Update app.py

app.py

@@ -1,5 +1,14 @@
-import streamlit as st
-import openai
+import streamlit as st  
+import gradio as gr  
+import numpy as np      
+from audiorecorder import audiorecorder      
+import whisper      
+import os      
+import streamlit.components.v1 as components   
+import tempfile      
+import io      
+import requests    
+import json  
 
 def chunk_text(text, chunk_size=2000):
     chunks = []
@@ -21,6 +30,31 @@ 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")
+
+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']
+
+    with st.expander("See transcript"):
+        st.markdown(transcript)
+
 # Model Selection Dropdown
 model_choice = st.selectbox(
     "Select the model you want to use:",
@@ -28,7 +62,7 @@ model_choice = st.selectbox(
 )
 
 # Context, Subject, and Level
-context = "You are a patent claims identifier and extractor. You will freeform text, identify any claims contained therein that may be patentable. You identify , extract, print such claims, briefly explain why each claim is patentable."
+context = "You are a patent claims identifier and extractor. You will freeform text, identify any claims contained therein that may be patentable. You identify, extract, print such claims, briefly explain why each claim is patentable."
 userinput = st.text_input("Input Text:", "Freeform text here!")
 
 # Initialize OpenAI API
@@ -42,10 +76,11 @@ claims_extraction = ""
 # Initialize status placeholder
 learning_status_placeholder = st.empty()
 disable_button_bool = False
+
 if userinput and api_key and st.button("Extract Claims", key="claims_extraction", disabled=disable_button_bool):
     # Split the user input into chunks
     input_chunks = chunk_text(userinput)
-    
+
     # Initialize a variable to store the extracted claims
     all_extracted_claims = ""
 
@@ -57,7 +92,7 @@ if userinput and api_key and st.button("Extract Claims", key="claims_extraction"
         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."}
+                {"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."}
             ]
         )
 
@@ -73,46 +108,47 @@ 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()}")
 
-# Generate Lesson Plan Button
-if st.button("Extract Claims") and api_key:
-    
-    # Construct the prompt as a dictionary
-    prompt_dict = {
-        "context": context,
-        "userinput": userinput,
-        "claims_extraction": claims_extraction.strip(),  # Use the claims_extraction variable
-        "tasks": [
-            {"task": "Extract Claims", "objective": "extract any wordphrases in the text provided that could be considered a patentable claim"},
-            {"task": "Extract Every Claim", "objective": "Ensure each and every wordphrase with a claim is evaluated whether or not it is patentable"},
-            {"task": "Explain Your Choice", "objective": "Briefly explain why you have retained each claim"},
-            {"task": "Check Your Work", "objective": "Check your work to assure you have not forgotten any claims"}
-        ],
-        "output_format": """Present in a structured format.
-        \nClaim: 
-        \nExplanation:
-        \nNotes:
-        
-        """
-    }
-
-    
-    # Convert the dictionary to a string
-    prompt_str = str(prompt_dict)
-
-    # API call to generate the lesson plan
-    lesson_plan_response = openai.ChatCompletion.create(
-        model=model_choice,
-        messages=[
-            {"role": "user", "content": f"Create a lesson plan based on the following parameters: {prompt_str}"}
-        ]
-    )
+# 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...")
+    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())
+    claims_extraction = st.text(assistant_reply.strip())
 
-# Citation 
+# 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)