Update app.py

app.py

@@ -1,54 +1,103 @@
-#pip install transformers 
 
 
-from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer, MistralForCausalLM
-from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model,AutoPeftModelForCausalLM
-import os,torch, platform, warnings
-from datasets import load_dataset
-from trl import SFTTrainer
-from huggingface_hub import notebook_login
-import fire
 import streamlit as st
+import requests
+
+# Function to call the Hugging Face model
+def query_huggingface_model(prompt):
+    API_TOKEN = "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"  # Replace with your Hugging Face API token
+    API_URL = "https://api-inference.huggingface.co/models/MariamAde/Mistral_finetuned_Base2"  # Replace with your model's API URL
+
+    headers = {"Authorization": f"Bearer {API_TOKEN}"}
+    response = requests.post(API_URL, headers=headers, json={"inputs": prompt})
+    
+    if response.status_code == 200:
+        return response.json()
+    else:
+        return {"error": response.text}
+
+# Streamlit interface
+def main():
+    st.title("My Fine-tuned Model Demo")
+
+    # User input
+    user_input = st.text_area("Enter your text here", "")
+
+    # Button to make the prediction
+    if st.button("Predict"):
+        with st.spinner("Predicting..."):
+            response = query_huggingface_model(user_input)
+            if "error" in response:
+                st.error(response["error"])
+            else:
+                st.success("Prediction Success")
+                st.write(response)  # Modify this based on how your model's response is structured
+
+if __name__ == "__main__":
+    main()
+
+
+
+
+
+
 
-#git clone https://huggingface.co/spaces/J4Lee/RadiantScriptor AutoModelForSequenceClassification
 
 
-st.set_page_config(page_title= "Reports generation from Radiological Image ")
 
-@st.cache(allow_output_mutation=True)
-def get_model():
-    #device = "cuda" # the device to load the model onto
-    model = AutoModelForCausalLM.from_pretrained("MariamAde/Mistral_finetuned_Base2")
-    tokenizer = AutoTokenizer.from_pretrained("MariamAde/Mistral_finetuned_Base2")
-    return tokenizer, model
+
+
+# #pip install transformers 
+
+
+# from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, HfArgumentParser,TrainingArguments,pipeline, logging, TextStreamer, MistralForCausalLM
+# from peft import LoraConfig, PeftModel, prepare_model_for_kbit_training, get_peft_model,AutoPeftModelForCausalLM
+# import os,torch, platform, warnings
+# from datasets import load_dataset
+# from trl import SFTTrainer
+# from huggingface_hub import notebook_login
+# import fire
+# import streamlit as st
+
+# #git clone https://huggingface.co/spaces/J4Lee/RadiantScriptor AutoModelForSequenceClassification
+
+
+# st.set_page_config(page_title= "Reports generation from Radiological Image ")
+
+# @st.cache(allow_output_mutation=True)
+# def get_model():
+#     #device = "cuda" # the device to load the model onto
+#     model = AutoModelForCausalLM.from_pretrained("MariamAde/Mistral_finetuned_Base2")
+#     tokenizer = AutoTokenizer.from_pretrained("MariamAde/Mistral_finetuned_Base2")
+#     return tokenizer, model
     
 
 
-tokenizer, model = get_model()
+# tokenizer, model = get_model()
 
-def generate_report(labels): #,model,tokenizer):
-    # Tokenize the input labels
-    inputs = tokenizer(labels, return_tensors="pt") #.to(device)
-    #model.to(device)
-    # Generate output using the model
-    output = model.generate(**inputs)
-    # Decode the output sentences
-    sentences = tokenizer.decode(output[0], skip_special_tokens=True)
-    return sentences
+# def generate_report(labels): #,model,tokenizer):
+#     # Tokenize the input labels
+#     inputs = tokenizer(labels, return_tensors="pt") #.to(device)
+#     #model.to(device)
+#     # Generate output using the model
+#     output = model.generate(**inputs)
+#     # Decode the output sentences
+#     sentences = tokenizer.decode(output[0], skip_special_tokens=True)
+#     return sentences
 
-# Streamlit interface
-st.title("Radiology Report Generator")
+# # Streamlit interface
+# st.title("Radiology Report Generator")
 
-# User input for finding labels
-labels = st.text_input("Enter Finding Labels:")
+# # User input for finding labels
+# labels = st.text_input("Enter Finding Labels:")
 
 
-if st.button("Generate Report"):
+# if st.button("Generate Report"):
 
-    # Generate the radiology report
-    report = generate_report(labels) #,model,tokenizer)
-    # Display the report
-    st.text_area("Generated Report:", value=report, height=300)
+#     # Generate the radiology report
+#     report = generate_report(labels) #,model,tokenizer)
+#     # Display the report
+#     st.text_area("Generated Report:", value=report, height=300)