Update app.py

app.py

@@ -1,153 +1,153 @@
-
-import streamlit as st
-import google.generativeai as genai
-import markdown
-from docx import Document
-from bs4 import BeautifulSoup
-from PyPDF2 import PdfFileReader
-import tempfile
-import os
-
-# Configure the API key
-genai.configure(api_key=os.getenv('gemini_api'))
-
-# Function to convert PDF to text
-def pdf_to_text(file):
-    with open(file, 'rb') as f:
-        pdf = PdfFileReader(f)
-        text = ""
-        for page_num in range(pdf.numPages):
-            page = pdf.getPage(page_num)
-            text += page.extract_text()
-    return text
-
-# Function to upload file to the Generative AI API
-def upload_file(file_path):
-    st.write("Uploading file...")
-    text_file = genai.upload_file(path=file_path)
-    st.write(f"Completed upload: {text_file.uri}")
-    return text_file
-
-# Function to convert text to Markdown
-def to_markdown(text):
-    text = text.replace('•', '  *')
-    return textwrap.indent(text, '> ', predicate=lambda _: True)
-
-chat_session = None
-
-# Function to build the model
-def build_model(text_file):
-    global chat_session
-    generation_config = {
-        "temperature": 0.2,
-        "top_p": 0.95,
-        "top_k": 64,
-        "max_output_tokens": 8192,
-        "response_mime_type": "text/plain",
-    }
-
-    model = genai.GenerativeModel(
-        model_name="gemini-1.5-flash",
-        generation_config=generation_config,
-        system_instruction="""Yüklenen belgedeki bilgilere göre Türkçe cevap ver.
-        Eğer sorunun cevabı belgede bulunmuyorsa 'Belgede Cevap Bulunmuyor' yaz.
-        """,
-    )
-
-    chat_session = model.start_chat(history=[])
-    response = chat_session.send_message(["Yüklenen belgeyi bir cümle ile özetle", text_file])
-
-    st.markdown(to_markdown(response.text))
-
-# Function to interact with the chat model
-def chat(prompt):
-    try:
-        response = chat_session.send_message(prompt)
-        markdown_text = to_markdown(response.text)
-        st.markdown(markdown_text)
-        return response.text
-    except ValueError:
-        st.write(response.prompt_feedback)
-        st.write(response.candidates[0].finish_reason)
-        st.write(response.candidates[0].safety_ratings)
-    except Exception as e:
-        st.write("An unexpected error occurred:", e)
-
-# Function to generate a report based on questions
-def generate_report(questions):
-    report_text = "\n## SORULARINIZ VE CEVAPLARI\n"
-    for question in questions:
-        report_text += f"\n## {question}\n"
-        answer = chat(question)
-        report_text += f"\n{answer}\n"
-    return report_text
-
-# Function to convert Markdown to HTML
-def convert_Markdown_to_HTML(report_text):
-    html_text = markdown.markdown(report_text)
-    return html_text
-
-# Function to add HTML to a Word document
-def add_html_to_word(html_text, doc):
-    soup = BeautifulSoup(html_text, 'html.parser')
-    for element in soup:
-        if element.name == 'h1':
-            doc.add_heading(element.get_text(), level=1)
-        elif element.name == 'h2':
-            doc.add_heading(element.get_text(), level=2)
-        elif element.name == 'h3':
-            doc.add_heading(element.get_text(), level=3)
-        elif element.name == 'h4':
-            doc.add_heading(element.get_text(), level=4)
-        elif element.name == 'h5':
-            doc.add_heading(element.get_text(), level=5)
-        elif element.name == 'h6':
-            doc.add_heading(element.get_text(), level=6)
-        elif element.name == 'p':
-            doc.add_paragraph(element.get_text())
-        elif element.name == 'ul':
-            for li in element.find_all('li'):
-                doc.add_paragraph(li.get_text(), style='List Bullet')
-        elif element.name == 'ol':
-            for li in element.find_all('li'):
-                doc.add_paragraph(li.get_text(), style='List Number')
-        elif element.name:
-            doc.add_paragraph(element.get_text())
-
-# Streamlit interface
-st.title("REPORT GENERATOR: ASK YOUR QUESTIONS TO A PDF FILE BY MURAT KARAKAYA AKADEMI")
-st.write("Upload a PDF to ask questions and get the answers.")
-
-uploaded_file = st.file_uploader("Upload PDF", type="pdf")
-questions_input = st.text_area("Enter Questions", placeholder="Type your questions here, one per line.", height=150)
-
-if uploaded_file and questions_input:
-    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
-        temp_file.write(uploaded_file.read())
-        temp_file_path = temp_file.name
-
-    text_content = pdf_to_text(temp_file_path)
-    text_file = upload_file(temp_file_path)
-    build_model(text_file)
-
-    questions = questions_input.split("\n")
-    report_text = generate_report(questions)
-
-    html_text = convert_Markdown_to_HTML(report_text)
-    doc = Document()
-    add_html_to_word(html_text, doc)
-    
-    doc_name = os.path.basename(temp_file_path).replace(".pdf", ".docx")
-    doc_name = "Rapor " + doc_name
-    doc.save(doc_name)
-    
-    st.markdown(report_text)
-    st.write("Document generated successfully!")
