Update app.py

app.py

@@ -3,13 +3,12 @@
 # -----------------------------------------------------------
 from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
 from evaluate import load
-from llama_cpp import Llama
 
 # Pipeline for easy text generation
 pipe = pipeline(
     "text-generation",
     model="varma007ut/Indian_Legal_Assitant",
-    device_map="auto",       # auto GPU/CPU
+    device="cpu",
     max_length=200
 )
 
@@ -21,7 +20,7 @@ print(result[0]["generated_text"])
 
 
 # -----------------------------------------------------------
-# 2. TEXT GENERATION USING Transformer model.generate()
+# 2. TEXT GENERATION USING model.generate()
 # -----------------------------------------------------------
 
 tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assitant")
@@ -30,7 +29,13 @@ model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assitant")
 prompt2 = "What are the fundamental rights in the Indian Constitution?"
 inputs = tokenizer(prompt2, return_tensors="pt")
 
-outputs = model.generate(**inputs, max_length=200)
+outputs = model.generate(
+    **inputs,
+    max_length=200,
+    do_sample=True,
+    top_p=0.95
+)
+
 generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
 
 print("\n=== HF Model.generate Output ===")
@@ -38,39 +43,17 @@ print(generated_text)
 
 
 # -----------------------------------------------------------
-# 3. TEXT GENERATION USING LLAMA.CPP (GGUF MODEL)
-# -----------------------------------------------------------
-
-llm = Llama(
-    model_path="./ggml-model-q4_0.gguf",  # Ensure the file exists!
-    n_ctx=4096
-)
-
-response = llm.create_chat_completion(
-    messages=[
-        {
-            "role": "user",
-            "content": "Explain the concept of judicial review in India."
-        }
-    ]
-)
-
-print("\n=== LLaMA.cpp Output ===")
-print(response["choices"][0]["message"]["content"])
-
-
-# -----------------------------------------------------------
-# 4. BLEU SCORE EVALUATION (CLEAN + CORRECT)
+# 3. BLEU SCORE EVALUATION (SAFE FOR SPACES)
 # -----------------------------------------------------------
 
 bleu = load("bleu")
 
-# Prediction text from model
-pred_text = generated_text  # using output from HF model
+pred_text = generated_text  # prediction from HF model
 
-# Reference answer (provide your own actual correct reference)
-references = ["Fundamental Rights are guaranteed by the Constitution..."]
+# Reference answer (your true dataset reference goes here)
+references = ["Fundamental Rights are guaranteed by the Indian Constitution."]
 
+# BLEU expects tokenized text (split into words)
 results = bleu.compute(
     predictions=[pred_text.split()],
     references=[[ref.split() for ref in references]]
@@ -78,4 +61,3 @@ results = bleu.compute(
 
 print("\n=== BLEU Score ===")
 print(results)
-