.get("message") or item.get("output")
                        else:
                            contents = getattr(item, "content", None) or getattr(item, "text", None) or getattr(item, "message", None) or getattr(item, "output", None)

                        # If contents is a single string, take it
                        if isinstance(contents, str):
                            if contents.strip():
                                text_pieces.append(contents.strip())
                            continue

                        # If contents is list-like, iterate
                        if isinstance(contents, (list, tuple)):
                            for c in contents:
                                if c is None:
                                    continue
                                if isinstance(c, str):
                                    if c.strip():
                                        text_pieces.append(c.strip())
                                    continue
                                c_text = None
                                if isinstance(c, dict):
                                    c_text = c.get("text") or c.get("content") or None
                                else:
                                    c_text = getattr(c, "text", None) or getattr(c, "content", None)
                                if c_text:
                                    text_pieces.append(str(c_text).strip())
                            continue

                        # If the item itself contains direct text fields
                        direct_txt = None
                        if isinstance(item, dict):
                            direct_txt = item.get("text") or item.get("output_text") or item.get("message")
                        else:
                            direct_txt = getattr(item, "text", None) or getattr(item, "output_text", None) or getattr(item, "message", None)
                        if direct_txt:
                            text_pieces.append(str(direct_txt).strip())

                    # final fallback: top-level text on response
                    if not text_pieces:
                        top_text = None
                        if isinstance(response, dict):
                            top_text = response.get("text") or response.get("message") or None
                        else:
                            top_text = getattr(response, "text", None) or getattr(response, "message", None)
                        if top_text:
                            text_pieces.append(str(top_text).strip())

                    # dedupe preserving order
                    seen = set()
                    filtered = []
                    for t in text_pieces:
                        if not isinstance(t, str):
                            continue
                        if t and t not in seen:
                            filtered.append(t)
                            seen.add(t)
                    out = "\n\n".join(filtered)

                # post-process output to remove prompt echo or placeholders
                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(est_cost_caption)
            except Exception as e:
                st.session_state["last_error"] = str(e)
                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"))