lets check

requirements.txt

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+anyio==3.6.2
+click==8.1.3
+colorama==0.4.6
+fastapi==0.95.1
+h11==0.14.0
+idna==3.4
+pydantic==1.10.7
+python-dotenv==1.0.0
+sniffio==1.3.0
+starlette==0.26.1
+textrazor==1.4.0
+typing_extensions==4.5.0
+uvicorn==0.22.0
+replicate

stream_lit.py

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+import streamlit as st
+from collections import Counter
+import textrazor
+from dotenv import load_dotenv
+import os
+import pandas as pd
+import replicate
+import plotly.express as px
+
+load_dotenv()
+REPLICATE_API_TOKEN = os.getenv("REPLICATE_API_TOKEN")
+
+text_ = """Alice: Hi there, is this Bob?
+# Bob: Yes, speaking. Who's calling?
+# Alice: Hey Bob, it's Alice from Acme Inc. We met at the conference last month.
+# Bob: Oh, hey Alice! Good to hear from you. How can I help you today?
+# Alice: Well, I was just calling because I wanted to touch base with you about that project we discussed at the conference. I was hoping we could set up a meeting to talk about it in more detail.
+# Bob: Absolutely, I'd be happy to. When were you thinking?
+# Alice: How about next Tuesday at 10 am?
+# Bob: That works for me. Where should we meet?
+# Alice: We can meet at our office in downtown. Here's the address: 123 Main St. Suite 400.
+# Bob: Great. And just to confirm, your mobile number is still (555) 123-4567, right?
+# Alice: Yes, that's correct.
+# Bob: Perfect. I'll put the meeting in my calendar and send you a confirmation email with all the details.
+# Alice: Sounds good, thanks Bob. Looking forward to it!"""
+
+replicate.Client(api_token=REPLICATE_API_TOKEN)
+
+def make_summary(text_):
+    output = replicate.run(
+        "replicate/flan-t5-xl:7a216605843d87f5426a10d2cc6940485a232336ed04d655ef86b91e020e9210",
+        input={"prompt": """Write the summary extract any useful information like name, number, and organization of the following conversation {text_to_summarize} """.format(text_to_summarize = text_)},
+        max_length = 500,
+        temperature = 0.7,
+        top_p = 0.95,
+        repetition_penalty = 1,
+    )
+    return " ".join(output)
+
+load_dotenv()
+textrazor.api_key = os.getenv("TEXT_RAZOR_API_KEY")
+
+client = textrazor.TextRazor(extractors=["entities", "topics"])
+
+def make_output(text:str):
+    response = client.analyze(text=text)
+    df = pd.DataFrame()
+    for entity in response.entities():
+        output_dict = {"id":  "".join(list(entity.id or "None")), 
+                       "type": ", ".join(list(entity.dbpedia_types or "None")), 
+                       "wiki link": "".join(list(entity.wikipedia_link or "None"))}
+        df2 = pd.DataFrame.from_records([output_dict])
+        # df = pd.concat([df, pd.DataFrame([new_row])], ignore_index=True)
+        df = pd.concat([df, df2], ignore_index=True)
+        # print(entity.id)
+        df1 = df.drop_duplicates()
+
+    return(df)
+
+st.title("CultureScout NLP Tool 🤖")
+
+# taking user inputs for context search
+st.write("***Enter Text You Need Help With:***")
+user_input = st.text_input("Text Here:", "")
+
+if st.button("🔎 Search It!"):
+    def predict_sentiment(data:str):
+        ans = make_output(user_input)
+        return ans
+    df = predict_sentiment(user_input)
+    df = df.drop_duplicates()
+    st.table(df)
+
+    id_df = predict_sentiment(user_input)['id']
+    id_freq = id_df.value_counts()
+    most_common_id = id_df.value_counts().index[0]
+
+    type_df = predict_sentiment(user_input)['type']
+    type_freq = type_df.value_counts()
+    most_common_type = type_df.value_counts().index[0]
+
+
+    st.bar_chart(id_freq)
+    st.bar_chart(type_freq)
+
+    st.write(f"Most appeared id is {most_common_id}.")
+    st.write(f"Most appeared type is {most_common_type}.")
+    st.write("""
+             """)
+
+# taking user inputs for summarization
+st.write("***Enter Text You Need to Summarize:***")
+user_input1 = st.text_area("Text Here:", "")
+
+if st.button("🪄 Summarize"):
+    def summarize(data:str):
+        ans1 = make_summary(user_input1)
+        return ans1
+    st.write(summarize(user_input1))