Update app.py

app.py

@@ -4,6 +4,7 @@ os.environ["XDG_CACHE_HOME"] = "/tmp"
 os.makedirs("/tmp/matplotlib", exist_ok=True)
 os.makedirs("/tmp/data", exist_ok=True)
 os.makedirs("/tmp/models_cache", exist_ok=True)
+os.makedirs("/tmp/static", exist_ok=True)
 from fastapi import FastAPI
 import io
 import time
@@ -12,6 +13,9 @@ import tempfile
 import numpy as np
 import matplotlib.pyplot as plt
 import pdfplumber
+from fastapi.responses import FileResponse, HTMLResponse
+import pandas as pd
+import plotly.express as px
 import spacy
 import torch
 import sqlite3
@@ -491,6 +495,8 @@ def process_audio_to_text(audio_file_path):
 
 def extract_named_entities(text):
     """Extracts named entities from legal text."""
+    if nlp is None:
+        return [{"entity": "NLP model not available", "label": "N/A"}]
     max_length = 10000
     entities = []
     for i in range(0, len(text), max_length):
@@ -519,6 +525,8 @@ def extract_context_for_risk_terms(text, risk_keywords, window=1):
     """
     Extracts and summarizes the context around risk terms.
     """
+    if nlp is None or summarizer is None:
+        return {category: "NLP/summarizer model not available" for category in risk_keywords}
     doc = nlp(text)
     sentences = list(doc.sents)
     risk_contexts = {category: [] for category in risk_keywords}
@@ -1406,7 +1414,122 @@ async def paypal_webhook(request: Request):
         logger.error(f"Webhook processing error: {str(e)}")
         # Return 200 even on error to acknowledge receipt to PayPal
         return {"status": "error", "message": str(e)}
+@app.get("/download_risk_chart")
+async def download_risk_chart():
+    """Generate and return a risk assessment chart as an image file."""
+    try:
+        risk_scores = {
+            "Liability": 11,
+            "Termination": 12,
+            "Indemnification": 10,
+            "Payment Risk": 41,
+            "Insurance": 71
+        }
+        plt.figure(figsize=(8, 5))
+        plt.bar(risk_scores.keys(), risk_scores.values(), color='red')
+        plt.xlabel("Risk Categories")
+        plt.ylabel("Risk Score")
+        plt.title("Legal Risk Assessment")
+        plt.xticks(rotation=30)
+        risk_chart_path = os.path.join(STATIC_DIR, "risk_chart.png")
+        plt.savefig(risk_chart_path)
+        plt.close()
+        return FileResponse(risk_chart_path, media_type="image/png", filename="risk_chart.png")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating risk chart: {str(e)}")
+
+@app.get("/download_risk_pie_chart")
+async def download_risk_pie_chart():
+    try:
+        risk_scores = {
+            "Liability": 11,
+            "Termination": 12,
+            "Indemnification": 10,
+            "Payment Risk": 41,
+            "Insurance": 71
+        }
+        plt.figure(figsize=(6, 6))
+        plt.pie(risk_scores.values(), labels=risk_scores.keys(), autopct='%1.1f%%', startangle=90)
+        plt.title("Legal Risk Distribution")
+        pie_chart_path = os.path.join(STATIC_DIR, "risk_pie_chart.png")
+        plt.savefig(pie_chart_path)
+        plt.close()
+        return FileResponse(pie_chart_path, media_type="image/png", filename="risk_pie_chart.png")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating pie chart: {str(e)}")
 
+@app.get("/download_risk_radar_chart")
+async def download_risk_radar_chart():
+    try:
+        risk_scores = {
+            "Liability": 11,
+            "Termination": 12,
+            "Indemnification": 10,
+            "Payment Risk": 41,
+            "Insurance": 71
+        }
+        categories = list(risk_scores.keys())
+        values = list(risk_scores.values())
+        categories += categories[:1]
+        values += values[:1]
+        angles = np.linspace(0, 2 * np.pi, len(categories), endpoint=False).tolist()
+        angles += angles[:1]
+        fig, ax = plt.subplots(figsize=(6, 6), subplot_kw=dict(polar=True))
+        ax.plot(angles, values, 'o-', linewidth=2)
+        ax.fill(angles, values, alpha=0.25)
+        ax.set_thetagrids(np.degrees(angles[:-1]), categories)
+        ax.set_title("Legal Risk Radar Chart", y=1.1)
+        radar_chart_path = os.path.join(STATIC_DIR, "risk_radar_chart.png")
+        plt.savefig(radar_chart_path)
+        plt.close()
+        return FileResponse(radar_chart_path, media_type="image/png", filename="risk_radar_chart.png")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating radar chart: {str(e)}")
+
+@app.get("/download_risk_trend_chart")
+async def download_risk_trend_chart():
+    try:
+        dates = ["2025-01-01", "2025-02-01", "2025-03-01", "2025-04-01"]
+        risk_history = {
+            "Liability": [10, 12, 11, 13],
+            "Termination": [12, 15, 14, 13],
+            "Indemnification": [9, 10, 11, 10],
+            "Payment Risk": [40, 42, 41, 43],
+            "Insurance": [70, 69, 71, 72]
+        }
+        plt.figure(figsize=(10, 6))
+        for category, scores in risk_history.items():
+            plt.plot(dates, scores, marker='o', label=category)
+        plt.xlabel("Date")
+        plt.ylabel("Risk Score")
+        plt.title("Historical Legal Risk Trends")
+        plt.xticks(rotation=45)
+        plt.legend()
+        trend_chart_path = os.path.join(STATIC_DIR, "risk_trend_chart.png")
+        plt.savefig(trend_chart_path, bbox_inches="tight")
+        plt.close()
+        return FileResponse(trend_chart_path, media_type="image/png", filename="risk_trend_chart.png")
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating trend chart: {str(e)}")
+
+@app.get("/interactive_risk_chart", response_class=HTMLResponse)
+async def interactive_risk_chart():
+    try:
+        risk_scores = {
+            "Liability": 11,
+            "Termination": 12,
+            "Indemnification": 10,
+            "Payment Risk": 41,
+            "Insurance": 71
+        }
+        df = pd.DataFrame({
+            "Risk Category": list(risk_scores.keys()),
+            "Risk Score": list(risk_scores.values())
+        })
+        fig = px.bar(df, x="Risk Category", y="Risk Score", title="Interactive Legal Risk Assessment")
+        return fig.to_html()
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Error generating interactive chart: {str(e)}")
 # Add this to your startup code
 @app.on_event("startup")
 async def startup_event():