Update src/streamlit_app.py

src/streamlit_app.py

@@ -7,8 +7,8 @@ from transformers import BertTokenizer, BertForSequenceClassification, AutoToken
 import asyncio
 import sys
 
-if sys.platform == "win32":
-    asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
+# if sys.platform == "win32":
+#     asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
 
 # import zipfile
 # zip_path = "bert-base-uncased.zip"
@@ -27,139 +27,140 @@ def prepare_tokenizer_folder():
     for file in tokenizer_files:
         if os.path.exists(file):
             shutil.move(file, os.path.join(folder_name, file))
+prepare_tokenizer_folder()
 # -------------------- Part 1: Prediction --------------------
-    
-@st.cache_resource
-def load_prediction_model():
-    prepare_tokenizer_folder()
-    # tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
-    tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
-    with open('label_encoder_new.pkl', 'rb') as f:
-        label_encoder = pickle.load(f)
-    id_to_class = {idx: class_name for idx, class_name in enumerate(label_encoder.classes_)}
-
-    # model = BertForSequenceClassification.from_pretrained('Divyanshu04/Issue_categorizer', num_labels=len(label_encoder.classes_))
-    model = AutoModelForSequenceClassification.from_pretrained('Divyanshu04/Issue_categorizer')
-    # model.load_state_dict(torch.load('Divyanshu04/Issue_categorizer', map_location=torch.device('cpu'))['model_state_dict'])
-    model.eval()
-    return tokenizer, model, id_to_class
-
-tokenizer_cls, model_cls, id_to_class = load_prediction_model()
-
-def preprocess_texts(texts):
-    return tokenizer_cls(texts, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
-
-def predict(text):
-    inputs = preprocess_texts(text)
-    with torch.no_grad():
-        outputs = model_cls(**inputs)
-        probabilities = F.softmax(outputs.logits, dim=1)
-        top3_probs, top3_classes = torch.topk(probabilities, k=3, dim=1)
-
-    top3_class_names = [id_to_class[idx.item()] for idx in top3_classes[0]]
-    top3_probs = top3_probs[0] * 100
-    top3_probs_np = top3_probs.cpu().numpy()
-    formatted_percentages = [f"{p:.4f}%" for p in top3_probs_np]
-
-    prediction = top3_class_names[0]
-    probability = top3_probs_np[0]
-
-    return top3_class_names, top3_probs_np, formatted_percentages, prediction, probability
-
-# -------------------- Part 2: Follow-up Generator --------------------
-
-@st.cache_resource
-def load_followup_model():
-    model_path = "Divyanshu04/Insurance_claim_followup_model"  # Adjust path as needed
-    tokenizer = AutoTokenizer.from_pretrained(model_path)
-    model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
-    model.eval()
-    return tokenizer, model
-
-tokenizer_seq, model_seq = load_followup_model()
-
-def generate_followup(context, condition=None, max_tokens=64):
-    prompt = f"Context: {context}"
-    if condition:
-        prompt += f"\nCondition: {condition}"
-    prompt += "\nFollow-up question:"
-    inputs = tokenizer_seq(prompt, return_tensors="pt", padding=True).to(model_seq.device)
-
-    outputs = model_seq.generate(
-        **inputs,
-        max_new_tokens=max_tokens,
-        do_sample=True,
-        top_k=50,
-        top_p=0.95,
-        temperature=0.9,
-        num_return_sequences=1
-    )
-    return tokenizer_seq.decode(outputs[0], skip_special_tokens=True)
-
-# -------------------- Streamlit UI --------------------
-
-st.title("Tenant-Landlord Query Classifier + Claim Assistant")
-
-if "prev_input" not in st.session_state:
-    st.session_state.prev_input = ""
-
-if "context" not in st.session_state:
-    st.session_state.context = ""
-
-user_input = st.text_input("Enter your query:")
-
-if user_input:
-    with st.spinner("Classifying your query..."):
-        combined_input = st.session_state.prev_input + " " + user_input if st.session_state.prev_input else user_input
-        top3_classes, top3_probs, formatted, prediction, probability = predict(combined_input)
-
-        if probability > 60:
-            st.success(f"Prediction: **{prediction}** with confidence **{probability:.2f}%**")
-            st.write("Top 3 predictions:")
-            for cls, prob in zip(top3_classes, formatted):
-                st.write(f"- {cls}: {prob}")
-            st.session_state.context = combined_input
-            st.session_state.prev_input = ""  # Reset
-        else:
-            st.warning("Confidence is low. Please elaborate your query more.")
