Update app.py

app.py

@@ -1,21 +1,77 @@
+import json
+import re
+import textwrap
+import openai
 import gradio as gr
-from gpt_index import GPTSimpleVectorIndex
-from gpt_index.langchain_helpers.chain_wrapper import OpenAI
-import os
-
-os.environ["OPENAI_API_KEY"] = "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"
-index = GPTSimpleVectorIndex.load_from_disk('./index (4).json')
-
-def my_model_function(input_text):
-    response = index.query(input_text, response_mode="compact")
-    return response
-
-iface = gr.Interface(
-    fn=my_model_function, 
-    inputs=gr.inputs.Textbox(label="Enter your question here"),
-    outputs="text",
-    title="GPT Index Demo",
-    description="Type a question and see the matching responses from the index!"
-)
-
-iface.launch()
+
+def gpt3_completion(prompt, engine='text-davinci-002', temp=0.6, top_p=1.0, tokens=1000, freq_pen=0.25, pres_pen=0.0, stop=['<<END>>']):
+    print("here");
+    max_retry = 5
+    retry = 0
+   
+    response = openai.Completion.create(
+        engine=engine,
+        prompt=prompt,
+        temperature=0.7,
+        max_tokens=963,
+        top_p=1,
+        frequency_penalty=0,
+        presence_penalty=0)
+    text = response['choices'][0]['text'].strip()
+    text = re.sub('\s+', ' ', text)
+    return text
+
+def summarize_podcast(podcast):
+    # Open the transcript file
+    vtt = podcast
+    transcript = vtt.read().decode()
+    res = len(transcript.split())
+    
+    transcript = transcript.replace("WEBVTT", "")
+    transcript = "\n".join(filter(None, transcript.splitlines()))
+    transcript = re.sub(r"\d\d:\d\d:\d\d\.\d\d\d \d\d:\d\d:\d\d\.\d\d\d", "", transcript)
+    text = transcript
+
+    lines = text.split('\n')
+    names = []
+    unique_list=[]
+    for line in lines:
+        if ':' in line:
+            name = line.split(':')[0]
+            names.append(name)
+    for x in range(0,len(names)):
+        if names[x] not in unique_list:
+            unique_list.append(names[x])
+    sp=""
+    al=["A","B","C","D","E","F","G","H","I","K","L","M","N","O","P","Q","R","S","T","V","X","Y","Z"]
+    for x in range(0,len(unique_list)):
+        transcript = transcript.replace(unique_list[x], al[x])
+        sp=sp+"Speaker "+str(x+1)+" "+unique_list[x]+"\n"
+    transcript=sp+""+transcript
+    openai.api_key = "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"
+    chunks = textwrap.wrap(transcript, 8000)
+    result = list()
+    count = 0
+    tempstr=""
+    for chunk in chunks:
+        count = count + 1
+        prompt = sp+"""\n
+        Summarize the portion of the podcast. The summary should be around 200 words. use the Name instead of A, B, C...
+        Podcast: 
+
+        """+str(chunk)+"""'\n\n"""
+        summary = gpt3_completion(prompt)
+        summary="chunk : "+str(count)+summary
+        result.append(summary)
+        tempstr=tempstr+"\n"+summary
+    return str(tempstr)
+
+input_file = gr.inputs.File(label="Upload your podcast in VTT format.")
+output_text = gr.outputs.Textbox(label="Podcast Summary")
+
+sample_url = "https://www.example.com/sample.vtt"
+
+iface = gr.Interface(fn=summarize_podcast, inputs=input_file, outputs=output_text, title="Podcast Summarizer", 
+                     description="This tool summarizes a podcast in VTT format, providing a summary of each chunk of text.")
+
+iface.launch()