Upload app.py

app.py

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+import os
+import streamlit as st
+from typing import List, Dict, Any
+from serpapi import GoogleSearch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+
+def search_serpapi(query: str, loc: str, api_key: str) -> List[Dict[str, Any]]:
+    """
+    Search using SerpAPI for the given query and return the results.
+    """
+    try:
+        search = GoogleSearch({
+            "q": query,
+            "location": loc,
+            "api_key": api_key
+        })
+        results = search.get_dict()
+        return results.get("organic_results", [])
+    except Exception as e:
+        raise Exception(f"An error occurred: {e}")
+
+def convert_to_md_table(data):
+    md_table = "| Title | Link |\n| :--- | :--- |\n"
+    for item in data:
+        title = item['title']
+        link = item['link']
+        md_table += f"| {title} | [Link]({link}) |\n"
+    return md_table
+
+# Load model directly
+tokenizer = AutoTokenizer.from_pretrained("jy46604790/Fake-News-Bert-Detect")
+model = AutoModelForSequenceClassification.from_pretrained("jy46604790/Fake-News-Bert-Detect")
+
+def call_classifier(text: str):
+    inputs = tokenizer(text, return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+    logits = outputs.logits
+    probabilities = torch.nn.functional.softmax(logits, dim=-1)
+    label = torch.argmax(probabilities, dim=1).item()
+    score = probabilities[0][label].item()
+    return {"label": label, "score": score}
+
+# Initialize session state
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
+if 'user_input' not in st.session_state:
+    st.session_state.user_input = ""
+
+if 'score' not in st.session_state:
+    st.session_state.score = {'label': 'LABEL_0', 'score': 0.0}
+
+# Streamlit app layout
+st.title("Chatbot News Search")
+
+# User input
+st.session_state.user_input = st.text_input("What news do you want to search?", st.session_state.user_input)
+
+# Threshold
+threshold = 0.7
+
+# Main logic
+if st.session_state.user_input:
+    st.session_state.history.append(f"User: {st.session_state.user_input}")
+    
+    if st.session_state.score['score'] > threshold:
+        query = st.session_state.user_input
+        SERPAPI_API_KEY = os.getenv("SERPAPI_API_KEY")  # Use environment variable for SerpAPI key
+        if not SERPAPI_API_KEY:
+            st.error("SerpAPI API key not found. Please set the SERPAPI_API_KEY environment variable.")
+        else:
+            news_results = search_serpapi(query, "New York", SERPAPI_API_KEY)
+            formatted_news = convert_to_md_table(news_results)
+            st.session_state.history.append(f"Chatbot: Here are the latest news results:\n{formatted_news}")
+
+            user_continue = st.radio("Are you okay with this?", ('Y', 'E'))
+            if user_continue == 'E':
+                st.session_state.history.append("User exited the conversation.")
+            else:
+                new_score = call_classifier(query)
+                while new_score['score'] < st.session_state.score['score']:
+                    st.session_state.history.append("Run the SerpAPI again.")
+                    new_score = call_classifier(query)['score']
+                    st.session_state.history.append(f"New score: {new_score}")
+
+                    user_continue = st.radio("Are you okay with this?", ('Y', 'E'))
+                    if user_continue == 'E':
+                        st.session_state.history.append("User exited the conversation.")
+                        break
+
+                st.session_state.score = new_score
+
+    else:
+        st.session_state.history.append(f"Chatbot: Current score: {st.session_state.score['score']}")
+        st.session_state.user_input = st.text_input("Please provide more information to refine the news search:", st.session_state.user_input)
+        st.session_state.score = call_classifier(st.session_state.user_input)
+        st.session_state.history.append(f"New score: {st.session_state.score['score']}")
+
+# Display chat history
+for message in st.session_state.history:
+    st.write(message)
+
+st.write("If you want to finish the conversation, please enter 'exit'.")