Create app.py

app.py

@@ -0,0 +1,117 @@
+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer
+
+def main():
+    # Custom CSS for styling
+    custom_css = """
+        <style>
+            body {
+                font-family: 'Arial', sans-serif;
+                background-color: #FF0000; /* Set your desired background color here */
+            }
+            .title {
+                font-size: 2.5em;
+                color: #333;
+                text-align: center;
+                padding: 1em;
+                background-color: #3498db;
+                color: #fff;
+                border-radius: 10px;
+                margin-bottom: 1em;
+            }
+            .input-container {
+                margin: 2em;
+            }
+            .button-container {
+                text-align: center;
+            }
+            .result-container {
+                margin: 2em;
+                padding: 1em;
+                background-color: #fff;
+                border-radius: 10px;
+                box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            }
+            .answer {
+                background-color: #3498db;
+                padding: 10px;
+                border-radius: 5px;
+                margin-top: 10px;
+            }
+            img {
+                max-width: 100%;
+                height: auto;
+                border-radius: 10px;
+                box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1);
+            }
+        </style>
+    """
+    st.markdown(custom_css, unsafe_allow_html=True)
+
+    # Title
+    st.markdown("<div class='title'>Question Answering with Transformers</div>", unsafe_allow_html=True)
+
+    # Model Selection Dropdown
+    model_name = st.selectbox("Select Model", ["deepset/roberta-base-squad2", "bert-large-uncased-whole-word-masking-finetuned-squad", "distilbert-base-cased-distilled-squad",
+    "bert-base-uncased",
+    "albert-base-v2"])
+    model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+    # Get user input
+    st.markdown("<div class='input-container'>", unsafe_allow_html=True)
+    context = st.text_area("Enter the context (max 400 words):")
+    question = st.text_input("Enter your question:")
+    st.markdown("</div>", unsafe_allow_html=True)
+
+    if st.button("Get Answer"):
+        #st.markdown("<div class='result-container'>", unsafe_allow_html=True)
+        if not question or not context:
+            st.warning("Please enter both a question and a context.")
+        else:
+            # Tokenize input
+            try:
+                # Check word count in the context
+                if len(context.split()) > 400:
+                    raise ValueError("Context exceeds 400 words limit.")
+
+                inputs = tokenizer.encode_plus(question, context, return_tensors='pt')
+
+                # Get predictions
+                outputs = model(**inputs)
+                start_logits = outputs.start_logits
+                end_logits = outputs.end_logits
+
+                # Get top N answer spans
+                top_n = 3
+                start_indexes = start_logits.argsort(dim=1, descending=True)[:, :top_n]
+                end_indexes = end_logits.argsort(dim=1, descending=True)[:, :top_n]
+
+                # Display detailed answers
+                st.subheader(f"Question: {question}")
+                for i in range(top_n):
+                    start_index = start_indexes[0, i].item()
+                    end_index = end_indexes[0, i].item()
+                    answer = tokenizer.decode(inputs['input_ids'][0, start_index:end_index + 1])
+
+                    # Highlight answer in context
+                    highlighted_context = f"{context[:start_index]}**{context[start_index:end_index+1]}**{context[end_index+1:]}"
+
+                    # Display confidence scores
+                    confidence_start = start_logits[0, start_index].item()
+                    confidence_end = end_logits[0, end_index].item()
+                    if answer == "":
+                        continue
+                    else:
+                      st.markdown(f"<div class='answer'><strong>Answer:</strong> {answer}<br>"
+                                f"<strong>Confidence (Start):</strong> {confidence_start:.4f}<br>"
+                                f"<strong>Confidence (End):</strong> {confidence_end:.4f}</div>", unsafe_allow_html=True)
+
+            except ValueError as ve:
+                st.error(str(ve))
+            except Exception as e:
+                st.error(f"An error occurred: {e}")
+        st.markdown("</div>", unsafe_allow_html=True)
+
+if __name__ == "__main__":
+    main()