Update app.py

app.py

@@ -23,7 +23,6 @@ import re
 nltk.download('punkt')
 nltk.download('stopwords')
 
-
 class VideoAnalytics:
     """
     Class for performing analytics on videos including transcription, summarization, topic generation,
@@ -425,8 +424,7 @@ class VideoAnalytics:
         return prompt+prompt1
 
 
-    def generate(self, task: str,temperature=0.9, max_new_tokens=5000, top_p=0.95,
-                 repetition_penalty=1.0) -> str:
+    def generate(self, question: str) -> str:
         """
         Generates text based on the prompt and transcribed text.
         Args:
@@ -439,31 +437,36 @@ class VideoAnalytics:
         Returns:
             str: Generated text.
         """
-        try:
-          temperature = float(temperature)
-          if temperature < 1e-2:
-              temperature = 1e-2
-          top_p = float(top_p)
-
-          generate_kwargs = dict(
-              temperature=temperature,
-              max_new_tokens=max_new_tokens,
-              top_p=top_p,
-              repetition_penalty=repetition_penalty,
-              do_sample=True,
-              seed=42,
-          )
-
-          # Generate text using the mistral client
-          stream = self.mistral_client.text_generation(task, **generate_kwargs, stream=True, details=True, return_full_text=False)
-          output = ""
-          # Concatenate generated text
-          for response in stream:
-              output += response.token.text
-          return output.replace("</s>","")
-        except Exception as e:
-            logging.error(f"Error in text generation: {e}")
-            return "An error occurred during text generation."
+        # try:
+        temperature=0.9 
+        max_new_tokens=5000
+        top_p=0.95
+        repetition_penalty=1.0  
+
+        temperature = float(temperature)
+        if temperature < 1e-2:
+            temperature = 1e-2
+        top_p = float(top_p)
+
+        generate_kwargs = dict(
+            temperature=temperature,
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            repetition_penalty=repetition_penalty,
+            do_sample=True,
+            seed=42,
+        )
+        prompt = self.format_prompt(question, self.english_text)
+        # Generate text using the mistral client
+        stream = self.mistral_client.text_generation(prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
+        output = ""
+        # Concatenate generated text
+        for response in stream:
+            output += response.token.text
+        return output.replace("</s>","")
+        # except Exception as e:
+        #     logging.error(f"Error in text generation: {e}")
+        #     return "An error occurred during text generation."
 
     def video_qa(self, question: str, model: str) -> str:
         """
@@ -474,26 +477,26 @@ class VideoAnalytics:
         Returns:
             str: Answer to the user's question.
         """
-        try:
-          if model == "OpenAI":
-            template = """you are the universal language expert .your task is  analyze the given  text and user ask any question about given text answer to the user question.otherwise reply i don't know.
-            english_text:{text}
-            user_question:{question}"""
-
-            prompt = PromptTemplate(template=template, input_variables=["text","question"])
-            llm_chain = LLMChain(prompt=prompt, verbose=True, llm=self.openai_llm)
-
-            # Run the language model chain
-            result = llm_chain.run({"text":self.english_text,"question":question})
+        # try:
+        if model == "OpenAI":
+          template = """you are the universal language expert .your task is  analyze the given  text and user ask any question about given text answer to the user question.otherwise reply i don't know.
+          english_text:{text}
+          user_question:{question}"""
+
+          prompt = PromptTemplate(template=template, input_variables=["text","question"])
+          llm_chain = LLMChain(prompt=prompt, verbose=True, llm=self.openai_llm)
+
+          # Run the language model chain
+          result = llm_chain.run({"text":self.english_text,"question":question})
+          return result
+
+        elif model == "Mixtral":
+            # Generate answer using Mixtral model
+            result = self.generate(question)
             return result
-
-          elif model == "Mixtral":
-              # Generate answer using Mixtral model
-              result = self.generate(question,self.english_text)
-              return result
-        except Exception as e:
-            logging.error(f"Error in video question answering: {e}")
-            return "An error occurred during video question answering."
+        # except Exception as e:
+        #     logging.error(f"Error in video question answering: {e}")
+        #     return "An error occurred during video question answering."
 
 
     def write_text_files(self, text: str, filename: str) -> None:
@@ -658,7 +661,7 @@ class VideoAnalytics:
                   result = gr.Textbox(label='Answer',lines=10)
               submit_btn.click(self.main,[video,yt_link,model_selection],[summary,Important_Sentences,Topics,summary_audio,important_sentence_audio,topics_audio])
               question.submit(self.video_qa,[question,model],result)
-        demo.launch()
+        demo.launch(debug=True)
 
 if __name__ == "__main__":
   video_analytics = VideoAnalytics()