up

app.py

@@ -6,7 +6,7 @@ import docx2txt
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import cosine_similarity
 import difflib
-from huggingface_hub import InferenceApi  # Import Hugging Face API
+from huggingface_hub import InferenceClient  # Import Hugging Face API
 
 # ========== CONFIG ========== 
 st.set_page_config(page_title="📑 Contract Analyzer", layout="wide")
@@ -15,10 +15,10 @@ st.set_page_config(page_title="📑 Contract Analyzer", layout="wide")
 
 # Tải mô hình Hugging Face từ Hub
 @st.cache_resource
-def load_inference_api():
-    return InferenceApi(repo_id="HuggingFaceH4/zephyr-7b-beta")  # Mô hình Zephyr
+def load_inference_client():
+    return InferenceClient(repo_id="HuggingFaceH4/zephyr-7b-beta")  # Mô hình Zephyr
 
-inference_api = load_inference_api()
+inference_client = load_inference_client()
 
 def extract_text_from_pdf(uploaded_file):
     try:
@@ -78,7 +78,7 @@ def compute_similarity(text1, text2):
 def query_zephyr_model(text1, text2, question):
     prompt = f"Compare the following two contracts and answer the question:\nText 1: {text1}\nText 2: {text2}\nQuestion: {question}"
     try:
-        result = inference_api(inputs=prompt)
+        result = inference_client(inputs=prompt)
         return result['generated_text']
     except Exception as e:
         st.error(f"Error querying the model: {e}")
@@ -129,24 +129,16 @@ def main():
     user_question = st.text_input("Enter your question about the contracts:")
 
     if user_question and st.button("Analyze Question"):
-        col1, col2 = st.columns(2)
-        with col1:
-            st.subheader("Answer from Document 1")
-            with st.spinner("Analyzing..."):
-                try:
-                    pred1 = query_zephyr_model(text1, text2, user_question)
-                    st.success(pred1)
-                except Exception as e:
-                    st.error(f"Failed on Document 1: {e}")
+        col = st.columns(1)
 
-        with col2:
-            st.subheader("Answer from Document 2")
+        with col:
+            st.subheader("Answer from Document")
             with st.spinner("Analyzing..."):
                 try:
-                    pred2 = query_zephyr_model(text1, text2, user_question)
-                    st.success(pred2)
+                    pred = query_zephyr_model(text1, text2, user_question)
+                    st.success(pred)
                 except Exception as e:
-                    st.error(f"Failed on Document 2: {e}")
+                    st.error(f"Failed on Document: {e}")
 
 if __name__ == "__main__":
     main()