Update app.py

app.py

@@ -1,17 +1,16 @@
 import streamlit as st
-import zipfile
-import tempfile
 import requests
 import pdfplumber
 import torch
 from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
-import os
-import warnings
 from reportlab.lib.pagesizes import letter
 from reportlab.platypus import SimpleDocTemplate, Paragraph, Spacer
 from reportlab.lib.styles import getSampleStyleSheet, ParagraphStyle
+from io import BytesIO
+import os
 
 # Suppress warnings
+import warnings
 warnings.filterwarnings("ignore")
 
 # Setup models
@@ -31,24 +30,25 @@ whisper_pipe = pipeline(
     device=device
 )
 
-# IBM Granite API URL and Headers
 granite_url = "https://us-south.ml.cloud.ibm.com/ml/v1/text/generation?version=2023-05-29"
 granite_headers = {
     "Accept": "application/json",
     "Content-Type": "application/json",
-    "Authorization": "Bearer YOUR_API_KEY_HERE"  # Replace with your actual API key
+    "Authorization": "Bearer eyJraWQiOiIyMDI0MDgwMzA4NDEiLCJhbGciOiJSUzI1NiJ9.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.fmiLcZExa22sN_8Xx3_e-VTvZQVvMqmAi_QiA4NKCV40ni8bobxiFEeBKyv8MpafA405jSzFYQUPRFmBy6XNpvVMWpIYKqsZao7l_EDtqXLDRkM_SySUhZtK4CHu-o6qiLyyObBGabke7niaqXuDhzfvpmZCvA98542aeEwSbYZe6siI9_l05xW1T__fIvKak9Y0Fkf7srAmwW7b0NmezQ0VLH13-hANFm0aXh_sEBT0pGujeyRV6X0Bl0zbNW2YurQzdug23BtdS-IR2xbjoAq9KqsSFK2PUMlA_ENg5oKR00sUqCl3gVvVMRNCFbdSkDnaSv2NWDHH-yhE2LwgTw"  # Replace with your actual API key
+
+    
 }
 
 # Function to transcribe audio files
-def transcribe_audio(file_path):
-    result = whisper_pipe(file_path)
+def transcribe_audio(file):
+    result = whisper_pipe(file)
     return result['text']
 
 # Function to extract text and questions from PDF
-def extract_text_from_pdf(pdf_path):
+def extract_text_from_pdf(pdf_file):
     text = ""
     questions = []
-    with pdfplumber.open(pdf_path) as pdf:
+    with pdfplumber.open(pdf_file) as pdf:
         for page in pdf.pages:
             page_text = page.extract_text()
             if page_text:
@@ -70,7 +70,7 @@ def generate_form_data(text, questions):
             "repetition_penalty": 1.05
         },
         "model_id": "ibm/granite-13b-chat-v2",
-        "project_id": "YOUR_PROJECT_ID",  # Replace with your actual project ID
+        "project_id": "698f0da7-6b34-4642-8540-978e70e85c8e",  # Replace with your actual project ID
         "moderations": {
             "hap": {
                 "input": {
@@ -93,8 +93,9 @@ def generate_form_data(text, questions):
     return data['results'][0]['generated_text'].strip()
 
 # Function to save responses to PDF
-def save_responses_to_pdf(responses, output_pdf_path):
-    document = SimpleDocTemplate(output_pdf_path, pagesize=letter)
+def save_responses_to_pdf(responses):
+    buffer = BytesIO()
+    document = SimpleDocTemplate(buffer, pagesize=letter)
     styles = getSampleStyleSheet()
     
     # Custom style for numbered responses
@@ -118,40 +119,35 @@ def save_responses_to_pdf(responses, output_pdf_path):
         content.append(Spacer(1, 18))  # Space between responses
     
     document.build(content)
+    buffer.seek(0)
+    return buffer
 
-# Streamlit Interface
-st.title("Audio to Form Filling")
-
-zip_file = st.file_uploader("Upload ZIP File with Audio Files", type="zip")
-pdf_file = st.file_uploader("Upload PDF Form", type="pdf")
+# Streamlit app
+st.title("FILL IT: By Umar Majeed")
 
-if zip_file and pdf_file:
-    with tempfile.TemporaryDirectory() as tmp_dir:
-        with zipfile.ZipFile(zip_file, 'r') as zip_ref:
-            zip_ref.extractall(tmp_dir)
+uploaded_audio_files = st.file_uploader("Upload audio files", type=["wav", "mp3"], accept_multiple_files=True)
+uploaded_pdf = st.file_uploader("Upload PDF form", type=["pdf"])
 
-        responses = []
-        for filename in os.listdir(tmp_dir):
-            if filename.endswith((".wav", ".mp3")):
-                file_path = os.path.join(tmp_dir, filename)
-                # Transcribe audio
-                transcribed_text = transcribe_audio(file_path)
-                # Extract text and form fields from PDF
-                pdf_text, pdf_questions = extract_text_from_pdf(pdf_file)
-                # Generate form data
-                form_data = generate_form_data(transcribed_text, pdf_questions)
-                responses.append(form_data)
-                st.write(f"File {len(responses)}:\n{form_data}\n")  # Display the extracted form data with numbering
+if uploaded_audio_files and uploaded_pdf:
+    responses = []
 
-        # Save all responses to a PDF
-        output_pdf_path = tempfile.NamedTemporaryFile(delete=False, suffix=".pdf").name
-        save_responses_to_pdf(responses, output_pdf_path)
-
-        # Provide a download button for the generated PDF
-        with open(output_pdf_path, "rb") as f:
-            st.download_button(
-                label="Download Processed PDF",
-                data=f,
-                file_name="processed_output.pdf",
-                mime="application/pdf"
-            )
+    for audio_file in uploaded_audio_files:
+        # Transcribe audio
+        transcribed_text = transcribe_audio(audio_file)
+        # Extract text and form fields from PDF
+        pdf_text, pdf_questions = extract_text_from_pdf(uploaded_pdf)
+        # Generate form data
+        form_data = generate_form_data(transcribed_text, pdf_questions)
+        responses.append(form_data)
+        st.write(f"Extracted form data for {audio_file.name}:")
+        st.write(form_data)
+    
+    if responses:
+        # Save responses to PDF
+        response_pdf_buffer = save_responses_to_pdf(responses)
+        st.download_button(
+            label="Download Response PDF",
+            data=response_pdf_buffer,
+            file_name="response_output.pdf",
+            mime="application/pdf"
+        )