Update app.py

app.py

@@ -1,68 +1,61 @@
-"""
-Simple Chatbot
-"""
-
 import streamlit as st
-import openai  # Ensure you have the correct import
+import requests
 import os
-from dotenv import load_dotenv
-
-load_dotenv()
-
-# Initialize the client
-openai.api_key = os.environ.get('HUGGINGFACEHUB_API_TOKEN')  # Replace with your token
-
-model_link = "mistralai/Mistral-7B-Instruct-v0.2"
-
-def reset_conversation():
-    """Resets Conversation"""
-    st.session_state.conversation = []
-    st.session_state.messages = []
-    return None
-
-# Set the temperature value directly in the code
-temperature = 0.5
-
-# Add a button to clear conversation
-if st.button('Reset Chat'):
-    reset_conversation()
-
-# Initialize chat history
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-st.title("Mistral-7B Chatbot")
-st.subheader("Ask me anything!")
-
-# Display chat messages from history on app rerun
-for message in st.session_state.messages:
-    with st.chat_message(message["role"]):
-        st.markdown(message["content"])
-
-# Accept user input
-prompt = st.chat_input("Type your message here...")
-
-if prompt:
-    # Display user message in chat message container
-    with st.chat_message("user"):
-        st.markdown(prompt)
-    # Add user message to chat history
-    st.session_state.messages.append({"role": "user", "content": prompt})
-
-    # Display assistant response in chat message container
-    with st.chat_message("assistant"):
-        try:
-            response = openai.Completion.create(
-                engine=model_link,
-                prompt=prompt,
-                max_tokens=3000,
-                temperature=temperature
-            )
-
-            response_content = response.choices[0].text.strip()
-            st.markdown(response_content)
-            st.session_state.messages.append({"role": "assistant", "content": response_content})
 
-        except Exception as e:
-            st.markdown("An error occurred. Please try again later.")
-            st.markdown(f"Error details: {e}")
+# Function to call the Together API with the provided model
+def call_ai_model(all_message):
+    url = "https://api.together.xyz/v1/chat/completions"
+    payload = {
+        "model": "NousResearch/Nous-Hermes-2-Yi-34B",
+        "temperature": 1.05,
+        "top_p": 0.9,
+        "top_k": 50,
+        "repetition_penalty": 1,
+        "n": 1,
+        "messages": [{"role": "user", "content": all_message}],
+        "stream_tokens": True,
+    }
+
+    TOGETHER_API_KEY = os.getenv('TOGETHER_API_KEY')
+    if TOGETHER_API_KEY is None:
+        raise ValueError("TOGETHER_API_KEY environment variable not set.")
+    
+    headers = {
+        "accept": "application/json",
+        "content-type": "application/json",
+        "Authorization": f"Bearer {TOGETHER_API_KEY}",
+    }
+
+    response = requests.post(url, json=payload, headers=headers, stream=True)
+    response.raise_for_status()  # Ensure HTTP request was successful
+
+    # Debugging line to print raw response
+    for line in response.iter_lines():
+        if line:
+            yield line.decode('utf-8')
+
+# Streamlit app layout
+st.title("Climate Change Impact on Sports Using AI")
+st.write("Predict and mitigate the impacts of climate change on sports performance and infrastructure.")
+
+# Input fields for user to enter data
+temperature = st.number_input("Enter temperature (°C):", min_value=-50, max_value=50, value=25)
+humidity = st.number_input("Enter humidity (%):", min_value=0, max_value=100, value=50)
+air_quality = st.number_input("Enter air quality index (AQI):", min_value=0, max_value=500, value=100)
+precipitation = st.number_input("Enter precipitation (mm):", min_value=0.0, max_value=500.0, value=10.0)
+
+if st.button("Generate Prediction"):
+    all_message = f"Predict the impact of the following climate conditions on sports performance and infrastructure: temperature {temperature}°C, humidity {humidity}%, air quality index {air_quality}, and precipitation {precipitation}mm."
+    
+    try:
+        with st.spinner("Generating response..."):
+            response_lines = call_ai_model(all_message)
+            generated_text = "".join(response_lines)
+            st.success("Response generated successfully!")
+            st.write(generated_text)
+    except ValueError as ve:
+        st.error(f"Configuration error: {ve}")
+    except requests.exceptions.RequestException as re:
+        st.error(f"Request error: {re}")
+    except Exception as e:
+        st.error(f"An unexpected error occurred: {e}")