import gradio as gr
import requests
import os
import json
import time
# CORRECTED IMPORT FOR RESTRICTED ENVIRONMENTS:
# We bypass the 'editor' module and import the class directly.
# This works now because requirements.txt ensures a full installation.
from moviepy.video.io.VideoFileClip import VideoFileClip
import traceback # Import traceback for detailed error logging

# --- 1. CONFIGURATION & CONSTANTS ---
# Securely load API key from Hugging Face Space secrets.
ONE_API_KEY = os.environ.get("ONE_API_KEY", "268976:66f4f58a2a905")
# The custom endpoint for the one-api.ir service.
ONE_API_URL = "https://api.one-api.ir/chatbot/v1/gpt4o/"

# --- MASTER PROMPTS ---
PROMPT_SHORTS_MODE = """
You are an expert producer of viral short-form content. Analyze the provided SRT transcript to find the single most impactful, hook-worthy segment for a video under 3 minutes (ideally 60-90 seconds).
PRIORITIES: Strong Hook, Single Clear Point, High Energy, Clear Payoff.
**Input SRT Content:**
{transcript_content}
**Instructions:**
Your output MUST be a single, valid JSON object and nothing else. Do not include any text, code blocks, or explanations before or after the JSON object.
{{
  "clip_title_suggestion": "A catchy, clickbait-style title for this short clip.",
  "reasoning": "Briefly explain why this segment is perfect for a short video, referencing the hook and payoff.",
  "final_clip_start_seconds": <The precise start time in total seconds from the SRT>,
  "final_clip_end_seconds": <The precise end time in total seconds from the SRT>
}}
"""

PROMPT_NARRATIVE_MODE = """
You are an expert video editor and storyteller. Analyze the provided transcript to find the most compelling narrative segment between 5 and 12 minutes long.
METHODOLOGY: Identify the peak moment, then find the corresponding setup and resolution to create a complete narrative arc.
**Input SRT Content:**
{transcript_content}
**Instructions:**
Your output MUST be a single, valid JSON object and nothing else. Do not include any text, code blocks, or explanations before or after the JSON object.
{{
  "narrative_summary": "A one-sentence summary of the story told in the extracted clip.",
  "reasoning": "Explain why this segment works as a standalone narrative, mentioning the peak moment and how the start/end points provide a full arc.",
  "final_clip_start_seconds": <The precise start time in total seconds from the SRT>,
  "final_clip_end_seconds": <The precise end time in total seconds from the SRT>
}}
"""

# --- 2. LLM AGENT WRAPPER ---
def call_gpt4o_oneapi(transcript_content, prompt_template):
    """Makes a robust, custom API call to the one-api.ir service."""
    if not ONE_API_KEY or not ONE_API_URL:
        raise ValueError("ONE_API_KEY and ONE_API_URL secrets are not set correctly.")

    headers = {
        "one-api-token": ONE_API_KEY,
        "Content-Type": "application/json"
    }
    final_prompt = prompt_template.format(transcript_content=transcript_content)
    payload = [{"role": "user", "content": final_prompt}]

    try:
        response = requests.post(ONE_API_URL, headers=headers, json=payload, timeout=180)
        response.raise_for_status()
        result = response.json()
        
        if "result" not in result or not isinstance(result.get("result"), list) or len(result["result"]) == 0:
            return f"Error: Unexpected JSON structure from API. 'result' list not found or empty.\nFull response: {json.dumps(result)}"

        first_item_in_result = result["result"][0]

        if isinstance(first_item_in_result, dict):
            message_content = first_item_in_result.get("content")
            if message_content:
                return message_content
            else:
                 return f"Error: 'content' key not found in API response dictionary.\nFull response: {json.dumps(result)}"
        elif isinstance(first_item_in_result, str):
            return first_item_in_result
        else:
            return f"Error: Unknown item type in API 'result' list.\nFull response: {json.dumps(result)}"

    except requests.exceptions.HTTPError as e:
        return f"HTTP Error calling API: {e}\nResponse Body: {e.response.text}"
    except requests.exceptions.RequestException as e:
        return f"Error connecting to API: {str(e)}"
    except json.JSONDecodeError:
        return f"Error: Failed to decode JSON from API response.\nResponse Body: {response.text}"

