Upload 2 files

app.py

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+import streamlit as st
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+
+# Load the fine-tuned model and tokenizer
+@st.cache_resource  # Cache model to avoid reloading
+def load_model():
+    model_directory = "C:/Users/DELL/Desktop/QC_streamlit/QC_fine_tuned_smollm2_360m_instruct_3_epoch"
+    model = AutoModelForCausalLM.from_pretrained(model_directory)
+    tokenizer = AutoTokenizer.from_pretrained(model_directory)
+    return model, tokenizer
+
+# Load model and tokenizer
+model, tokenizer = load_model()
+
+# Create a pipeline
+question_completion_pipeline = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    device=-1   
+)
+
+# Streamlit UI
+st.title("Question Completion Model")
+st.write("Provide a partial question, and the model will complete it.")
+
+partial_question = st.text_input("Enter a partial question:", "")
+
+if st.button("Complete Question"):
+    if partial_question.strip():
+        output = question_completion_pipeline(
+            partial_question,
+            max_length=60,
+            num_return_sequences=1,
+            do_sample=True
+        )
+       
+        completed_question = output[0]["generated_text"]
+        st.success(f"Completed Question: {completed_question}")    
+        
+    else:
+        st.warning("Please enter a partial question.")
+
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requirements.txt

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+streamlit
+transformers
+torch