Create Check 3.py

pages/Check 3.py

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+import streamlit as st
+import requests
+import os
+import time
+
+# Define the endpoint and API key
+api_url = "https://api-inference.huggingface.co/models/meta-llama/Meta-Llama-3-8B-Instruct"
+api_key = os.getenv('HFSecret')
+
+headers = {
+    "Authorization": f"Bearer {api_key}"
+}
+
+# API call function
+def call_huggingface_api(prompt):
+    data = {"inputs": prompt, "parameters": {"max_length": 500, "temperature": 0.5}}
+    response = requests.post(api_url, headers=headers, json=data)
+    
+    if response.status_code != 200:
+        st.error(f"Error: {response.status_code} - {response.text}")
+        return None
+    
+    return response.json()
+
+# Function to load text from a URL
+def load_text_from_url(url):
+    response = requests.get(url)
+    return response.text if response.status_code == 200 else ""
+
+# Preset sample text options
+options = ['None', 'Appreciation Letter', 'Regret Letter', 'Kindness Tale', 'Lost Melody Tale', 'Twitter Example 1', 'Twitter Example 2']
+url_dict = {
+    'Appreciation Letter': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Appreciation_Letter.txt",
+    'Regret Letter': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Regret_Letter.txt",
+    'Kindness Tale': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Kindness_Tale.txt",
+    'Lost Melody Tale': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Lost_Melody_Tale.txt",
+    'Twitter Example 1': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_1.txt",
+    'Twitter Example 2': "https://raw.githubusercontent.com/peteciank/public_files/main/Transformers/Twitter_Example_2.txt"
+}
+
+# Streamlit layout
+st.title("Sentiment Analysis, Summarization, and Keyword Extraction")
+
+# Dropdown to select a text file
+selected_option = st.selectbox("Select a preset option", options)
+
+# Initialize text_input
+text_input = ""
+
+# Load text based on dropdown selection
+if selected_option != 'None':
+    with st.spinner("Loading text..."):
+        text_input = load_text_from_url(url_dict[selected_option])
+        time.sleep(1)  # Simulate loading time
+        st.success("Text loaded!")
+else:
+    text_input = st.text_area("Or enter your own text for analysis")
+
+if st.button("Analyze"):
+    if text_input:
+        with st.spinner('Processing...'):
+            # Sentiment Analysis
+            sentiment_prompt = f"Perform sentiment analysis on the following text: {text_input}"
+            sentiment_result = call_huggingface_api(sentiment_prompt)
+            
+            # Summarization
+            summarization_prompt = f"Summarize the following text: {text_input}"
+            summarization_result = call_huggingface_api(summarization_prompt)
+            
+            # Keyword Extraction
+            keyword_prompt = f"Extract important keywords from the following text: {text_input}"
+            keyword_result = call_huggingface_api(keyword_prompt)
+            
+            time.sleep(1)  # Simulate a small delay
+            st.success('Analysis completed!')
+
+        # Display Results in Collapsible Expanders
+        if sentiment_result:
+            with st.expander("Sentiment Analysis (Conclusion)"):
+                st.write("Conclusion: Positive :) or Negative :( ")
+                st.write(sentiment_result[0]['generated_text'])
+        
+        if summarization_result:
+            with st.expander("Summarization"):
+                st.write(summarization_result[0]['generated_text'])
+        
+        if keyword_result:
+            with st.expander("Keyword Extraction"):
+                st.write(keyword_result[0]['generated_text'].split(','))  # Display keywords as list
+    else:
+        st.warning("Please enter some text for analysis.")