🚀 Auto-deploy from GitHub Action

.github/workflows/sync_env_to_huggingface.yml

@@ -0,0 +1,59 @@
+name: 🔐 Sync .env to Hugging Face Secrets
+
+on:
+  push:
+    branches:
+      - main  # or any branch you want
+
+jobs:
+  sync:
+    runs-on: ubuntu-latest
+
+    steps:
+      - name: Checkout repo
+        uses: actions/checkout@v4
+
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install huggingface_hub python-dotenv requests
+
+      - name: Sync .env to Hugging Face Space Secrets
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+          DOTENV_CONTENT: ${{ secrets.DOTENV_CONTENT }}
+        run: |
+          python - <<'EOF'
+          import os, requests
+          from dotenv import dotenv_values
+
+          HF_TOKEN = os.getenv("HF_TOKEN")
+          DOTENV_CONTENT = os.getenv("DOTENV_CONTENT")
+          SPACE_ID = "ali4568/LawMadad"  # change this
+
+          # Parse .env contents
+          lines = DOTENV_CONTENT.strip().splitlines()
+          secrets = {}
+          for line in lines:
+              if line and not line.startswith("#") and "=" in line:
+                  key, value = line.split("=", 1)
+                  secrets[key.strip()] = value.strip()
+
+          # Hugging Face API endpoint
+          url = f"https://huggingface.co/api/spaces/{SPACE_ID}/secrets"
+          headers = {"Authorization": f"Bearer {HF_TOKEN}"}
+
+          for key, value in secrets.items():
+              print(f"🔁 Syncing {key} ...")
+              payload = {"key": key, "value": value}
+              resp = requests.post(url, headers=headers, json=payload)
+              if resp.status_code in (200, 201):
+                  print(f"✅ {key} synced.")
+              elif resp.status_code == 409:
+                  print(f"🌀 {key} already exists, updating...")
+                  requests.put(f"{url}/{key}", headers=headers, json={"value": value})
+              else:
+                  print(f"❌ Failed to sync {key}: {resp.status_code} - {resp.text}")
+
+          print("🎯 All .env variables synced to Hugging Face!")
+          EOF

