Create app.py

app.py

@@ -0,0 +1,42 @@
+from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llama_cpp import Llama
+from datasets import load_metric
+
+pipe = pipeline("text-generation", model="varma007ut/Indian_Legal_Assitant")
+
+prompt = "Summarize the key points of the Indian Contract Act, 1872:"
+result = pipe(prompt, max_length=200)
+print(result[0]['generated_text'])
+
+
+tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assitant")
+model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assitant")
+
+prompt = "What are the fundamental rights in the Indian Constitution?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0]))
+
+
+
+llm = Llama.from_pretrained(
+    repo_id="varma007ut/Indian_Legal_Assitant",
+    filename="ggml-model-q4_0.gguf",  # Replace with the actual GGUF filename if different
+)
+
+response = llm.create_chat_completion(
+    messages = [
+        {
+            "role": "user",
+            "content": "Explain the concept of judicial review in India."
+        }
+    ]
+)
+
+print(response['choices'][0]['message']['content'])
+
+
+bleu = load_metric("bleu")
+predictions = model.generate(encoded_input)
+results = bleu.compute(predictions=predictions, references=references)