-            st.session_state.prev_input = combined_input
-
-# -------------------- Ask to Make a Claim --------------------
-
-if st.session_state.context:
-    make_claim = st.radio("Do you want to make a claim?", ["Yes", "No"])
-
-    if make_claim == "No":
-        st.info("Thank you! No claim will be made.")
-        st.stop()
-
-    elif make_claim == "Yes":
-        st.subheader("Claim Assistant - Answer Follow-up Questions")
-
-        if "followup_count" not in st.session_state:
-            st.session_state.followup_count = 0
-        if "questions" not in st.session_state:
-            st.session_state.questions = []
-        if "responses" not in st.session_state:
-            st.session_state.responses = []
-
-        # Generate new follow-up question if needed
-        if len(st.session_state.questions) <= st.session_state.followup_count and st.session_state.followup_count < 5:
-            with st.spinner("Generating follow-up question..."):
-                new_question = generate_followup(st.session_state.context)
-            st.session_state.questions.append(new_question)
-
-        # Render follow-up questions and collect responses
-        for i in range(len(st.session_state.questions)):
-            st.markdown(f"**Follow-up Question {i+1}:** {st.session_state.questions[i]}")
-            response_key = f"response_input_{i}"
-            response = st.text_input(f"Your response to question {i+1}:", key=response_key)
-
-            if response and len(st.session_state.responses) <= i:
-                st.session_state.responses.append(response)
-                st.session_state.context += " " + response
-                st.session_state.followup_count += 1
-
-        if st.session_state.followup_count >= 5:
-            st.success("All follow-up questions answered. Your claim has been registered.")
+
+# @st.cache_resource
+# def load_prediction_model():
+#     prepare_tokenizer_folder()
+#     # tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
+#     tokenizer = AutoTokenizer.from_pretrained('bert-base-uncased')
+#     with open('label_encoder_new.pkl', 'rb') as f:
+#         label_encoder = pickle.load(f)
+#     id_to_class = {idx: class_name for idx, class_name in enumerate(label_encoder.classes_)}
+
+#     # model = BertForSequenceClassification.from_pretrained('Divyanshu04/Issue_categorizer', num_labels=len(label_encoder.classes_))
+#     model = AutoModelForSequenceClassification.from_pretrained('Divyanshu04/Issue_categorizer')
+#     # model.load_state_dict(torch.load('Divyanshu04/Issue_categorizer', map_location=torch.device('cpu'))['model_state_dict'])
+#     model.eval()
+#     return tokenizer, model, id_to_class
+
+# tokenizer_cls, model_cls, id_to_class = load_prediction_model()
+
+# def preprocess_texts(texts):
+#     return tokenizer_cls(texts, padding='max_length', truncation=True, max_length=128, return_tensors='pt')
+
+# def predict(text):
+#     inputs = preprocess_texts(text)
+#     with torch.no_grad():
+#         outputs = model_cls(**inputs)
+#         probabilities = F.softmax(outputs.logits, dim=1)
+#         top3_probs, top3_classes = torch.topk(probabilities, k=3, dim=1)
+
+#     top3_class_names = [id_to_class[idx.item()] for idx in top3_classes[0]]
+#     top3_probs = top3_probs[0] * 100
+#     top3_probs_np = top3_probs.cpu().numpy()
+#     formatted_percentages = [f"{p:.4f}%" for p in top3_probs_np]
+
+#     prediction = top3_class_names[0]
+#     probability = top3_probs_np[0]
+
+#     return top3_class_names, top3_probs_np, formatted_percentages, prediction, probability
+
+# # -------------------- Part 2: Follow-up Generator --------------------
+
+# @st.cache_resource
+# def load_followup_model():
+#     model_path = "Divyanshu04/Insurance_claim_followup_model"  # Adjust path as needed
+#     tokenizer = AutoTokenizer.from_pretrained(model_path)
