Update app.py

app.py

@@ -1,61 +1,61 @@
-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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+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 = "Vaishu16/QC_fine_tuned_model"
+    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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