Create app.py

app.py

@@ -0,0 +1,61 @@
+import streamlit as st
+import requests
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the Llama 3.2 model and tokenizer
+model_name = "your-llama-3-2-model"  # Replace with the correct model path in Hugging Face Hub
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+def search_api_call(query: str):
+    api_key = '614e992d87c496fb15a81a2039e00e6a42530f5c'  # Replace with your actual API key
+    url = f"https://api.serper.dev/search?api_key={api_key}&q={query}"
+    
+    try:
+        response = requests.get(url)
+        data = response.json()
+        return data['organic_results']  # Adjust according to the API response structure
+    except Exception as e:
+        st.error("Error fetching from the API: " + str(e))
+        return None
+
+def is_llm_insufficient(llm_response: str) -> bool:
+    return "I don't know" in llm_response or llm_response.strip() == ''
+
+def summarize_search_results(results):
+    summary = ""
+    for result in results:
+        summary += f"- {result['title']} ({result['link']})\n"
+    return summary
+
+def generate_llm_response(user_query: str) -> str:
+    inputs = tokenizer(user_query, return_tensors="pt")
+    outputs = model.generate(**inputs, max_length=150)
+    return tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+# Streamlit user interface
+st.title("Real-Time Factual Information Fetcher")
+user_query = st.text_input("Enter your query:")
+
+if st.button("Submit"):
+    if user_query:
+        llm_response = generate_llm_response(user_query)
+
+        if is_llm_insufficient(llm_response) or 'recent' in user_query.lower():
+            search_results = search_api_call(user_query)
+            if search_results:
+                search_summary = summarize_search_results(search_results)
+                combined_response = f"{llm_response}\n\nHere are some recent findings:\n{search_summary.strip()}"
+            else:
+                combined_response = llm_response  # Fallback to the original LLM response
+        else:
+            combined_response = llm_response
+        
+        st.markdown("### Response:")
+        st.markdown(combined_response)
+
+    else:
+        st.warning("Please enter a query.")
+
+if __name__ == "__main__":
+    import streamlit as st