-    
-    with open(doc_name, "rb") as file:
-        st.download_button(label="Download Report", data=file, file_name=doc_name)
-    
-    os.remove(temp_file_path)
-    os.remove(doc_name)
-
+
+import streamlit as st
+import google.generativeai as genai
+import markdown
+from docx import Document
+from bs4 import BeautifulSoup
+from PyPDF2 import PdfFileReader
+import tempfile
+import os
+
+# Configure the API key
+genai.configure(api_key=os.getenv('gemini_api'))
+
+# Function to convert PDF to text
+def pdf_to_text(file):
+    with open(file, 'rb') as f:
+        pdf = PdfFileReader(f)
+        text = ""
+        for page_num in range(pdf.numPages):
+            page = pdf.getPage(page_num)
+            text += page.extract_text()
+    return text
+
+# Function to upload file to the Generative AI API
+def upload_file(file_path):
+    st.write("Uploading file...")
+    text_file = genai.upload_file(path=file_path)
+    st.write(f"Completed upload: {text_file.uri}")
+    return text_file
+
+# Function to convert text to Markdown
+def to_markdown(text):
+    text = text.replace('•', '  *')
+    return textwrap.indent(text, '> ', predicate=lambda _: True)
+
+chat_session = None
+
+# Function to build the model
+def build_model(text_file):
+    global chat_session
+    generation_config = {
+        "temperature": 0.2,
+        "top_p": 0.95,
+        "top_k": 64,
+        "max_output_tokens": 8192,
+        "response_mime_type": "text/plain",
+    }
+
+    model = genai.GenerativeModel(
+        model_name="gemini-1.5-flash",
+        generation_config=generation_config,
+        system_instruction="""Yüklenen belgedeki bilgilere göre Türkçe cevap ver.
+        Eğer sorunun cevabı belgede bulunmuyorsa 'Belgede Cevap Bulunmuyor' yaz.
+        """,
+    )
+
+    chat_session = model.start_chat(history=[])
+    response = chat_session.send_message(["Yüklenen belgeyi bir cümle ile özetle", text_file])
+
+    st.markdown(to_markdown(response.text))
+
+# Function to interact with the chat model
+def chat(prompt):
+    try:
+        response = chat_session.send_message(prompt)
+        markdown_text = to_markdown(response.text)
+        st.markdown(markdown_text)
+        return response.text
+    except ValueError:
+        st.write(response.prompt_feedback)
+        st.write(response.candidates[0].finish_reason)
+        st.write(response.candidates[0].safety_ratings)
+    except Exception as e:
+        st.write("An unexpected error occurred:", e)
+
+# Function to generate a report based on questions
+def generate_report(questions):
+    report_text = "\n## SORULARINIZ VE CEVAPLARI\n"
+    for question in questions:
+        report_text += f"\n## {question}\n"
+        answer = chat(question)
+        report_text += f"\n{answer}\n"
+    return report_text
+
+# Function to convert Markdown to HTML
+def convert_Markdown_to_HTML(report_text):
+    html_text = markdown.markdown(report_text)
+    return html_text
+
+# Function to add HTML to a Word document
+def add_html_to_word(html_text, doc):
+    soup = BeautifulSoup(html_text, 'html.parser')
+    for element in soup:
+        if element.name == 'h1':
+            doc.add_heading(element.get_text(), level=1)
+        elif element.name == 'h2':
+            doc.add_heading(element.get_text(), level=2)
+        elif element.name == 'h3':
+            doc.add_heading(element.get_text(), level=3)
+        elif element.name == 'h4':
+            doc.add_heading(element.get_text(), level=4)
+        elif element.name == 'h5':
+            doc.add_heading(element.get_text(), level=5)
+        elif element.name == 'h6':
+            doc.add_heading(element.get_text(), level=6)
+        elif element.name == 'p':
+            doc.add_paragraph(element.get_text())
+        elif element.name == 'ul':
+            for li in element.find_all('li'):
+                doc.add_paragraph(li.get_text(), style='List Bullet')
+        elif element.name == 'ol':
+            for li in element.find_all('li'):
+                doc.add_paragraph(li.get_text(), style='List Number')
+        elif element.name:
+            doc.add_paragraph(element.get_text())
+
+# Streamlit interface
+st.title("REPORT GENERATOR: ASK YOUR QUESTIONS TO A PDF FILE by @drmurataltun")
+st.write("Upload a PDF to ask questions and get the answers.")
+
+uploaded_file = st.file_uploader("Upload PDF", type="pdf")
+questions_input = st.text_area("Enter Questions", placeholder="Type your questions here, one per line.", height=150)
+
+if uploaded_file and questions_input:
+    with tempfile.NamedTemporaryFile(delete=False) as temp_file:
+        temp_file.write(uploaded_file.read())
+        temp_file_path = temp_file.name
+
+    text_content = pdf_to_text(temp_file_path)
+    text_file = upload_file(temp_file_path)
+    build_model(text_file)
+
+    questions = questions_input.split("\n")
+    report_text = generate_report(questions)
+
+    html_text = convert_Markdown_to_HTML(report_text)
+    doc = Document()
+    add_html_to_word(html_text, doc)
+    
+    doc_name = os.path.basename(temp_file_path).replace(".pdf", ".docx")
+    doc_name = "Rapor " + doc_name
+    doc.save(doc_name)
+    
+    st.markdown(report_text)
+    st.write("Document generated successfully!")
+    
+    with open(doc_name, "rb") as file:
+        st.download_button(label="Download Report", data=file, file_name=doc_name)
+    
+    os.remove(temp_file_path)
+    os.remove(doc_name)
+
     genai.delete_file(text_file.name)