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app.py

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+import os
+import json
+import uuid
+import gradio as gr
+import chromadb
+import numpy as np
+from pathlib import Path
+from sentence_transformers import SentenceTransformer
+from huggingface_hub import CommitScheduler
+from chromadb.errors import NotFoundError
+from openai import OpenAI
+
+# Load embedding model
+embed_model = SentenceTransformer("BAAI/bge-small-en-v1.5")
+
+# Load ChromaDB client
+chroma_client = chromadb.PersistentClient(path="./clause_index")
+try:
+    collection = chroma_client.get_collection("legal_clauses")
+except NotFoundError:
+    collection = None
+
+# Setup OpenAI/Hugging Face client
+client = OpenAI(
+    base_url="https://router.huggingface.co/featherless-ai/v1",
+    api_key=os.getenv("HF_TOKEN"),
+)
+
+# Prompt template
+system_message = """You are a legal AI assistant trained on contract clause examples from the CUAD dataset.
+If no clauses are retrieved from the database, infer the answer using your understanding of common contractual standards. and report that no clause retrieved"""
+user_template = """
+### Context:
+{context}
+
+### Question:
+{question}
+"""
+
+# Setup logging
+log_file = Path("logs/") / f"query_{uuid.uuid4()}.json"
+log_file.parent.mkdir(exist_ok=True)
+scheduler = CommitScheduler(
+    repo_id="legal-rag-output",
+    repo_type="dataset",
+    folder_path=log_file.parent,
+    path_in_repo="logs",
+    every=2
+)
+
+# Main QA function
+def predict(question):
+    try:
+        # Encode query
+        query_embedding = embed_model.encode([question], normalize_embeddings=True)[0]
+
+        # Default fallback context
+        context = "No relevant clauses were found in the database. Please answer using your legal understanding from the CUAD dataset."
+
+        # If collection exists, try retrieval
+        if collection:
+            try:
+                results = collection.query(
+                    query_embeddings=[query_embedding.tolist()],
+                    n_results=3
+                )
+                documents = results["documents"][0]
+                metadatas = results["metadatas"][0]
+
+                if documents:
+                    context = "\n\n".join(
+                        f"[Clause Type: {m['clause_type']}] {doc}"
+                        for doc, m in zip(documents, metadatas)
+                    )
+            except Exception:
+                context = "Due to an internal retrieval issue, please answer based on your legal knowledge from CUAD dataset."
+
+        # Construct prompt
+        prompt = [
+            {"role": "system", "content": system_message},
+            {"role": "user", "content": user_template.format(context=context, question=question)}
+        ]
+
+        # Generate response
+        stream = client.chat.completions.create(
+            model="mistralai/Mistral-7B-Instruct-v0.2",
+            messages=prompt,
+            temperature=0.4,
+            top_p=0.7,
+            stream=True
+        )
+
+        output = ""
+        for chunk in stream:
+            output += chunk.choices[0].delta.content or ""
+
+    except Exception as e:
+        output = f"An internal error occurred while generating the response: {str(e)}"
+
+    # Log to file
+    with scheduler.lock:
+        with log_file.open("a") as f:
+            f.write(json.dumps({
+                "question": question,
+                "context": context,
+                "response": output
+            }) + "\n")
+
+    return output
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Textbox(label="Enter your legal question:", lines=4),
+    outputs=gr.Textbox(label="Answer"),
+    title="⚖️ GL_LegalMind",
+    description="Ask contract-related legal questions. Answers are based on retrieved clauses or inferred from CUAD knowledge."
+)
+
+demo.queue()
+demo.launch()