create app

app.py

@@ -0,0 +1,62 @@
+import streamlit as st
+from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
+import requests
+import pandas as pd
+
+url = 'https://elharchaouiresume.s3.eu-west-3.amazonaws.com/resume'
+response = requests.get(url)
+resume_context=None
+
+if response.status_code == 200:
+    resume_context = response.text
+else:
+    load_error=f"Conext loading Error: {response.status_code}"
+
+# with open('resume') as f:
+#     resume_context = f.read()
+
+model_name = "deepset/tinyroberta-squad2"
+
+# a) Get predictions
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+
+
+# Streamlit app layout
+st.title("Ask me any question about my career")
+st.write("This app uses a pre-trained model from Hugging Face to perform question answering, about my career as context, on user input.")
+
+# User input
+user_input = st.text_area("Enter some a question :", value="", height=150, max_chars=500)
+
+if user_input:
+
+    if resume_context:
+        print(resume_context)
+        QA_input = {
+            'question': user_input,
+            'context': resume_context
+        }
+        # Perform sentiment analysis on the user input
+        result = nlp(QA_input)
+
+        # Display the sentiment analysis result
+        answer = result["answer"]
+    else:
+        answer="It seems there is a problem loading context, here is more details :"+load_error
+
+    st.write(f"Response: {answer}")
+else:
+    st.write("Please enter some text to analyze.")
+
+# Display a table with example inputs
+st.write("Example inputs:")
+
+example_inputs = [
+    {"Inputs example": "What programming languages you have experience in?"},
+    {"Inputs example": "What cloud platforms are you familiar with?"},
+    {"Inputs example": "What are your skills in machine learning?"},
+    {"Inputs example": "The future of technology is..."},
+]
+
+example_df = pd.DataFrame(example_inputs)
+st.table(example_df)