.gitignore

@@ -0,0 +1 @@
+.env

app.py

@@ -1,409 +1,3 @@
-# # Import necessary libraries
-# import warnings
-# import os
-# import nltk
-
-# # Ensure NLTK uses our custom data directory
-# nltk.data.path = ['/app/nltk_data'] + nltk.data.path
-
-# # Import other libraries
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# from llama_index.core import (
-#     VectorStoreIndex,
-#     StorageContext,
-#     ServiceContext,
-#     load_index_from_storage,
-#     Document
-# )
-# from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-# from llama_index.llms.groq import Groq
-# import pdfplumber
-
-# # Suppress warnings
-# warnings.filterwarnings('ignore')
-
-# # Initialize FastAPI app
-# app = FastAPI()
-
-# # Define the request model
-# class QueryRequest(BaseModel):
-#     query: str
-
-# # Set up the GROQ API key - use environment variable for security
-# GROQ_API_KEY = "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"
-
-# # Define the context path for PDF files
-# input_files = [
-#     "civil.pdf",
-#     "constitution.pdf",
-#     "criminal.pdf",
-#     "family.pdf",
-# ]
-
-# # Preprocessing function for PDF text extraction
-# def extract_text_from_pdf(file_path):
-#     text_data = []
-#     try:
-#         with pdfplumber.open(file_path) as pdf:
-#             for page in pdf.pages:
-#                 text = page.extract_text()
-#                 if text:
-#                     text_data.append(text)
-#         return "\n".join(text_data)
-#     except Exception as e:
-#         print(f"Error extracting text from {file_path}: {str(e)}")
-#         return ""
-
-# # Function to initialize the index
-# def initialize_index():
-#     # Check if storage exists
-#     persist_dir = "./storage_law_app"
-#     if os.path.exists(persist_dir) and os.listdir(persist_dir):
-#         print("Loading existing index...")
-#         # Initialize embedding model
-#         embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-#         # Initialize LLM
-#         llm = Groq(model="llama-3.1-8b-instant", api_key=GROQ_API_KEY)
-#         service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
-        
-#         # Reload the index
-#         storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
-#         return load_index_from_storage(storage_context, service_context=service_context)
-#     else:
-#         print("Creating new index...")
-#         os.makedirs(persist_dir, exist_ok=True)
-        
-#         # Load and preprocess documents
-#         documents = []
-#         for file in input_files:
-#             if os.path.exists(file):
-#                 content = extract_text_from_pdf(file)
-#                 if content:
-#                     documents.append(Document(text=content))
-#             else:
-#                 print(f"Warning: File {file} not found")
-        
-#         if not documents:
-#             print("Warning: No documents were loaded")
-#             # Create a dummy document if no documents are found
-#             documents = [Document(text="This is a placeholder document as no actual documents were found.")]
-        
-#         # Initialize embedding model
-#         embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-#         # Initialize LLM
-#         llm = Groq(model="llama-3.1-8b-instant", api_key=GROQ_API_KEY)
-#         service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
-        
-#         # Build and persist vector index
-#         vector_index = VectorStoreIndex.from_documents(
-#             documents=documents,
-#             service_context=service_context,
-#             show_progress=True
-#         )
-#         vector_index.storage_context.persist(persist_dir=persist_dir)
-#         return vector_index
-
-# # Define the context for legal queries
-# LEGAL_CONTEXT = """
-# Context: Provide legal guidance based on the Pakistani legal framework.
-# Task: Analyze the query and provide a structured response with headings and bullet points.
-# The format should be:
-# 1. **Introduction/Overview**: A brief overview of the law or section.
-# 2. **Section Description**: Explain what this section does, including its purpose and scope.
-# 3. **Legal Provisions**: Highlight the key legal provisions or clauses under the specified section.
-# 4. **Punishments**: Explicitly mention the punishments with references if applicable.
-# 5. **Related Precedents**: Summarize any relevant legal precedents or landmark cases along with their results.
-# 6. **Conclusion/Recommendations**: Conclude with advice or recommendations tailored to the query.
-# """
-
-# # Initialize the index at startup
-# @app.on_event("startup")
-# async def startup_event():
-#     global index
-#     index = initialize_index()
-
-# # API endpoint for querying the model
-# @app.post("/query/")
-# async def query_model(request: QueryRequest):
-#     try:
-#         # Prepare the query engine
-#         query_engine = index.as_query_engine()
-        
-#         # Append context to user query
-#         full_query = f"{LEGAL_CONTEXT}\n\nQuery: {request.query}"
-        
-#         # Query the index
-#         response = query_engine.query(full_query)
-#         return {"response": response.response}
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e))
-
-# # Add a simple root endpoint for API documentation
-# @app.get("/")
-# async def root():
-#     return {
