from fastapi import FastAPI, UploadFile, File
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel
from final import predict_news, get_gemini_analysis
import os
from tempfile import NamedTemporaryFile
from knowledge_graph_generator import KnowledgeGraphBuilder
import networkx as nx
import plotly.graph_objects as go

app = FastAPI()

# Add CORS middleware
app.add_middleware(
    CORSMiddleware,
    allow_origins=["http://localhost:5173"],  # Your React app's URL
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

# Rest of your code remains the same
class NewsInput(BaseModel):
    text: str

@app.post("/analyze")
async def analyze_news(news: NewsInput):
    prediction = predict_news(news.text)
    gemini_analysis = get_gemini_analysis(news.text)
    
    return {
        "prediction": prediction,
        "detailed_analysis": gemini_analysis
    }

    
# @app.post("/detect-deepfake")
# async def detect_deepfake(image: UploadFile = File(...)):
#     try:
#         # Save uploaded image temporarily
#         with NamedTemporaryFile(delete=False, suffix='.jpg') as temp_file:
#             contents = await image.read()
#             temp_file.write(contents)
#             temp_file_path = temp_file.name

#         # Use your existing deepfake detection function
#         from deepfake2.testing2 import predict_image  # Use your existing function
#         result = predict_image(temp_file_path)
        
#         # Clean up temp file
#         os.remove(temp_file_path)
        
#         return result
    # except Exception as e:
    #     return {"error": str(e)}, 500

@app.post("/detect-deepfake")
async def detect_deepfake(file: UploadFile = File(...)):
    try:
        # Save uploaded file temporarily
        with NamedTemporaryFile(delete=False, suffix=os.path.splitext(file.filename)[1]) as temp_file:
            contents = await file.read()
            temp_file.write(contents)
            temp_file_path = temp_file.name

        # Import functions from testing2.py
        from deepfake2.testing2 import predict_image, predict_video

        # Use appropriate function based on file type
        if file.filename.lower().endswith('.mp4'):
            result = predict_video(temp_file_path)
            file_type = "video"
        else:
            result = predict_image(temp_file_path)
            file_type = "image"
        
        # Clean up temp file
        os.remove(temp_file_path)
        
        return {
            "result": result,
            "file_type": file_type
        }
            
    except Exception as e:
        return {"error": str(e)}, 500

@app.post("/generate-knowledge-graph")
async def generate_knowledge_graph(news: NewsInput):
    kg_builder = KnowledgeGraphBuilder()
    is_fake = predict_news(news.text) == "FAKE"
    kg_builder.update_knowledge_graph(news.text, not is_fake)
    
    pos = nx.spring_layout(kg_builder.knowledge_graph)
    
    # Create edge traces with different colors
    edge_trace = go.Scatter(
        x=[], y=[], 
        line=dict(
            width=2, 
            color='rgba(255,0,0,0.7)' if is_fake else 'rgba(0,255,0,0.7)'  # Using rgba for transparency
        ),
        hoverinfo='none', 
        mode='lines'
    )
    
    node_trace = go.Scatter(
        x=[], y=[], 
        mode='markers+text',
        hoverinfo='text', 
        textposition='top center',
        marker=dict(
            size=15, 
            color='white',
            line=dict(width=2, color='black')
        ),
        text=[]
    )

    # Add edges to visualization
    for edge in kg_builder.knowledge_graph.edges():
        x0, y0 = pos[edge[0]]
        x1, y1 = pos[edge[1]]
        edge_trace['x'] += (x0, x1, None)
        edge_trace['y'] += (y0, y1, None)

    # Add nodes to visualization
    for node in kg_builder.knowledge_graph.nodes():
        x, y = pos[node]
        node_trace['x'] += (x,)
        node_trace['y'] += (y,)
        node_trace['text'] += (node,)

    fig = go.Figure(data=[edge_trace, node_trace],
                   layout=go.Layout(
                       showlegend=False,
                       hovermode='closest',
                       margin=dict(b=0,l=0,r=0,t=0),
                       xaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                       yaxis=dict(showgrid=False, zeroline=False, showticklabels=False),
                       plot_bgcolor='rgba(0,0,0,0)',
                       paper_bgcolor='rgba(0,0,0,0)'
                   ))
    
    return fig.to_html()