Update app.py

app.py

@@ -9,6 +9,7 @@ import tempfile
 import io      
 import requests    
 import json  
+import openai
 
 def chunk_text(text, chunk_size=2000):
     chunks = []
@@ -30,16 +31,6 @@ st.title("Patent Claims Extraction")
 # API Key Input
 api_key = st.text_input("Enter your OpenAI API Key:", type="password")
 
-# Camera Input
-image = st.camera_input("Camera input")
-
-with tempfile.NamedTemporaryFile(delete=False, suffix=".jpg") as tf:
-    if image:
-        tf.write(image.read())
-        temp_image_path = tf.name
-    else:
-        temp_image_path = None
-
 # Audio Recording
 audio = st.audio_recorder("Click to record audio", "Click to stop recording")
 
@@ -47,13 +38,20 @@ submit_button = st.button("Use this audio")
 
 if submit_button:
     model = whisper.load_model("base")
-    result = model.transcribe(audio)
-    st.info("Transcribing...")
-    st.success("Transcription complete")
-    transcript = result['text']
+    audio_data = audio.export().read()
+    audio_bytes_io = io.BytesIO(audio_data)
+
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as audio_file:
+        audio_file.write(audio_bytes_io.read())
+        audio_file_path = audio_file.name
+        st.audio(audio_file_path, format="audio/wav")
+        st.info("Transcribing...")
+        st.success("Transcription complete")
+        result = model.transcribe(audio_file_path)
+        transcript = result['text']
 
-    with st.expander("See transcript"):
-        st.markdown(transcript)
+        with st.expander("See transcript"):
+            st.markdown(transcript)
 
 # Model Selection Dropdown
 model_choice = st.selectbox(
@@ -108,47 +106,6 @@ if userinput and api_key and st.button("Extract Claims", key="claims_extraction"
     # Display generated objectives for all chunks
     learning_status_placeholder.text(f"Patentable Claims Extracted!\n{all_extracted_claims.strip()}")
 
-# Claims Extraction
-if st.button("Extract Claims") and api_key and transcript:
-    # You should have 'transcript' available at this point
-    # Ensure 'transcript' is defined before this block.
-
-    # Split the user input into chunks
-    input_chunks = chunk_text(transcript)  # Use 'transcript' instead of 'userinput'
-
-    # Initialize a variable to store the extracted claims
-    all_extracted_claims = ""
-
-    for chunk in input_chunks:
-        # Display status message for the current chunk
-        learning_status_placeholder.text(f"Extracting Patentable Claims for chunk {input_chunks.index(chunk) + 1}...")
-
-        # API call to generate objectives for the current chunk
-        claims_extraction_response = openai.ChatCompletion.create(
-            model=model_choice,
-            messages=[
-                {"role": "user", "content": f"Extract any patentable claims from the following: \n {chunk}. \n Extract each claim. Briefly explain why you extracted this word phrase. Exclude any additional commentary."}
-            ]
-        )
-
-        # Extract the generated objectives from the API response
-        claims_extraction = claims_extraction_response['choices'][0]['message']['content']
-
-        # Append the extracted claims from the current chunk to the overall results
-        all_extracted_claims += claims_extraction.strip()
-
-    # Save the generated objectives to session state
-    st.session_state.claims_extraction = all_extracted_claims
-
-    # Display generated objectives for all chunks
-    learning_status_placeholder.text(f"Patentable Claims Extracted!\n{all_extracted_claims.strip()}")
-
-    # Display status message
-    lesson_plan = st.text("Extracting Patentable Claims...")
-
-    # Extract and display
-    assistant_reply = claims_extraction_response['choices'][0]['message']['content']
-    claims_extraction = st.text(assistant_reply.strip())
 
 # Citation
 st.markdown("<sub>This app was created by [Taylor Ennen](https://github.com/taylor-ennen/GPT-Streamlit-MVP) & [Tonic](https://huggingface.co/tonic)</sub>", unsafe_allow_html=True)