-#         "message": "Pakistani Legal Assistant API",
-#         "usage": "Send POST requests to /query/ with a JSON body containing the 'query' field"
-#     }
-
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=7860)
-
-
-#-----------------------------------------------------------------------------------
-#-----------------------------------------------------------------------------------
-
-
-# import warnings
-# import os
-# import nltk
-# import re
-
-# # Ensure NLTK uses our custom data directory
-# nltk.data.path = ['/app/nltk_data'] + nltk.data.path
-
-# # Import other libraries
-# from fastapi import FastAPI, HTTPException
-# from pydantic import BaseModel
-# from llama_index.core import (
-#     VectorStoreIndex,
-#     StorageContext,
-#     ServiceContext,
-#     load_index_from_storage,
-#     Document
-# )
-# from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-# from llama_index.llms.groq import Groq
-# import pdfplumber
-
-# # Suppress warnings
-# warnings.filterwarnings('ignore')
-
-# # Initialize FastAPI app
-# app = FastAPI()
-
-# # Define the request model
-# class QueryRequest(BaseModel):
-#     query: str
-
-# # Set up the GROQ API key - use environment variable for security
-# GROQ_API_KEY = "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"
-
-# # Define the context path for PDF files
-# input_files = [
-#     "civil.pdf",
-#     "constitution.pdf",
-#     "criminal.pdf",
-#     "family.pdf",
-#     "civil_1.pdf",
-#     "civil_2.pdf",
-#     "property_final.pdf",
-#     "criminal_final.pdf",
-#     "family_final.pdf"
-# ]
-
-# # Preprocessing function for PDF text extraction
-# def extract_text_from_pdf(file_path):
-#     text_data = []
-#     try:
-#         with pdfplumber.open(file_path) as pdf:
-#             for page in pdf.pages:
-#                 text = page.extract_text()
-#                 if text:
-#                     text_data.append(text)
-#         return "\n".join(text_data)
-#     except Exception as e:
-#         print(f"Error extracting text from {file_path}: {str(e)}")
-#         return ""
-
-# # Function to initialize the index
-# def initialize_index():
-#     # Check if storage exists
-#     persist_dir = "./storage_law_app"
-#     if os.path.exists(persist_dir) and os.listdir(persist_dir):
-#         print("Loading existing index...")
-#         # Initialize embedding model
-#         embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-#         # Initialize LLM
-#         llm = Groq(model="llama-3.1-8b-instant", api_key=GROQ_API_KEY)
-#         service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
-        
-#         # Reload the index
-#         storage_context = StorageContext.from_defaults(persist_dir=persist_dir)
-#         return load_index_from_storage(storage_context, service_context=service_context)
-#     else:
-#         print("Creating new index...")
-#         os.makedirs(persist_dir, exist_ok=True)
-        
-#         # Load and preprocess documents
-#         documents = []
-#         for file in input_files:
-#             if os.path.exists(file):
-#                 content = extract_text_from_pdf(file)
-#                 if content:
-#                     documents.append(Document(text=content))
-#             else:
-#                 print(f"Warning: File {file} not found")
-        
-#         if not documents:
-#             print("Warning: No documents were loaded")
-#             # Create a dummy document if no documents are found
-#             documents = [Document(text="This is a placeholder document as no actual documents were found.")]
-        
-#         # Initialize embedding model
-#         embed_model = HuggingFaceEmbedding(model_name="sentence-transformers/all-MiniLM-L6-v2")
-#         # Initialize LLM
-#         llm = Groq(model="llama-3.1-8b-instant", api_key=GROQ_API_KEY)
-#         service_context = ServiceContext.from_defaults(embed_model=embed_model, llm=llm)
-        
-#         # Build and persist vector index
-#         vector_index = VectorStoreIndex.from_documents(
-#             documents=documents,
-#             service_context=service_context,
-#             show_progress=True
-#         )
-#         vector_index.storage_context.persist(persist_dir=persist_dir)
-#         return vector_index
-
-# # Define the context for legal queries
-# LEGAL_CONTEXT = """
-# Context: Provide legal guidance based on the Pakistani legal framework.
-# Task: Analyze the query and provide a structured response with headings and bullet points.
-# The format should be:
-# 1. **Introduction/Overview**: A brief overview of the law or section.
-# 2. **Section Description**: Explain what this section does, including its purpose and scope.
-# 3. **Legal Provisions**: Highlight the key legal provisions or clauses under the specified section.
-# 4. **Punishments**: Explicitly mention the punishments with references if applicable.
-# 5. **Related Precedents**: Summarize any relevant legal precedents or landmark cases along with their results.
-# 6. **Conclusion/Recommendations**: Conclude with advice or recommendations tailored to the query.
-# """
-
-# # Define general response templates for common non-legal queries
-# GENERAL_RESPONSES = {
-#     "greeting": "Hello! I'm your Pakistani Legal Assistant. I can help you with questions about Pakistani law, including civil law, criminal law, family law, and constitutional matters. How can I assist you today?",
