import os
from dotenv import load_dotenv
import gradio as gr
from groq import Groq
from exa_py import Exa

load_dotenv()

groq_client = Groq(api_key=os.getenv("GROQ_API_KEY"))
groq_model = "llama-3.3-70b-versatile"

exa_client = Exa(api_key=os.getenv("EXA_API"))


def generate_verification_query(text, speaker):
    prompt = f"""Someone said: "{text}"

The speaker is: {speaker}

Generate a specific, concise search query that would verify if this statement is factually true or false. The query should be specific enough to fact-check this exact claim. Return ONLY the search query, nothing else."""
    try:
        response = groq_client.chat.completions.create(
            model=groq_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
            max_tokens=100
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error generating query: {e}"


def search_with_exa(query):
    try:
        response = exa_client.search(
            query=query,
            num_results=10,
            type="neural"
        )
        sources = []
        for result in response.results:
            sources.append({
                "title": result.title or "No title",
                "url": result.url,
                "text": result.text[:500] if result.text else ""
            })
        return sources
    except Exception as e:
        return f"Error searching: {e}"


def agent_claim_made(claim, speaker, sources):
    sources_text = "\n\n".join([
        f"Source {i+1}: {s['title']}\nURL: {s['url']}\nContent: {s['text']}"
        for i, s in enumerate(sources)
    ])
    source_refs = "\n".join([f"Source {i+1}: [{s['title']}]({s['url']})" for i, s in enumerate(sources)])
    prompt = f"""Based on the verification sources below, answer ONLY this question:

CLAIM: "{claim}"
SPEAKER: {speaker}

SOURCES:
{sources_text}

## 1. Was this claim made by the sayer?
[Answer based on sources - did this person actually make this statement? Provide a clear, evidence-based answer with specific citations. 
IMPORTANT: Inline cite sources using markdown links like this: "According to [Source 1](URL), ..." or "As stated in [Source 2](URL), ..."
Use the exact source titles and URLs from this reference list:
{source_refs}]"""
    try:
        response = groq_client.chat.completions.create(
            model=groq_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.2,
            max_tokens=800
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error: {e}"


def agent_action_done(claim, speaker, sources):
    sources_text = "\n\n".join([
        f"Source {i+1}: {s['title']}\nURL: {s['url']}\nContent: {s['text']}"
        for i, s in enumerate(sources)
    ])
    source_refs = "\n".join([f"Source {i+1}: [{s['title']}]({s['url']})" for i, s in enumerate(sources)])
    prompt = f"""Based on the verification sources below, answer ONLY this question:

CLAIM: "{claim}"
SPEAKER: {speaker}

SOURCES:
{sources_text}

## 2. Was this action/event done by the sayer?
[Answer based on sources - did the person actually do what they claimed? Provide a clear, evidence-based answer with specific citations. 
IMPORTANT: Inline cite sources using markdown links like this: "According to [Source 1](URL), ..." or "As stated in [Source 2](URL), ..."
Use the exact source titles and URLs from this reference list:
{source_refs}]"""
    try:
        response = groq_client.chat.completions.create(
            model=groq_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.2,
            max_tokens=800
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error: {e}"


def agent_consequences(claim, speaker, sources):
    sources_text = "\n\n".join([
        f"Source {i+1}: {s['title']}\nURL: {s['url']}\nContent: {s['text']}"
        for i, s in enumerate(sources)
    ])
    source_refs = "\n".join([f"Source {i+1}: [{s['title']}]({s['url']})" for i, s in enumerate(sources)])
    prompt = f"""Based on the verification sources below, answer ONLY this question:

CLAIM: "{claim}"
SPEAKER: {speaker}

SOURCES:
{sources_text}

## 3. What were the consequences of this act/statement?
[Detail the consequences, outcomes, or impact based on sources. Provide a clear, evidence-based answer with specific citations. 
IMPORTANT: Inline cite sources using markdown links like this: "According to [Source 1](URL), ..." or "As stated in [Source 2](URL), ..."
Use the exact source titles and URLs from this reference list:
{source_refs}]"""
    try:
        response = groq_client.chat.completions.create(
            model=groq_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.2,
            max_tokens=800
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return f"Error: {e}"


def agent_sources(sources):
    prompt = f"""Format these sources as clickable markdown links, one per line:

SOURCES:
{chr(10).join([f"{i+1}. {s['title']} - {s['url']}" for i, s in enumerate(sources)])}

Return ONLY the markdown list, nothing else."""
    try:
        response = groq_client.chat.completions.create(
            model=groq_model,
            messages=[{"role": "user", "content": prompt}],
            temperature=0.1,
            max_tokens=500
        )
        return response.choices[0].message.content.strip()
    except Exception as e:
        return "\n".join([f"- [{s['title']}]({s['url']})" for s in sources])


def verify_claim(text, speaker, progress=gr.Progress()):
    if not text or not speaker:
        return "Please provide both claim and speaker name."
    
    progress(0.1, desc="Generating search query...")
    query = generate_verification_query(text, speaker)
    if query.startswith("Error"):
        return query
    
    progress(0.3, desc="Searching Exa API...")
    sources = search_with_exa(query)
    if isinstance(sources, str) and sources.startswith("Error"):
        return sources
    
    if not sources:
        return "No sources found for verification."
    
    progress(0.5, desc="Agent 1: Verifying claim attribution...")
    part1 = agent_claim_made(text, speaker, sources)
    
    progress(0.65, desc="Agent 2: Verifying action/event...")
    part2 = agent_action_done(text, speaker, sources)
    
    progress(0.8, desc="Agent 3: Analyzing consequences...")
    part3 = agent_consequences(text, speaker, sources)
    
    progress(0.9, desc="Agent 4: Formatting sources...")
    part4 = agent_sources(sources)
    
    progress(1.0, desc="Complete!")
    
    return f"""## 1. Was this claim made by the sayer?
{part1}

## 2. Was this action/event done by the sayer?
{part2}

## 3. What were the consequences of this act/statement?
{part3}

## Sources
{part4}"""


with gr.Blocks() as demo:
    gr.Markdown("## Fact Verification")
    
    gr.Markdown("Enter a claim and who said it, then click Verify. Four specialized agents will analyze the evidence.")
    
    with gr.Row():
        selected_text = gr.Textbox(label="Claim", placeholder="Enter the claim to fact-check...")
        speaker = gr.Textbox(label="Who said this?", placeholder="e.g., Jeff Bezos, Elon Musk, etc.")
    
    verify_btn = gr.Button("Verify Claim", variant="primary", size="lg")
    status_msg = gr.Markdown(value="", visible=False)
    verification_output = gr.Markdown(label="Verification Result")
    
    def start_verification(text, speaker):
        # Show status message and make it visible
        return gr.update(value="🤖 **Agents are handling your research — just sit tight!**", visible=True)
    
    verify_btn.click(
        fn=start_verification,
        inputs=[selected_text, speaker],
        outputs=status_msg
    ).then(
        verify_claim,
        inputs=[selected_text, speaker],
        outputs=verification_output,
        show_progress="full"
    ).then(
        lambda: gr.update(visible=False),
        outputs=status_msg
    )

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