HF_inference.py

@@ -72,18 +72,22 @@ RETRY_INTERVAL = 1  # in seconds
 @st.cache_resource(experimental_allow_widgets=True, show_spinner=False)
 def query(payload, selected_model):
     # st.write(selected_model)
+    
     API_URL = MODEL_URLS.get(selected_model, MODEL_URLS[selected_model])  # Get API URL based on selected model
 
     for retry in range(MAX_RETRIES):
+        
         response = requests.post(API_URL, headers=headers, json=payload)
         if response.status_code == 200:
             return response.json()
         else:
+            st.info("loadings")
             time.sleep(RETRY_INTERVAL)
 
     return None
 
 def analyze_sintement(text, selected_model):
+    # print(headers)
     output = query({"inputs": text}, selected_model)
     if output:
         return output[0][0]['label'], output[0][0]['score']

app5_selectbox/evaluation.py

@@ -168,7 +168,7 @@ def analyze_instructors(evaluation_df):
         try:
             with st.spinner("Analyzing... "):
                 # st.write(llm_chain.run(prompt))
-                st.write(g4f_prompt(results_to_prompt))     #############################
+                # st.write(g4f_prompt(results_to_prompt))     #############################
                 st.success("Analyzing Complete!")
                 break
             
@@ -381,7 +381,7 @@ def analyze_instructors(evaluation_df):
             while True:
                 with st.spinner("Generating Recommendation"):
                     try:
-                        st.write(g4f_prompt(prompt))    #############################
+                        # st.write(g4f_prompt(prompt))    #############################
                         # pass
                         # break
                         break

app5_selectbox/evaluation_analysis.py

@@ -97,10 +97,9 @@ def classify_sentiments(text_samples, model):
 
     # text = ["i love this", "nice one!", "happy!"]
     selected_model = model
+
     results = [analyze_sintement(t, selected_model) for t in text_samples]
     
-    
-    
     for idx, result in enumerate(results):
         # st.text(result[0])
         # predicted_class, probabilities = analyze_sintement(text_sample, model)
@@ -129,16 +128,17 @@ def calculate_average_scores(probability_list):
 
 def eval_analysis(instructor, instructor_comment, criteria_results, selected_model):
     if selected_model < 3:
-        model = model_list[selected_model]
+        ## local model
+        # model = model_list[selected_model]
         # model_tokenizer = model_tokenizer_list[selected_model]
-        model_tokenizer = model_list[selected_model]
-        loaded_model = AutoModelForSequenceClassification.from_pretrained(model)
-        tokenizer = AutoTokenizer.from_pretrained(model_tokenizer)
-
+        # model_tokenizer = model_list[selected_model]
+        # loaded_model = AutoModelForSequenceClassification.from_pretrained(model)
+        # tokenizer = AutoTokenizer.from_pretrained(model_tokenizer)
         clean_instructor_comment = clean_text(instructor_comment)
         
-        predicted_sentiments_transformer = classify_sentiments(clean_instructor_comment, tokenizer, loaded_model) # local model
-        # predicted_sentiments_transformer = classify_sentiments(clean_instructor_comment, models[selected_model])  # inference
+        # print(models[selected_model])
+        # predicted_sentiments_transformer = classify_sentiments(clean_instructor_comment, tokenizer, loaded_model) # local model
+        predicted_sentiments_transformer = classify_sentiments(clean_instructor_comment, models[selected_model])  # inference
         
         predicted_sentiments = predicted_sentiments_transformer[1]
         scores = predicted_sentiments_transformer[2]
@@ -316,7 +316,7 @@ def eval_analysis(instructor, instructor_comment, criteria_results, selected_mod
             while True:
                 try:
                     with st.spinner("Generating...."):
-                        if not llama2_g4f: st.write(g4f_prompt(prompt))        #################
+                        # if not llama2_g4f: st.write(g4f_prompt(prompt))        #################
                         # else: st.write(llama_prompt(prompt))        #################
                         st.success("Generation Complete!")
                         break
@@ -340,7 +340,7 @@ def eval_analysis(instructor, instructor_comment, criteria_results, selected_mod
             while True:
                 try:
                     with st.spinner("Generating...."):
-                        if not llama2_g4f: st.write(g4f_prompt(prompt))        #################
+                        # if not llama2_g4f: st.write(g4f_prompt(prompt))        #################
                         # else: st.write(llama_prompt(prompt))        #################
                         st.success("Generation Complete!")
                         break

app5_selectbox/evaluation_fac.py

@@ -391,7 +391,7 @@ def analyze_instructors(evaluation_df):
                 with st.spinner("Analyzing... "):
                     # st.write(llm_chain.run(prompt))
                     if enable_llm_analyze_sintement and sentiment_model:
-                        st.write(g4f_prompt(results_to_prompt))     #############################
+                        # st.write(g4f_prompt(results_to_prompt))     #############################
                         st.success("Analyzing Complete!")
                     break
                 
@@ -487,7 +487,7 @@ def analyze_instructors(evaluation_df):
             while True:
                 with st.spinner("Generating Recommendation"):
                     try:
-                        if enable_llm_analyze_sintement and sentiment_model: st.write(g4f_prompt(prompt))    #############################
+                        # if enable_llm_analyze_sintement and sentiment_model: st.write(g4f_prompt(prompt))    #############################
                         # pass
                         # break
                         break