-    
-#     "capabilities": "I'm a specialized Pakistani Legal Assistant that can help you with:\n\n"
-#                     "- Information about Pakistani civil, criminal, family, and constitutional law\n"
-#                     "- Legal provisions and sections with detailed explanations\n"
-#                     "- Applicable punishments under various legal provisions\n"
-#                     "- Legal precedents and relevant case law\n"
-#                     "- Recommendations on legal matters\n\n"
-#                     "Just ask me any legal question, and I'll provide a structured response based on Pakistani law.",
-    
-#     "default": "I'm your Pakistani Legal Assistant. I can help answer questions about Pakistani law. "
-#                "For legal queries, I'll provide detailed information with proper structure. "
-#                "How can I assist you with your legal questions today?"
-# }
-
-# # Function to detect if a query is a legal question or general conversation
-# def is_legal_query(query):
-#     # Convert to lowercase for easier matching
-#     query_lower = query.lower()
-    
-#     # Define patterns for greetings and capability questions
-#     greeting_patterns = [
-#         r'\b(hi|hello|hey|greetings|howdy|salam|assalam|namaste)\b',
-#         r'\bhow are you\b',
-#         r'\bnice to meet you\b'
-#     ]
-    
-#     capability_patterns = [
-#         r'\bwhat can you do\b',
-#         r'\bwhat are your capabilities\b',
-#         r'\bhow can you help\b',
-#         r'\bwhat do you know\b',
-#         r'\bwhat are you\b',
-#         r'\bwho are you\b',
-#         r'\bwhat is your purpose\b',
-#         r'\bhow do you work\b'
-#     ]
-    
-#     # Check if query matches any greeting patterns
-#     for pattern in greeting_patterns:
-#         if re.search(pattern, query_lower):
-#             return False, "greeting"
-    
-#     # Check if query matches any capability inquiry patterns
-#     for pattern in capability_patterns:
-#         if re.search(pattern, query_lower):
-#             return False, "capabilities"
-    
-#     # Legal keywords that suggest a legal query
-#     legal_keywords = [
-#         'law', 'legal', 'court', 'justice', 'right', 'constitution', 'section', 
-#         'crime', 'criminal', 'civil', 'family', 'divorce', 'marriage', 'inheritance',
-#         'punishment', 'penalty', 'fine', 'jail', 'prison', 'arrest', 'police',
-#         'judge', 'lawyer', 'attorney', 'defendant', 'plaintiff', 'accused',
-#         'trial', 'case', 'lawsuit', 'petition', 'appeal', 'witness', 'evidence',
-#         'contract', 'property', 'damages', 'compensation', 'regulation', 'statute',
-#         'act', 'provision', 'legislation', 'parliament', 'supreme court', 'high court',
-#         'district court', 'tribunal', 'code', 'penal', 'procedure'
-#     ]
-    
-#     # Check if the query contains legal keywords
-#     for keyword in legal_keywords:
-#         if keyword in query_lower:
-#             return True, None
-    
-#     # If the query is longer than 20 characters and not identified as greeting or capabilities,
-#     # assume it might be a legal query
-#     if len(query) > 20:
-#         return True, None
-    
-#     # Default to general response if we can't clearly identify
-#     return False, "default"
-
-# # Initialize the index at startup
-# @app.on_event("startup")
-# async def startup_event():
-#     global index
-#     index = initialize_index()
-
-# # API endpoint for querying the model
-# @app.post("/query/")
-# async def query_model(request: QueryRequest):
-#     try:
-#         # Determine if the query is legal or general
-#         is_legal, response_type = is_legal_query(request.query)
-        
-#         if not is_legal:
-#             # Return predefined general response
-#             return {"response": GENERAL_RESPONSES.get(response_type, GENERAL_RESPONSES["default"])}
-#         else:
-#             # Prepare the query engine for legal questions
-#             query_engine = index.as_query_engine()
-            
-#             # Append context to user query
-#             full_query = f"{LEGAL_CONTEXT}\n\nQuery: {request.query}"
-            
-#             # Query the index
-#             response = query_engine.query(full_query)
-#             return {"response": response.response}
-            
-#     except Exception as e:
-#         raise HTTPException(status_code=500, detail=str(e))
-
-# # Add a simple root endpoint for API documentation
-# @app.get("/")
-# async def root():
-#     return {
-#         "message": "Pakistani Legal Assistant API",
-#         "usage": "Send POST requests to /query/ with a JSON body containing the 'query' field"
-#     }
-
-
-# if __name__ == "__main__":
-#     import uvicorn
-#     uvicorn.run(app, host="0.0.0.0", port=7860)
-
-
-#-----------------------------------------------------------------------------------
-#-----------------------------------------------------------------------------------
-
-
 import warnings
 import os
 import nltk
@@ -437,7 +31,7 @@ class QueryRequest(BaseModel):
     query: str
 
 # Set up the GROQ API key - use environment variable for security
-GROQ_API_KEY = "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"
+GROQ_API_KEY = os.getenv("GROQ_API_KEY")
 
 # Define the context path for PDF files
 input_files = [