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README.md

@@ -1,11 +1,11 @@
----
-title: Ani
-emoji: 💻
-colorFrom: indigo
-colorTo: pink
-sdk: docker
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: JudicaBOT
+emoji: 🐢
+colorFrom: blue
+colorTo: gray
+sdk: docker
+pinned: false
+license: apache-2.0
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from fastapi import APIRouter, HTTPException
+from pydantic import BaseModel
+from pathlib import Path
+import os
+from langchain_community.vectorstores import FAISS
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain.prompts import PromptTemplate
+from langchain_together import Together
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.chains import ConversationalRetrievalChain
+
+# Set the API key for Together.ai
+TOGETHER_AI_API = os.getenv("TOGETHER_AI_API", "1c27fe0df51a29edee1bec6b4b648b436cc80cf4ccc36f56de17272d9e663cbd")
+
+# Ensure proper cache directory is available for models
+os.environ['TRANSFORMERS_CACHE'] = '/tmp/cache'
+
+# Initialize FastAPI Router
+app = APIRouter()
+
+# Lazy loading of large models (only load embeddings and index when required)
+embeddings = HuggingFaceEmbeddings(
+    model_name="nomic-ai/nomic-embed-text-v1",
+    model_kwargs={"trust_remote_code": True, "revision": "289f532e14dbbbd5a04753fa58739e9ba766f3c7"},
+)
+
+index_path = Path("index.faiss")
+if not index_path.exists():
+    raise FileNotFoundError("FAISS index not found. Please generate it and place it in 'ipc_vector_db'.")
+
+# Load the FAISS index
+db = FAISS.load_local("AI LAW CHATBOT", embeddings, allow_dangerous_deserialization=True)
+db_retriever = db.as_retriever(search_type="similarity", search_kwargs={"k": 4})
+
+# Define the prompt template for the legal chatbot
+prompt_template = """<s>[INST]This is a chat template and as a legal chatbot specializing in Indian Penal Code queries, your objective is to provide accurate and concise information.
+CONTEXT: {context}
+CHAT HISTORY: {chat_history}
+QUESTION: {question}
+ANSWER:
+</s>[INST]"""
+prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question", "chat_history"])
+
+# Set up the LLM (Large Language Model) for the chatbot
+llm = Together(
+    model="mistralai/Mistral-7B-Instruct-v0.2",
+    temperature=0.5,
+    max_tokens=1024,
+    together_api_key=TOGETHER_AI_API,
+)
+
+# Set up memory for conversational context
+memory = ConversationBufferWindowMemory(k=2, memory_key="chat_history", return_messages=True)
+
+# Create the conversational retrieval chain with the LLM and retriever
+qa_chain = ConversationalRetrievalChain.from_llm(
+    llm=llm,
+    memory=memory,
+    retriever=db_retriever,
+    combine_docs_chain_kwargs={"prompt": prompt},
+)
+
+# Input schema for chat requests
+class ChatRequest(BaseModel):
+    question: str
+    chat_history: str
+
+# POST endpoint to handle chat requests
+@app.post("/chat/")
+async def chat(request: ChatRequest):
+    try:
+        # Prepare the input data
+        inputs = {"question": request.question, "chat_history": request.chat_history}
+        # Run the chain to get the answer
+        result = qa_chain(inputs)
+        return {"answer": result["answer"]}
+    except Exception as e:
+        # Return an error if something goes wrong
+        raise HTTPException(status_code=500, detail=f"Error processing request: {str(e)}")
+
+# GET endpoint to check if the API is running
+@app.get("/")
+async def root():
+    return {"message": "LawGPT API is running."}

requirements.txt

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+fastapi>=0.100.0
+uvicorn[standard]>=0.22.0
+pydantic>=2.0.0
+langchain_together
+langchain-huggingface
+langchain_community
+einops
+transformers
+huggingface_hub
+faiss-cpu