Krish Patel

Seperated knowledge graph and Model responses

4adafc2 almost 2 years ago

4.58 kB

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()

	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()