Create Check 2.py

pages/Check 2.py

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+import streamlit as st
+import requests
+import os
+
+# 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()
+
+# Streamlit layout
+st.title("Sentiment Analysis, Summarization, and Keyword Extraction")
+
+text_input = st.text_area("Enter text for analysis")
+
+if st.button("Analyze"):
+    if text_input:
+        # Sentiment Analysis
+        sentiment_prompt = f"Perform sentiment analysis on the following text: {text_input}"
+        sentiment_result = call_huggingface_api(sentiment_prompt)
+        if sentiment_result:
+            st.write("Sentiment Analysis Result:", sentiment_result[0]['generated_text'])
+
+        # Summarization
+        summarization_prompt = f"Summarize the following text: {text_input}"
+        summarization_result = call_huggingface_api(summarization_prompt)
+        if summarization_result:
+            st.write("Summarization Result:", summarization_result[0]['generated_text'])
+
+        # Keyword Extraction (Using LLM, not RAKE)
+        keyword_prompt = f"Extract important keywords from the following text: {text_input}"
+        keyword_result = call_huggingface_api(keyword_prompt)
+        if keyword_result:
+            st.write("Keyword Extraction Result:", keyword_result[0]['generated_text'])
+    else:
+        st.warning("Please enter some text for analysis.")