Create app.py

app.py

@@ -1,31 +1,64 @@
-from fastapi import FastAPI
-from transformers import pipeline
-from transformers import pipeline
-import os
-## create a new FASTAPI app instance
-app=FastAPI()
-
-# Initialize the text generation pipeline
-
-#pipe = pipeline("text2text-generation", model="google/flan-t5-small")
-# Use a pipeline as a high-level helper
-import os
-#print(os.getenv('MODEL_REPO_ID'))
-from huggingface_hub import login
-
-pipe = pipeline("text-generation", model="mistralai/Mistral-7B-v0.1")
-
-@app.get("/")
-def home():
-    return {"message":"Hello World"}
-
-# Define a function to handle the GET request at `/generate`
-
-
-@app.get("/generate")
-def generate(text:str):
-    ## use the pipeline to generate text from given input text
-    output=pipe(text)
-
-    ## return the generate text in Json reposne
-    return {"output":output[0]['generated_text']}
+import streamlit as st
+from dotenv import load_dotenv
+
+load_dotenv()
+
+st.title("🤖 LLM Evaluation")
+pages = [
+        st.Page("GenerateModelTraces.py", title="Generate Traces"),
+        st.Page("LLMasjudge.py", title="LLM as a Judge"),
+        st.Page("evaluationwithexistingdata.py", title="Evaluate with existing data"),
+        st.Page("experiments.py", title="Run Experiment"),
+    ]
+
+pg = st.navigation(pages)
+pg.run()
+
+
+# action = st.radio("What would you like to do?", ["Generate Traces for Model", "Evaluate Model"])
+# models = helpers.fetch_models()
+# if models:
+#     if action == "Generate Traces for Model":
+#         st.subheader("Select a Model")
+#         if "selected_model" not in st.session_state:
+#             st.session_state.selected_model = models[0]
+#         st.selectbox(
+#             "Choose a model to use:", 
+#             models, key = 'selected_model',
+#             index=models.index(st.session_state.selected_model) if st.session_state.selected_model in models else 0
+#         )
+
+#         if st.session_state.selected_model:
+#             st.subheader("Enter a Prompt")
+#             if "prompt" not in st.session_state:
+#                 st.session_state.prompt = ""
+#             st.session_state.prompt = st.text_area("Enter your prompt:", value=st.session_state.prompt)
+#             if st.button("Generate Content", on_click=callback):
+#                 if st.session_state.prompt:
+#                     st.subheader("Model Output")
+#                     st.session_state.generated_content = helpers.generate_content(st.session_state.selected_model, st.session_state.prompt)
+#                     st.write(st.session_state.generated_content)
+#                     st.session_state.spans_df = phoenix_helpers.get_spans_df()
+#                     # print(spans_df)
+#                     st.dataframe(st.session_state.spans_df)
+#                 else:
+#                     st.write("Enter something to generate content.")
+#     elif action == "Evaluate Model":
+#         st.session_state.spans_df = phoenix_helpers.get_spans_df()
+#         st.subheader("Evaluate LLM")
+
+#         if "evaluation_result" not in st.session_state:
+#             st.session_state.evaluation_result = None
+#         if (st.button("Evaluate", on_click=callback2) or st.session_state.eval_btn_clicked):
+#             if "eval_model" not in st.session_state:
+#                 st.session_state.eval_model = models[0]
+#             st.selectbox(
+#                 "Choose a model to use for evaluation:", 
+#                 models, key = 'eval_model',
+#                 index=models.index(st.session_state.eval_model) if st.session_state.eval_model in models else 0,
+#             )
+#             if st.session_state.eval_model:
+#                 st.session_state.evaluation_result = phoenix_helpers.evaluate_model(st.session_state.spans_df, st.session_state.eval_model)
+#                 st.write(st.session_state.evaluation_result)
+
+