+#     model = AutoModelForSeq2SeqLM.from_pretrained(model_path)
+#     model.eval()
+#     return tokenizer, model
+
+# tokenizer_seq, model_seq = load_followup_model()
+
+# def generate_followup(context, condition=None, max_tokens=64):
+#     prompt = f"Context: {context}"
+#     if condition:
+#         prompt += f"\nCondition: {condition}"
+#     prompt += "\nFollow-up question:"
+#     inputs = tokenizer_seq(prompt, return_tensors="pt", padding=True).to(model_seq.device)
+
+#     outputs = model_seq.generate(
+#         **inputs,
+#         max_new_tokens=max_tokens,
+#         do_sample=True,
+#         top_k=50,
+#         top_p=0.95,
+#         temperature=0.9,
+#         num_return_sequences=1
+#     )
+#     return tokenizer_seq.decode(outputs[0], skip_special_tokens=True)
+
+# # -------------------- Streamlit UI --------------------
+
+# st.title("Tenant-Landlord Query Classifier + Claim Assistant")
+
+# if "prev_input" not in st.session_state:
+#     st.session_state.prev_input = ""
+
+# if "context" not in st.session_state:
+#     st.session_state.context = ""
+
+# user_input = st.text_input("Enter your query:")
+
+# if user_input:
+#     with st.spinner("Classifying your query..."):
+#         combined_input = st.session_state.prev_input + " " + user_input if st.session_state.prev_input else user_input
+#         top3_classes, top3_probs, formatted, prediction, probability = predict(combined_input)
+
+#         if probability > 60:
+#             st.success(f"Prediction: **{prediction}** with confidence **{probability:.2f}%**")
+#             st.write("Top 3 predictions:")
+#             for cls, prob in zip(top3_classes, formatted):
+#                 st.write(f"- {cls}: {prob}")
+#             st.session_state.context = combined_input
+#             st.session_state.prev_input = ""  # Reset
+#         else:
+#             st.warning("Confidence is low. Please elaborate your query more.")
+#             st.session_state.prev_input = combined_input
+
+# # -------------------- Ask to Make a Claim --------------------
+
+# if st.session_state.context:
+#     make_claim = st.radio("Do you want to make a claim?", ["Yes", "No"])
+
+#     if make_claim == "No":
+#         st.info("Thank you! No claim will be made.")
+#         st.stop()
+
+#     elif make_claim == "Yes":
+#         st.subheader("Claim Assistant - Answer Follow-up Questions")
+
+#         if "followup_count" not in st.session_state:
+#             st.session_state.followup_count = 0
+#         if "questions" not in st.session_state:
+#             st.session_state.questions = []
+#         if "responses" not in st.session_state:
+#             st.session_state.responses = []
+
+#         # Generate new follow-up question if needed
+#         if len(st.session_state.questions) <= st.session_state.followup_count and st.session_state.followup_count < 5:
+#             with st.spinner("Generating follow-up question..."):
+#                 new_question = generate_followup(st.session_state.context)
+#             st.session_state.questions.append(new_question)
+
+#         # Render follow-up questions and collect responses
+#         for i in range(len(st.session_state.questions)):
+#             st.markdown(f"**Follow-up Question {i+1}:** {st.session_state.questions[i]}")
+#             response_key = f"response_input_{i}"
+#             response = st.text_input(f"Your response to question {i+1}:", key=response_key)
+
+#             if response and len(st.session_state.responses) <= i:
+#                 st.session_state.responses.append(response)
+#                 st.session_state.context += " " + response
+#                 st.session_state.followup_count += 1
+
+#         if st.session_state.followup_count >= 5:
+#             st.success("All follow-up questions answered. Your claim has been registered.")