# --- 3. CORE ORCHESTRATOR FUNCTION ---
def generate_viral_clip(video_file, srt_file, analysis_mode, progress=gr.Progress()):
    if not video_file or not srt_file:
        return "Error: Please upload both a video file and an SRT file.", None
    if not ONE_API_KEY or not ONE_API_URL:
        return "Error: API keys for OneAPI are not configured correctly in the Space secrets.", None

    video = None
    new_clip = None
    try:
        progress(0.1, desc="Reading SRT file...")
        with open(srt_file, 'r', encoding='utf-8') as f:
            transcript_content = f.read()

        progress(0.2, desc="Preparing analysis prompt...")
        prompt_template = PROMPT_SHORTS_MODE if analysis_mode == "Viral Spot for Shorts (< 3 mins)" else PROMPT_NARRATIVE_MODE

        progress(0.4, desc="Calling AI for analysis...")
        llm_response_str = call_gpt4o_oneapi(transcript_content, prompt_template)

        progress(0.7, desc="Parsing AI response...")
        if llm_response_str.startswith("Error") or llm_response_str.startswith("HTTP Error"):
             return llm_response_str, None

        try:
            cleaned_response = llm_response_str.strip()
            if cleaned_response.startswith("```json"):
                cleaned_response = cleaned_response[7:]
            if cleaned_response.endswith("```"):
                cleaned_response = cleaned_response[:-3]
            
            parsed_response = json.loads(cleaned_response)

            if not isinstance(parsed_response, dict):
                raise TypeError(f"AI did not return a valid JSON object. It returned a {type(parsed_response).__name__}.")

            start_time = float(parsed_response['final_clip_start_seconds'])
            end_time = float(parsed_response['final_clip_end_seconds'])
            reasoning = parsed_response.get('reasoning', 'No reasoning provided.')

            summary = (f"✅ Analysis Complete!\n\n"
                       f"Reasoning: {reasoning}\n\n"
                       f"Title Suggestion: {parsed_response.get('clip_title_suggestion', 'N/A')}\n"
                       f"Narrative Summary: {parsed_response.get('narrative_summary', 'N/A')}\n\n"
                       f"Clipping video from {time.strftime('%H:%M:%S', time.gmtime(start_time))} to {time.strftime('%H:%M:%S', time.gmtime(end_time))}.")

        except (json.JSONDecodeError, KeyError, TypeError) as e:
            error_msg = f"Error: Failed to parse AI response. Details: {e}\n\nRaw AI Response:\n---\n{llm_response_str}"
            return error_msg, None

        progress(0.8, desc="Clipping video...")
        output_filename = "viral_clip.mp4"
        
        video = VideoFileClip(video_file)
        
        if end_time > video.duration:
            end_time = video.duration
            summary += f"\n\n⚠️ Warning: End time was beyond video duration, adjusted to {end_time:.2f}s."
        
        new_clip = video.subclipped(start_time, end_time)
        new_clip.write_videofile(output_filename, codec="libx264", audio_codec="aac")

        progress(1.0, desc="Done!")
        return summary, output_filename

    except Exception as e:
        tb_str = traceback.format_exc()
        return f"An unexpected error occurred in the main process: {str(e)}\n\nTraceback:\n{tb_str}", None
    
    finally:
        # Gracefully close the video clips to release file handles
        if new_clip:
            try:
                new_clip.close()
            except Exception:
                pass # Ignore errors on close
        if video:
            try:
                video.close()
            except Exception:
                pass # Ignore errors on close

# --- 4. GRADIO UI DEFINITION ---
with gr.Blocks(theme=gr.themes.Soft()) as demo:
    gr.Markdown(
        """
        # 🎬 AI Viral Video Extractor
        This tool uses an AI agent to analyze a video transcript and automatically clip the most viral segment.
        **⚠️ Important Setup:** For best security, configure `ONE_API_KEY` in your Hugging Face **Space Settings > Secrets**.
        """
    )
    with gr.Row():
        with gr.Column(scale=1):
            video_input = gr.Video(label="1. Upload Original Video")
            srt_input = gr.File(label="2. Upload English SRT File", file_types=['.srt'])
            mode_input = gr.Radio(
                label="3. Select Analysis Mode",
                choices=["Viral Spot for Shorts (< 3 mins)", "Viral Narrative Clip (5-12 mins)"],
                value="Viral Narrative Clip (5-12 mins)"
            )
            submit_button = gr.Button("🚀 Generate Viral Clip", variant="primary")

        with gr.Column(scale=2):
            summary_output = gr.Textbox(label="Analysis Summary", lines=12, interactive=False)
            video_output = gr.Video(label="Generated Clip", interactive=False)

    submit_button.click(
        fn=generate_viral_clip,
        inputs=[video_input, srt_input, mode_input],
        outputs=[summary_output, video_output],
    )

if __name__ == "__main__":
    demo.launch(debug=True)