Create app.py

app.py

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+import streamlit as st
+import pdfplumber
+from transformers import pipeline
+
+# Load AI Models
+summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+qa_model = pipeline("question-answering", model="deepset/roberta-base-squad2")
+
+# Function to extract text from PDF
+def extract_text_from_pdf(pdf):
+    with pdfplumber.open(pdf) as pdf_file:
+        text = ""
+        for page in pdf_file.pages:
+            text += page.extract_text() + "\n"
+    return text
+
+# Function to summarize text
+def summarize_text(text):
+    summary = summarizer(text, max_length=200, min_length=50, do_sample=False)
+    return summary[0]['summary_text']
+
+# Function for Q&A
+def answer_question(context, question):
+    response = qa_model(question=question, context=context)
+    return response['answer']
+
+# Streamlit UI
+st.set_page_config(page_title="MedGen-AI", page_icon="🩺", layout="wide")
+st.title("🩺 MedGen-AI: Medical Report Simplifier")
+
+uploaded_file = st.file_uploader("📄 Upload a Medical Report (PDF)", type="pdf")
+
+if uploaded_file:
+    text = extract_text_from_pdf(uploaded_file)
+    st.subheader("📜 Extracted Text from Report")
+    st.write(text[:1000] + " ...")  # Show first 1000 characters
+
+    summary = summarize_text(text)
+    st.subheader("📝 Simplified Medical Summary")
+    st.write(summary)
+
+    st.subheader("💬 Ask a Question About Your Report")
+    user_question = st.text_input("🔍 Enter your question:")
+    if user_question:
+        answer = answer_question(text, user_question)
+        st.write("🧑‍⚕️ **AI Answer:**", answer)